Posts Tagged Brain Computer Interface

[ARTICLE] Brain-Machine Neurofeedback: Robotics or Electrical Stimulation? – Full Text

Neurotechnology such as brain-machine interfaces (BMI) are currently being investigated as training devices for neurorehabilitation, when active movements are no longer possible. When the hand is paralyzed following a stroke for example, a robotic orthosis, functional electrical stimulation (FES) or their combination may provide movement assistance; i.e., the corresponding sensory and proprioceptive neurofeedback is given contingent to the movement intention or imagination, thereby closing the sensorimotor loop. Controlling these devices may be challenging or even frustrating. Direct comparisons between these two feedback modalities (robotics vs. FES) with regard to the workload they pose for the user are, however, missing. Twenty healthy subjects controlled a BMI by kinesthetic motor imagery of finger extension. Motor imagery-related sensorimotor desynchronization in the EEG beta frequency-band (17–21 Hz) was turned into passive opening of the contralateral hand by a robotic orthosis or FES in a randomized, cross-over block design. Mental demand, physical demand, temporal demand, performance, effort, and frustration level were captured with the NASA Task Load Index (NASA-TLX) questionnaire by comparing these workload components to each other (weights), evaluating them individually (ratings), and estimating the respective combinations (adjusted workload ratings). The findings were compared to the task-related aspects of active hand movement with EMG feedback. Furthermore, both feedback modalities were compared with regard to their BMI performance. Robotic and FES feedback had similar workloads when weighting and rating the different components. For both robotics and FES, mental demand was the most relevant component, and higher than during active movement with EMG feedback. The FES task led to significantly more physical (p = 0.0368) and less temporal demand (p = 0.0403) than the robotic task in the adjusted workload ratings. Notably, the FES task showed a physical demand 2.67 times closer to the EMG task, but a mental demand 6.79 times closer to the robotic task. On average, significantly more onsets were reached during the robotic as compared to the FES task (17.22 onsets, SD = 3.02 vs. 16.46, SD = 2.94 out of 20 opportunities; p = 0.016), even though there were no significant differences between the BMI classification accuracies of the conditions (p = 0.806; CI = −0.027 to −0.034). These findings may inform the design of neurorehabilitation interfaces toward human-centered hardware for a more natural bidirectional interaction and acceptance by the user.


About half of all severely affected stroke survivors remain with persistent motor deficits in the chronic disease stage despite therapeutic interventions on the basis of the current standard of care (Winters et al., 2015). Since these patients cannot use the affected hand for activities of daily living, novel interventions investigate different neurotechnological devices to facilitate upper limb motor rehabilitation, such as brain-machine interfaces (BMI), robotic orthoses, neuromuscular functional electrical stimulation (FES), and brain stimulation (Coscia et al., 2019). BMI approaches, for example, aim at closing the impaired sensorimotor loop in severe chronic stroke patients. They use robotic orthoses (Ang et al., 2015Kasashima-Shindo et al., 2015Belardinelli et al., 2017), FES devices (Kim et al., 2016Biasiucci et al., 2018), and their combination (Grimm et al., 2016cResquín et al., 2017) to provide natural sensory and proprioceptive neurofeedback during movement intention or imagery. It is hypothesized that this approach will lead to reorganization of the corticospinal network through repetitive practice, and might ultimately restore the lost motor function (Naros and Gharabaghi, 20152017Belardinelli et al., 2017Guggenberger et al., 2018).

However, these novel approaches often result in no relevant clinical improvements in severe chronic stroke patients yet (Coscia et al., 2019). Therefore, recent research has taken a refined and rather mechanistic approach, e.g., by targeting physiologically grounded and clinically relevant biomarkers with BMI neurofeedback; this has led to the conceptional differentiation between restorative therapeutic BMIs on the one side (as those applied in this study) and classical assistive BMIs on the other side like those applied to control devices such as wheel-chairs (Gharabaghi, 2016): While assistive BMIs intend to maximize the decoding accuracy, restorative BMIs want to enhance behaviorally relevant biomarkers. Specifically, brain oscillations in the beta frequency band have been suggested as potential candidate markers and therapeutic targets for technology-assisted stroke rehabilitation with restorative BMIs (Naros and Gharabaghi, 20152017Belardinelli et al., 2017), since they are known to enhance signal propagation in the motor system and to determine the input-output ratio of corticospinal excitability in a frequency- and phase-specific way (Raco et al., 2016Khademi et al., 20182019Naros et al., 2019).

However, these restorative BMI devices differ from their predecessors, i.e., assistive BMIs, by an intentionally regularized and restricted feature space, e.g., by using the beta frequency band as a feedback signal for BMI control (Gharabaghi, 2016Bauer and Gharabaghi, 2017). Such a more specific approach is inherently different from previous more flexible algorithms that select and weight brain signal features to maximize the decoding accuracy of the applied technology; restorative BMIs like the those applied in this study have, therefore, relevantly less classification accuracy than classical assistive BMIs (Vidaurre et al., 2011Bryan et al., 2013). As the regularized and restricted feature space of such restorative BMI devices leads to a lower classification accuracy in comparison to more flexible approaches, it may be frustrating even for healthy participants (Fels et al., 2015). IN the context of the present study, we conjectured that such challenging tasks will increase the relevance of extraneous load aspects like the workload (Schnotz and Kürschner, 2007). Furthermore, the modulation range of the oscillatory beta frequency band is compromised in stroke patients, proportionally to their motor impairment level (Rossiter et al., 2014Shiner et al., 2015). That means that more severely affected patients show less oscillatory event-related desynchronization (ERD) and synchronization (ERS) during motor execution or imagery (Pfurtscheller and Lopes da Silva, 1999). To our understanding, this underlines the relevance of beta oscillations as a therapeutic target for post-stroke rehabilitation. At the same time, however, this poses a major challenge for the affected patients and may, thereby, compromise their therapeutic benefit (Gomez-Rodriguez et al., 2011a,bBrauchle et al., 2015).

To overcome these hurdles that are inherent to restorative BMI devices, we have investigated different approaches in the past: (i) Reducing the brain signal attenuation by the skull via the application of epidural interfaces (Gharabaghi et al., 2014b,cSpüler et al., 2014), (ii) Augmenting the afferent feedback of the robotic orthosis by providing concurrent virtual reality input (Grimm et al., 2016a,b), (iii) combining the orthosis-assisted movements with neuromuscular (Grimm and Gharabaghi, 2016Grimm et al., 2016c) or transcranial electrical stimulation (Naros et al., 2016a) to enhance the cortical modulation range (Reynolds et al., 2015), and (iv) optimizing the mental workload related to the use of BMI devices.

In this study, we focus on the latter approach, i.e., optimizing the mental workload related to the use of BMI devices. For the latter approach it would be necessary to better understand the workloads related to different technologies applied in the context of BMI feedback (robotics vs. FES). We, therefore, investigated the mental demand, physical demand, temporal demand, performance, effort, and frustration of healthy subjects controlling a BMI by motor imagery of finger extension. Motor imagery-related sensorimotor desynchronization in the beta frequency-band was turned into passive opening of the contralateral hand by a robotic exoskeleton or FES in a randomized, cross-over block design. The respective workloads were compared to the task-related aspects of active hand movement with EMG feedback. We conjectured a feedback-specific workload profile that would be informative for more personalized future BMI approaches.



We recruited 20 healthy subjects (age = 23.5 ± 1.08 yeas [mean ± SD], range 19–27, 15 female) for this study. Subjects were not naive to the tasks. All were right-handed and reached a score equal or above 60 in the Edinburgh Handedness Inventory (Oldfield, 1971). The subjects gave their written informed consent before participation and the study protocol was approved by the Ethics Committee of the Medical Faculty of the University of Tübingen. They received monetary compensation.

Subject Preparation

We used Ag/AgCl electrodes in a 32 channel setup according to the international 10-20 system (Fp1, Fp2, F3, Fz, F4, FC5, FC3, FC1, FCz, FC2, FC4, FC6, C5, C3, C1, Cz, C2, C4, C6, TP9, CP5, CP3, CP1, CPz, CP2, CP4, CP6, P3, Pz, P4, O1, O2 with TP10 as Reference and AFz as Ground) to examine the cortical activation pattern during the training session. Electrode impedances were set below 10 kΩ. All signals are digitalized at a sampling frequency of 1,000 Hz (robotic block) or 5,000 Hz (FES block) using Brain Products Amplifiers and transmitted online to BCI2000 software. BCI2000 controlled in combination with a custom-made software the respective feedback device, i.e., either the robotic orthosis or the functional electrical stimulation. Depending on the task, one of the following preparations was performed. Either the robotic hand orthosis (Amadeo, Tyromotion) was attached to the subject’s left hand (Figure 1A), fixated with Velcro strips across the forearm and with magnetic pads on the fingertips (Gharabaghi et al., 2014aNaros et al., 2016b); or functional electrical stimulation (FES, Figure 1B) was applied to the M. extensor digitorum communis (EDC) by the RehaMove2 (Hasomed GmbH, Magdeburg) with two self-adhering electrodes (50 mm, HAN-SEN Trading & Consulting GmbH, Hamburg). First an electrode was fixed to the distal end of the EDC’s muscle belly serving as ground. Then a rectangular electrode prepared with contact gel was used to find the optimal place for the second electrode where maximal extension of the left hand could be achieved. Here a custom written Matlab script was executed to detect the current threshold needed for the extension. Starting at 1 mA, the current was increased in steps of 0.5–1 mA. During each trial, FES was applied for 3 s with a pulse width of 1,000 μs and a frequency of 100 Hz. At the beginning of stimulation, a ramping protocol was implemented for 500 ms. Once, the correct position and threshold of stimulation were found, the temporary electrode was replaced by the second stimulation electrode and both were fixed with tape. A mean stimulation intensity of 6.5 mA (SD = 2.27) was required to cause the desired contraction in this study.

Figure 1. Experimental set-up. (Left) Robotic hand orthosis as feedback device (Amadeo, Tyromotion GmbH, Graz). (Middle) Neuromuscular forearm stimulation as feedback device (RehaMove 2, Hasomed GmbH, Magdeburg). In both cases, a brain-machine interface (BMI) detected motor imagery-related oscillations in the beta frequency band by an electroencephalogram (EEG) and provided via a BCI2000-system contingent feedback by moving the hand with either the robot or the electrical stimulation. (Right) The EEG montage used in this study.


Continue —-> Frontiers | Brain-Machine Neurofeedback: Robotics or Electrical Stimulation? | Bioengineering and Biotechnology

, , , , , , , ,

Leave a comment

[Review] Immediate and long-term effects of BCIbased rehabilitation of the upper extremity after stroke: a systematic review and metaanalysis – Full Text PDF


Background: A substantial number of clinical studies have demonstrated the functional recovery induced by the use of brain-computer interface (BCI) technology in patients after stroke. The objective of this review is to evaluate the effect sizes of clinical studies investigating the use of BCIs in restoring upper extremity function after stroke and
the potentiating effect of transcranial direct current stimulation (tDCS) on BCI training for motor recovery.

Methods: The databases (PubMed, Medline, EMBASE, CINAHL, CENTRAL, PsycINFO, and PEDro) were systematically searched for eligible single-group or clinical controlled studies regarding the effects of BCIs in hemiparetic upper extremity recovery after stroke. Single-group studies were qualitatively described, but only controlled-trial studies were included in the meta-analysis. The PEDro scale was used to assess the methodological quality of the controlled studies. A meta-analysis of upper extremity function was performed by pooling the standardized mean difference (SMD). Subgroup meta-analyses regarding the use of external devices in combination with the application of BCIs were also carried out. We summarized the neural mechanism of the use of BCIs on stroke.

Results: A total of 1015 records were screened. Eighteen single-group studies and 15 controlled studies were included. The studies showed that BCIs seem to be safe for patients with stroke. The single-group studies consistently showed a
trend that suggested BCIs were effective in improving upper extremity function. The meta-analysis (of 12 studies) showed a medium effect size favoring BCIs for improving upper extremity function after intervention (SMD = 0.42; 95% CI = 0.18–0.66; I2 = 48%; P < 0.001; fixed-effects model), while the long-term effect (five studies) was not significant (SMD = 0.12; 95% CI = − 0.28 – 0.52; I2 = 0%; P = 0.540; fixed-effects model). A subgroup meta-analysis indicated that using functional electrical stimulation as the external device in BCI training was more effective than using other devices (P = 0.010). Using movement attempts as the trigger task in BCI training appears to be more effective than using motor
imagery (P = 0.070). The use of tDCS (two studies) could not further facilitate the effects of BCI training to restore upper extremity motor function (SMD = − 0.30; 95% CI = − 0.96 – 0.36; I2 = 0%; P = 0.370; fixed-effects model).

Conclusion: The use of BCIs has significant immediate effects on the improvement of hemiparetic upper extremity function in patients after stroke, but the limited number of studies does not support its long-term effects. BCIs combined with functional electrical stimulation may be a better combination for functional recovery than other kinds
of neural feedback. The mechanism for functional recovery may be attributed to the activation of the ipsilesional premotor and sensorimotor cortical network.

Full Text PDF


, , , , , , , , , , , ,

Leave a comment

[Book Chapter] A Sensorimotor Rhythm-Based Brain–Computer Interface Controlled Functional Electrical Stimulation for Handgrasp Rehabilitation. (Abstract + References)


Each year, 795,000 stroke patients suffer a new or recurrent stroke and 235,000 severe traumatic brain injuries (TBIs) occur in the US. These patients are susceptible to a combination of significant motor, sensory, and cognitive deficits, and it becomes difficult or impossible for them to perform activities of daily living due to residual functional impairments. Recently, sensorimotor rhythm (SMR)-based brain–computer interface (BCI)-controlled functional electrical stimulation (FES) has been studied for restoration and rehabilitation of motor deficits. To provide future neuroergonomists with the limitations of current BCI-controlled FES research, this chapter presents the state-of-the-art SMR-based BCI-controlled FES technologies, such as current motor imagery (MI) training procedures and guidelines, an EEG-channel montage used to decode MI features, and brain features evoked by MI.


  1. Ang, K. K., Chin, Z. Y., Zhang, H., & Guan, C. (2008). Filter Bank Common Spatial Pattern (FBCSP) in brain-computer interface. In IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2390–2397.Google Scholar
  2. Ang, K. K., Guan, C., Ang, Kai Keng, & Guan, Cuntai. (2015). Brain-computer interface for neurorehabilitation of upper limb after stroke. Proceedings of the IEEE, 103(6), 944–953.CrossRefGoogle Scholar
  3. Bashashati, A., Fatourechi, M., Ward, R. K., & Birch, G. E. (2007). A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. Journal of Neural Engineering, 4(2), R32–R57.CrossRefGoogle Scholar
  4. Berrar, D., Bradbury, I., & Dubitzky, W. (2006). Avoiding model selection bias in small-sample genomic datasets. Bioinformatics22(10), 1245–1250. Oxford Univ Press.Google Scholar
  5. Blanchard, G., & Blankertz, B. (2004). BCI competition 2003—Data set IIa: Spatial patterns of self-controlled brain rhythm modulations. IEEE Transactions on Biomedical Engineering, 51(6), 1062–1066.CrossRefGoogle Scholar
  6. Choi, I., Bond, K., Krusienski, D., & Nam, C. S. (2015). Comparison of stimulation patterns to elicit steady-state somatosensory evoked potentials (SSSEPs): Implications for hybrid and SSSEP-based BCIs. Systems, Man, and Cybernetics (SMC)2015 IEEE International Conference on (pp. 3122–3127).Google Scholar
  7. Choi, I., Bond, K., & Nam, C. S. (2016). A hybrid BCI-controlled FES system for hand-wrist motor function. IEEE International Conference on Systems, Man, and Cybernetics.Google Scholar
  8. Daly, J. J., Cheng, R., Rogers, J., Litinas, K., Hrovat, K., & Dohring, M. (2009). Feasibility of a new application of noninvasive brain computer interface (BCI): A case study of training for recovery of volitional motor control after stroke. Journal of Neurologic Physical Therapy33(4), 203–211.Google Scholar
  9. Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21.CrossRefGoogle Scholar
  10. Delorme, A., Makeig, S., & Sejnowski, T. (2001). Automatic artifact rejection for EEG data using high-order statistics and independent component analysis. Proceedings of the third international ICA conference (pp. 9–12).Google Scholar
  11. Doucet, B. M., Lam, A., & Griffin, L. (2012). Neuromuscular electrical stimulation for skeletal muscle function. Yale J Biol Med, 85(2), 201–215.Google Scholar
  12. Elnady, A. M., Zhang, X., Xiao, Z. G., Yong, X., Randhawa, B. K., Boyd, L., & Menon, C. (2015). A single-session preliminary evaluat on of an affordable BCI-controlled arm exoskeleton and motor-proprioception platform. Frontiers in Human Neuroscience9, 168. Switzerland.Google Scholar
  13. Ferree, T. C., Clay, M. T., & Tucker, D. M. (2001). The spatial resolution of scalp EEG. Neurocomputing, 38–40, 1209–1216.CrossRefGoogle Scholar
  14. Forrester, B. J., & Petrofsky, J. S. (2004). Effect of electrode size, shape, and placement during electrical stimulation. Journal of Applied Research, 4(2), 346–354.Google Scholar
  15. Gordon, C. C., Churchill, T., Clauser, C. E., Bradtmiller, B., & McConville, J. T. (1989). Anthropometric survey of US army personnel: methods and summary statistics 1988.Google Scholar
  16. Gu, Y., Dremstrup, K., & Farina, D. (2009). Single-trial discrimination of type and speed of wrist movements from EEG recordings. Clinical Neurophysiology120(8), 1596–1600. International Federation of Clinical Neurophysiology.Google Scholar
  17. Hamedi, M., Salleh, S.-H., & Noor, A. M. (2016). Electroencephalographic motor imagery brain connectivity analysis for BCI: A review. Neural Computation, 28(6), 999–1041.MathSciNetCrossRefGoogle Scholar
  18. Hyvärinen, a, & Oja, E. (2000). Independent component analysis: algorithms and applications. Neural networks : the official journal of the International Neural Network Society13(4–5), 411–430.Google Scholar
  19. Kayser, J., & Tenke, C. E. (2003). Optimizing PCA methodology for ERP component identification and measurement: Theoretical rationale and empirical evaluation. Clinical Neurophysiology, 114(12), 2307–2325.CrossRefGoogle Scholar
  20. Kim, T., Kim, S., & Lee, B. (2016). Effects of action observational training plus brain-computer interface-based functional electrical stimulation on paretic arm motor recovery in patient with stroke: A randomized controlled trial. Occupational therapy international23(1), 39–47. England.Google Scholar
  21. Lawrence, M. (2009). Transcutaneous electrode technology for neuroprostheses, (18213).Google Scholar
  22. Lee, H., & Choi, S. (2003). PCA + HMM + SVM for EEG pattern classification. Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings.1(2), 1–4.Google Scholar
  23. Liu, Y., Li, M., Zhang, H., Wang, H., Li, J., Jia, J., Wu, Y., et al. (2014). A tensor-based scheme for stroke patients’ motor imagery EEG analysis in BCI-FES rehabilitation training. Journal of neuroscience methods222, 238–249. Elsevier.Google Scholar
  24. Looned, R., Webb, J., Xiao, Z. G., & Menon, C. (2014). Assisting drinking with an affordable BCI-controlled wearable robot and electrical stimulation: a preliminary investigation. Journal of neuroengineering and rehabilitation11, 51. England.Google Scholar
  25. Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007). A review of classification algorithms for EEG-based brain-computer interfaces. Journal of Neural Engineering, 4(2), R1–R13.CrossRefGoogle Scholar
  26. Lyons, G. M., Leane, G. E., Clarke-Moloney, M., O’Brien, J. V., & Grace, P. A. (2004). An investigation of the effect of electrode size and electrode location on comfort during stimulation of the gastrocnemius muscle. Medical Engineering & Physics, 26(10), 873–878.CrossRefGoogle Scholar
  27. McGie, S. C., Zariffa, J. J., Popovic, M. R., & Nagai, M. K. (2015). Short-term neuroplastic effects of brain-controlled and muscle-controlled electrical stimulation. Neuromodulation18(3), 233–240. United States.Google Scholar
  28. Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., et al. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4(1), 1.CrossRefGoogle Scholar
  29. Mukaino, M., Ono, T., Shindo, K., Fujiwara, T., Ota, T., Kimura, A., Liu, M., et al. (2014). Efficacy of brain-computer interface-driven neuromuscular electrical stimulation for chronic paresis after stroke. Journal of rehabilitation medicine46(4), 378–382. Sweden: Medical Journals Limited.Google Scholar
  30. Müller, G. R. R., Neuper, C., Rupp, R., Keinrath, C., Gerner, H. J. J., & Pfurtscheller, G. (2003). Event-related beta EEG changes during wrist movements induced by functional electrical stimulation of forearm muscles in man. Neuroscience Letters, 340(2), 143–147.CrossRefGoogle Scholar
  31. Nam, C. S., Lee, J., Bahn, S., Li, Y., & Choi, I. (2014). Brain-computer interface supported collaborative work. Proceedings of 5th International Brain-Computer Interface Meeting.Google Scholar
  32. Nam, C. S., Moore, M., Choi, I., & Li, Y. (2015). Designing better, cost-effective brain-computer interfaces. Ergonomics in Design: The Quarterly of Human Factors Applications, 23(4), 13–19. SAGE.Google Scholar
  33. Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012). Brain computer interfaces, a review. Sensors.Google Scholar
  34. Noirhomme, Q., Lesenfants, D., Gomez, F., Soddu, A., Schrouff, J., Garraux, G., Luxen, A., et al. (2014). Biased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions. NeuroImage: Clinical4, 687–694.CrossRefGoogle Scholar
  35. Nolan, H., Whelan, R., & Reilly, R. B. (2010). FASTER: fully automated statistical thresholding for EEG artifact rejection. Journal of neuroscience methods192(1), 152–162. Elsevier.Google Scholar
  36. Novi, Q., Guan, C., Dat, T. H., & Xue, P. (2007). Sub-band common spatial pattern (SBCSP) for brain-computer interface. Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering, 204–207.Google Scholar
  37. Pfurtscheller, G., & Lopes, F. H. (1999). Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology, 110, 1842–1857.CrossRefGoogle Scholar
  38. Pfurtscheller, G., Müller-Putz, G. R., Pfurtscheller, J. J., Rupp, R. R., Muller-Putz, G. R., Pfurtscheller, J. J., Rupp, R. R., et al. (2005). EEG-based asynchronous BCI controls functional electrical stimulation in a tetraplegic patient. EURASIP Journal on Applied Signal Processing2005(19), 3152–3155. Hindawi, USA.Google Scholar
  39. Pfurtscheller, G., Müller, G. R., Pfurtscheller, J. J., Gerner, H. J. J., Rupp, R. R., Muller, G. R., Pfurtscheller, J. J., et al. (2003). “Thought”—control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neuroscience letters351(1), 33–36. Ireland.CrossRefGoogle Scholar
  40. Pfurtscheller, G., & Neuper, C. (2006). Future prospects of ERD/ERS in the context of brain—computer interface (BCI) developments. Progress in Brain Research, 159, 433–437.CrossRefGoogle Scholar
  41. Pfurtscheller, G., Solis-Escalante, T., Ortner, R., Linortner, P., & Muller-Putz, G. R. (2010). Self-paced operation of an SSVEP-based orthosis with and without an imagery-based “brain switch”: A feasibility study towards a hybrid BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(4), 409–414.CrossRefGoogle Scholar
  42. Polikar, R. (2006). Ensemble based systems in decision making. Circuits and Systems Magazine, IEEE, 6(3), 21–45.CrossRefGoogle Scholar
  43. Powers, J. C., Bieliaieva, K., Wu, S., & Nam, C. S. (2015). The human factors and ergonomics of P300-based brain-computer interfaces. Brain sciences5(3), 318–56. Switzerland.Google Scholar
  44. Reynolds, C., Osuagwu, B. A., & Vuckovic, A. (2015). Influence of motor imagination on cortical activation during functional electrical stimulation. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology126(7), 1360–1369. Netherlands.Google Scholar
  45. Rohm, M., Muller-Putz, G. R., Kreilinger, A., von Ascheberg, A., & Rupp, R. (2010). A hybrid-Brain Computer Interface for control of a reaching and grasping neuroprosthesis. Biomedizinische Technik55(suppl. 1). Fachverlag Schiele &amp; Schon GmbH, Germany.Google Scholar
  46. Rohm, M., Schneiders, M., Müller, C., Kreilinger, A., Kaiser, V., Müller-Putz, G. R., Rupp, R. R. R., et al. (2013). Hybrid brain-computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury. Artificial Intelligence in Medicine59(2), 133–142. Netherlands: Elsevier Science B.V., Netherlands.Google Scholar
  47. Roset, S. A., Gant, K., Prasad, A., & Sanchez, J. C. (2014). An adaptive brain actuated system for augmenting rehabilitation. Frontiers in neuroscience8, 415. Switzerland.Google Scholar
  48. Rosner, B. (2015). Fundamentals of biostatistics. Nelson Education.Google Scholar
  49. Schalk, G., & Mellinger, J. (2010). A practical guide to brain–computer interfacing with BCI2000: General-purpose software for brain-computer interface research, data acquisition, stimulus presentation, and brain monitoring. Springer Science & Business Media.Google Scholar
  50. Sun, S., Zhang, C., & Zhang, D. (2007). An experimental evaluation of ensemble methods for EEG signal classification. Pattern Recognition Letters, 28(15), 2157–2163.CrossRefGoogle Scholar
  51. Tan, H. G., Shee, C. Y., Kong, K. H., Guan, C., Ang, W. T., et al. (2011). EEG controlled neuromuscular electrical stimulation of the upper limb for stroke patients. Frontiers of Mechanical Engineering6(1), 71–81. SP Higher Education Press, Germany.Google Scholar
  52. Vuckovic, A., Wallace, L., & Allan, D. B. (2015). Hybrid brain-computer interface and functional electrical stimulation for sensorimotor training in participants with tetraplegia: a proof-of-concept study. Journal of neurologic physical therapy : JNPT39(1), 3–14. United States.Google Scholar
  53. Wang, D., Miao, D., & Blohm, G. (2012). Multi-class motor imagery EEG decoding for brain-computer interfaces. Frontiers in Neuroscience6(OCT), 1–13.Google Scholar
  54. Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767–791.CrossRefGoogle Scholar
  55. Young, B. M., Nigogosyan, Z., Walton, L. M., Remsik, A., Song, J., Nair, V. A., Tyler, M. E., et al. (2015). Dose-response relationships using brain-computer interface technology impact stroke rehabilitation. Frontiers in human neuroscience9, 361. Switzerland.Google Scholar
  56. Young, B. M., Nigogosyan, Z., Nair, V. A., Walton, L. M., Song, J., Tyler, M. E., Edwards, D. F., et al. (2014). Case report: post-stroke interventional BCI rehabilitation in an individual with preexisting sensorineural disability. Frontiers in neuroengineering7, 18. Switzerland.Google Scholar
  57. Zickler, C., Riccio, A., Leotta, F., Hillian-Tress, S., Halder, S., Holz, E., Staiger-Salzer, P., et al. (2011). A brain-computer interface as input channel for a standard assistive technology software. Clinical EEG and neuroscience42(4), 236–244.CrossRefGoogle Scholar

via A Sensorimotor Rhythm-Based Brain–Computer Interface Controlled Functional Electrical Stimulation for Handgrasp Rehabilitation | SpringerLink

, , , , ,

Leave a comment

[Abstract] Motor Imagery Based Brain-Computer Interface Control of Continuous Passive Motion for Wrist Extension Recovery in Chronic Stroke Patients


  • Twenty-one patients successfully recovered active wrist extension.
  • Motor imagery based BCI control of wrist CPM training was applied.
  • Typical spatial and spectrum patterns of ERD/ERS formed after training.


Motor recovery of wrist and fingers is still a great challenge for chronic stroke survivors. The present study aimed to verify the efficiency of motor imagery based brain-computer interface (BCI) control of continuous passive motion (CPM) in the recovery of wrist extension due to stroke. An observational study was conducted in 26 chronic stroke patients, aged 49.0 ± 15.4 years, with upper extremity motor impairment. All patients showed no wrist extension recovery. A 24-channel highresolution electroencephalogram (EEG) system was used to acquire cortical signal while they were imagining extension of the affected wrist. Then, 20 sessions of BCI-driven CPM training were carried out for 6 weeks. Primary outcome was the increase of active range of motion (ROM) of the affected wrist from the baseline to final evaluation. Improvement of modified Barthel Index, EEG classification and motor imagery pattern of wrist extension were recorded as secondary outcomes. Twenty-one patients finally passed the EEG screening and completed all the BCI-driven CPM trainings. From baseline to the final evaluation, the increase of active ROM of the affected wrists was (24.05 ± 14.46)˚. The increase of modified Barthel Index was 3.10 ± 4.02 points. But no statistical difference was detected between the baseline and final evaluations (P > 0.05). Both EEG classification and motor imagery pattern improved. The present study demonstrated beneficial outcomes of MI-based BCI control of CPM training in motor recovery of wrist extension using motor imagery signal of brain in chronic stroke patients.


Graphical abstract

via Motor Imagery Based Brain-Computer Interface Control of Continuous Passive Motion for Wrist Extension Recovery in Chronic Stroke Patients – ScienceDirect


, , , , , , , , , , , , , , ,

Leave a comment

[ARTICLE] The Integration of Brain-Computer Interface (BCI) as Control Module for Functional Electrical Stimulation (FES) Intervention in Post-Stroke Upper Extremity Rehabilitation – Full Text


One of the prevalent disabilities after stroke is the loss of upper extremity motor function, leading survivors to suffer from an increased dependency in their activities of daily living and a general decrease in their overall quality of life. Therefore, the restoration of upper extremity function to improve survivors’ independency is crucial. Conventional stroke rehabilitation interventions, while effective, fall short of helping individuals achieve maximum recovery potential. Functional Electrical Stimulation (FES), both with passive and active approaches, has been found to moderately increase function in the affected limbs. This paper discusses a novel EEG-Based BCI-FES system that provides FES stimulation to the affected limbs based on the brain activity patterns of the patient specifically in the sensory motor cortex, while the patient imagines moving the affected limb. This system allows the synchronization of brain activity with peripheral movements, which may lead to brain reorganization and restoration of motor function by affecting motor learning or re-learning, and therefore induce brain plasticity to restore normal-like brain function.


Stroke is one of the leading causes of severe motor disability, with approximately 800,000 individuals each year are experiencing a new or recurrent stroke in the US alone (1). Advances in healthcare and medical technology, and the high incidence of stroke and its increasing rate in the growing elderly population, have contributed to a relatively large population of stroke survivors currently estimated at 4 million individuals in the United States alone (1). These survivors are left with several devastating long-term neurological impairments.

The most apparent defect after a stroke is motor impairments, with impairment of upper extremity (UE) functions standing as the most disabling motor deficit. Approximately 80% of survivors suffering from UE paresis, and only about one-tenth of the them regain complete functional recovery (2). Stroke survivors generally suffer from a decrease in their quality of life, and an increase dependency in their activities of daily living. Statistically, close to one quarter of the stroke survivors become dependent in activities of daily living (3). Thus, the optimal restoration of arm and hand function is crucial to improve their independence.

Recently, several remarkable advancements in the medical management of stroke have been made. However, a widely applicable or effective medical treatment is still missing, and most post-stroke care will continue to depend on rehabilitation interventions (4). The available UE stroke rehabilitation interventions can be categorized as: conventional physical and occupational therapy, constraint-induced movement therapy (CIT), functional electrical stimulation (FES), and robotic-aided and sensor-based therapy systems (5). Although an increased effort has been made to enhance the recovery process following a stroke, survivors generally do not reach their full recovery potential. Thus, the growing population of stroke survivors is in a vital need for innovative strategies in stroke rehabilitation, especially in the domain of UE motor rehabilitation. This paper presents an innovative integration of a brain-computer interface (BCI) system to actively control the delivery of FES. Early research and product development activities are advancing the reality of this becoming a mainstream intervention option.


The use of FES on the impaired arm is an accepted intervention for stroke rehabilitation aiming to improve motor function. A systematic review with meta-analysis of 18 randomized control trials found that FES had a moderate effect on activity compared with no intervention or placebo and a large effect on UE activity compared to control groups, suggesting that FES should be used in stroke rehabilitation to improve the ability to perform activities (6). Specifically, improvements in UE motor function after intensive FES intervention can be ascribed to the increased ability to voluntarily contract impaired muscles, the reduction in spasticity and improved muscle tone in the stimulated muscles, and the increased range of motion in all joints (7). These improvements in UE after FES could be attributable to multiple neural mechanisms, with one mechanism suggesting that proprioceptive sensory input and visual perception of the movement could promote neural reorganization and motor learning (8). A potential limiting factor to the application of FES is that the stimulation is administered manually, usually from a therapist, without any regard to the concurrent brain activity of the patient. This makes the delivery a passive process with no to minimal coordination with the mental task required to happen concurrently from the patient.

On the other hand, electromyography (EMG)-triggered FES systems made the delivery of FES an active process. Such systems are activated through detecting a preset electrical threshold in certain muscles, which provide the user (patient) the ability to actively control the delivery of FES and make the delivery concurrent with the patient’s brain activity. However, a systematic review of 8 randomized controlled trials (n=157) that assessed the effects of EMG-triggered neuromuscular electrical stimulation for improving hand function in stroke patients found no statistically significant differences in effects when compared to patients receiving usual care (9). A possibility to explain the shortcoming of EMG-triggered FES systems, is that the ability of the brain to generate and send efficient neural signals to the peripheral nervous system is disrupted after stroke, which could affect the control mechanism of these systems. Thus, the synchronization of FES with brain activity maybe critical for the optimization of recovery.


BCI technology can be used to actively control the FES application through detecting the brain neural activity directly when imagining or attempting a movement. Performing or mentally imagining (or as it commonly called motor imagery) a movement results in the generation of neurophysiological phenomena called event-related desynchronization or synchronization (ERD or ERS). ERD or ERS can be observed from Mu (9–13 Hz) or Beta rhythms (22–29 Hz) over the primary sensorimotor area contralateral to the imagined part of the body (10). These rhythms can be detected using electroencephalography (EEG). Therefore, an EEG based BCI system can be utilized to provide automated FES neurofeedback through detecting either actual movement or motor imagery (MI) and can be used to train the voluntary modulation of these rhythms. The ability to modulate these rhythms alongside the real-time neurofeedback from the FES application may induce neuroplastic change in a disrupted motor system to allow for more normal motor-related brain activity, and thus promote functional recovery. Figure 1 provides an overview of the BCI-FES system.

Any BCI-FES intervention session includes two screening tasks: an open-loop screening followed by a closed-loop task. The open-loop screening task is used to identify appropriate EEG-based control features to guide all subsequent closed-loop tasks. In the open-loop screening task, subjects are instructed to perform attempted movement of either hand by following on-screen cues of “right”, “left”, and “rest”. The attempted movement can vary across subjects, depending on the subject’s baseline abilities and recovery goals. For example, subjects can perform opening and closing of the hand or wrist flexion/extension movements. During this screening task, no feedback is provided to the subject.

figure 2 shows a screenshot of the closed-loop task interface, with a ball at the center and a target to the right, in order to provide a cue for the user to move his/her right hand.

Figure 2. Screenshot of Closed-loop Task

Data from the open-loop screening task will then be analyzed to identify appropriate EEG-based control features by determine the EEG channels the presents the largest r-squared values within the frequency ranges of the Mu and Beta rhythms for each attempted movement using left or right hand (11). The identified channels and the specific frequency bins will then be used to control the signals for the closed-loop neurofeedback task.

In the closed-loop screening task, a real-time visual feedback is given to the subject in a form of a game. A ball appears on the center of a computer monitor with a vertical rectangle (target) to either the right or left side of the screen (Figure 2). The movement of the ball is controlled by the BCI system in which the detection of an attempted movement in either hand will be translated into moving the ball toward the same side. For example, if the target appeared on the left side of the screen and the BCI system detected a movement attempt of the user’s left hand, the ball then moves toward the left. Users are instructed to perform or attempt the same movement that they used during the open-loop task. The FES electrodes are placed on the subject’s affected side over a specific muscle of the forearm. The selection of which muscle to be innervated with FES is dependent on the rehabilitation goal for the subject. For example, if a subject is having a difficulty extending his/her wrist, the FES electrodes are placed over the extensor muscles of the impaired forearm.

The FES neurofeedback is triggered when cortical activity related to attempted movement of the impaired limb is detected by the BCI system, and the subject is cued to attempt movement of the impaired hand. Thus, since both ball movement and FES are controlled by the same set of EEG signals, FES is only applied when the ball moves correctly toward the target on the affected side of the body. This triggering of the FES ensures that only consistent, desired patterns of brain activity associated with attempted movement of the impaired hand are rewarded with feedback from the FES device.


The growing population of stroke survivors constitutes an increasing need for new strategies in stroke rehabilitation. Thus, it is imperative to explore novel intervention technologies that present promise to aid in the recovery process of this population. Some studies suggest that noninvasive EEG-based BCI systems hold a potential for facilitating upper extremities motor recovery after stroke (12,13). Although several groups had gave up on the idea of using non-invasive EEG-based BCI systems for control, there might be several implementations of these systems in the context of rehabilitation that yet need to be explored. The active EEG-based BCI-FES system is one example. However, more research and clinical studies are needed to investigate the efficacy of the system in order to be accepted and integrated into regular stroke rehabilitation practice.


(1) Norrving B, Kissela B. The global burden of stroke and need for a continuum of care. Neurology 2013 Jan 15;80(3 Suppl 2):S5-12.

(2) Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: a systematic review. The Lancet Neurology 2009;8(8):741-754.

(3) Sanchez RJ, Liu J, Rao S, Shah P, Smith R, Rahman T, et al. Automating arm movement training following severe stroke: functional exercises with quantitative feedback in a gravity-reduced environment. IEEE Transactions on neural systems and rehabilitation engineering 2006;14(3):378-389.

(4) Langhorne P, Bernhardt J, Kwakkel G. Stroke rehabilitation. The Lancet 2011;377(9778):1693-1702.

(5) Loureiro RC, Harwin WS, Nagai K, Johnson M. Advances in upper limb stroke rehabilitation: a technology push. Med Biol Eng Comput 2011;49(10):1103.

(6) Howlett OA, Lannin NA, Ada L, McKinstry C. Functional electrical stimulation improves activity after stroke: a systematic review with meta-analysis. Arch Phys Med Rehabil 2015;96(5):934-943.

(7) Kawashima N, Popovic MR, Zivanovic V. Effect of intensive functional electrical stimulation therapy on upper-limb motor recovery after stroke: case study of a patient with chronic stroke. Physiotherapy Canada 2013;65(1):20-28.

(8) Wang R. Neuromodulation of effects of upper limb motor function and shoulder range of motion by functional electric stimulation (FES). Operative Neuromodulation: Springer; 2007. p. 381-385.

(9) Meilink A, Hemmen B, Seelen H, Kwakkel G. Impact of EMG-triggered neuromuscular stimulation of the wrist and finger extensors of the paretic hand after stroke: a systematic review of the literature. Clin Rehabil 2008;22(4):291-305.

(10) Ang KK, Guan C. EEG-Based Strategies to Detect Motor Imagery for Control and Rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2017;25(4):392-401.

(11) Wilson JA, Schalk G, Walton LM, Williams JC. Using an EEG-based brain-computer interface for virtual cursor movement with BCI2000. J Vis Exp 2009 Jul 29;(29). pii: 1319. doi(29):10.3791/1319.

(12) Caria A, Weber C, Brötz D, Ramos A, Ticini LF, Gharabaghi A, et al. Chronic stroke recovery after combined BCI training and physiotherapy: a case report. Psychophysiology 2011;48(4):578-582.

(13) Young BM, Nigogosyan Z, Remsik A, Walton LM, Song J, Nair VA, et al. Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device. Frontiers in neuroengineering 2014;7:25.


This project is supported in part by UW-Madison Institute for Clinical and Translational Research, and College of Health Sciences, UW-Milwaukee.


via The Integration of Brain-Computer Interface (BCI) as Control Module for Functional Electrical Stimulation (FES) Intervention in Post-Stroke Upper Extremity Rehabilitation

, , , , , , , ,

Leave a comment

[Abstract + References] A Wireless BCI-FES Based on Motor Intent for Lower Limb Rehabilitation


Recent investigations have proposed brain computer interfaces combined with functional electrical stimulation as a novel approach for upper limb motor recovery. These systems could detect motor intention movement as a power decrease of the sensorimotor rhythms in the electroencephalography signal, even in people with damaged brain cortex. However, these systems use a large number of electrodes and wired communication to be employed for gait rehabilitation. In this paper, the design and development of a wireless brain computer interface combined with functional electrical stimulation aimed at lower limb motor recovery is presented. The design requirements also account the dynamic of a rehabilitation therapy by allowing the therapist to adapt the system during the session. A preliminary evaluation of the system in a subject with right lower limb motor impairment due to multiple sclerosis was conducted and as a performance metric, the true positive rate was computed. The developed system evidenced a robust wireless communication and was able to detect lower limb motor intention. The mean of the performance metric was 75%. The results encouraged the possibility of testing the developed system in a gait rehabilitation clinical study.


  1. 1.
    Pfurtscheller, G., Mcfarland, D.: BCIs that use sensorimotor rhythms. In: Wolpaw, J.R., Wolpaw, E. (eds.) Brain-Computer Interfaces: Principles and Practice, pp. 227–240. Oxford University Press (2012)Google Scholar
  2. 2.
    Carrere, L.C., Tabernig, C.B.: Detection of foot motor imagery using the coefficient of determination for neurorehabilitation based on BCI technology. IFMBE Proc. 49, 944–947 (2015). Scholar
  3. 3.
    Sannelli, C., Vidaurre, C., Müller, K.R., Blankertz, B.: A large scale screening study with a SMR-based BCI: categorization of BCI users and differences in their SMR activity (2019)Google Scholar
  4. 4.
    Do, A.H., Wang, P.T., King, C.E., Schombs, A., Cramer, S.C., Nenadic, Z.: Brain-computer interface controlled functional electrical stimulation device for foot drop due to stroke, pp. 6414–6417 (2012)Google Scholar
  5. 5.
    Ramos-Murguialday, A., Broetz, D., Rea, M., Yilmaz, Ö., Brasil, F.L., Liberati, G., Marco, R., Garcia-cossio, E., Vyziotis, A., Cho, W., Cohen, L.G., Birbaumer, N.: Brain-Machine-interface in chronic stroke rehabilitation: a controlled study. Ann. Neurol. 74, 100–108 (2014). Scholar
  6. 6.
    Biasiucci, A., Leeb, R., Iturrate, I., Perdikis, S., Al-Khodairy, A., Corbet, T., Schnider, A., Schmidlin, T., Zhang, H., Bassolino, M., Viceic, D., Vuadens, P., Guggisberg, A.G., Millán, J.D.R.: Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat. Commun. 9, 1–13 (2018). Scholar
  7. 7.
    Tabernig, C.B., Lopez, C.A., Carrere, L.C., Spaich, E.G., Ballario, C.H.: Neurorehabilitation therapy of patients with severe stroke based on functional electrical stimulation commanded by a brain computer interface. J. Rehabil. Assist. Technol. Eng. 5, 205566831878928 (2018). Scholar
  8. 8.
    McCrimmon, C.M., King, C.E., Wang, P.T., Cramer, S.C., Nenadic, Z., Do, A.H.: Brain-controlled functional electrical stimulation therapy for gait rehabilitation after stroke: a safety study. J. Neuroeng. Rehabil. 12 (2015).
  9. 9.
    g.Nautilus wireless biosignal acquisition Homepage.
  10. 10.
    Emotiv EpocFlex flexible wireless EEG system Homepage.
  11. 11.
    Vuckovic, A., Wallace, L., Allan, D.: Hybrid brain-computer interface and functional electrical stimulation for sensorimotor training in participants with tetraplegia: a proof-of-concept study. J. Neurol. Phys. Ther. 39, 3–14 (2015)CrossRefGoogle Scholar
  12. 12.
    Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51, 1034–1043 (2004). Scholar
  13. 13.
    McCrimmon, C.M., Fu, J.L., Wang, M., Lopes, L.S., Wang, P.T., Karimi-Bidhendi, A., Liu, C.Y., Heydari, P., Nenadic, Z., Do, A.H.: Performance assessment of a custom, portable, and low-cost brain-computer interface platform. IEEE Trans. Biomed. Eng. 64, 2313–2320 (2017). Scholar

via A Wireless BCI-FES Based on Motor Intent for Lower Limb Rehabilitation | SpringerLink

, , , , , , ,

Leave a comment

[ARTICLE] Brain–computer interface and assist-as-needed model for upper limb robotic arm – Full Text

Post-stroke paralysis, whereby subjects loose voluntary control over muscle actuation, is one of the main causes of disability. Repetitive physical therapy can reinstate lost motions and strengths through neuroplasticity. However, manually delivered therapies are becoming ineffective due to scarcity of therapists, subjectivity in the treatment, and lack of patient motivation. Robot-assisted physical therapy is being researched these days to impart an evidence-based systematic treatment. Recently, intelligent controllers and brain–computer interface are proposed for rehabilitation robots to encourage patient participation which is the key to quick recovery. In the present work, a brain–computer interface and assist-as-needed training paradigm have been proposed for an upper limb rehabilitation robot. The brain–computer interface system is implemented with the use of electroencephalography sensor; moreover, backdrivability in the actuator has been achieved with the use of assist-as-needed control approach, which allows subjects to move the robot actively using their limited motions and strengths. The robot only assists for the remaining course of trajectory which subjects are unable to perform themselves. The robot intervention point is obtained from the patient’s intent which is captured through brain–computer interface. Problems encountered during the practical implementation of brain–computer interface and achievement of backdrivability in the actuator have been discussed and resolved.

The recovery of upper limb motions and strengths in patients with damaged neuromuscular system via robotic rehabilitation devices is a promising way of enhancing existing treatments and their efficacies. Various reasons may cause limb dysfunctions, including stroke, spinal cord injuries, or even ligament rupture. According to the World Health Organization, about 15 million people globally suffer from Cerebro-Vascular Accidents (CVAs) each year and up to 65% of these need limb recovery procedures.1 Only in the last 15 years, the number of CVA or stroke patients is increased by 40%, which is the result of a more intense pace of living, deterioration of ecology, and increased aging population.2 Considering these statistics, development of new and efficient ways of rehabilitation is just as important as implementation of improved prevention strategies.

For the last 20 years, robotics-based therapy was steadily paving its way for becoming an essential practice in rehabilitation medicine.3,4 According to the systematic review of Kwakkel et al.5 on the upper limb recovery using robot-aided therapy, repetitive, meaningful, labor-intensive treatment programs implemented with robotic devices provide positive impact for the restoration of functional abilities in human limbs. In medical terminology, a device that provides support, and aligns or improves the function of movable limbs is known as orthosis, and robotic devices intended to provide such treatment are called robotic orthoses.6 Particularly, two key directions gained major attention in the medical engineering research: robot-assisted therapy and functional electrical simulation (FES) therapy. The FES therapy describes a technique that stimulates weakened or paralyzed muscles on a human limb by applying electric charges externally. The goal of FES therapy is to reactivate the neural connections between a muscle and human’s sensorimotor system to enable patients’ ability to control their limbs without assistance.7 In the study by Popovic and others, the functional electrical therapy (FET) was applied with the use of surface electrodes and it was used to stimulate arm fingers of patients, this therapy has demonstrated positive therapeutic effects.8 It was revealed that daily 30-min therapy for 1-month period allowed improvement in movement range, speed, and increased strength in muscles. There are also side effects of FES-based treatment such as pain and irritation on the affected area, autonomic dysreflexia, increased spasticity, broken bones, and mild electric shocks from faulty equipment. However, the robot-assisted rehabilitation is non-invasive and free from above risks, and it is preferred for the rehabilitation of stroke survivors.

The important advantage of robotic devices is that they can reduce the burden on health care workers who traditionally had to conduct labor-intensive training sessions for patients. Equipped with sensors, intelligent controllers, and haptic and visual interfaces, robotic orthosis can have a potential to put the recovery process to a new level by collecting relevant data about various health parameters (pulse rate, body temperature, etc.) and adjusting the training modes accordingly. Besides the positive impacts of robot-based rehabilitation, the reliability of robot-based assistance is still questionable and adversely it may worsen the recovery progress made before, and that depends on the type of assistance control robot employs.9 Assist-as-needed (AAN) control type has become one of the prominent strategies recently which has been recommended positively from clinical trials.10 In order to stabilize the system, AAN-based approach has become subject to be researched by scientists. In the work done by Wolbrecht, AAN control is obtained from the adaptive control by incorporating novel force to address and decrease the system’s parametric errors.11 There are also other works which propose AAN type of control for their systems;1214 however, there are no works which have incorporated both BCI (brain–computer interface)- and AAN-based control approach into the system.

Owing to the recent advances in biosensors, especially in their robustness and signal processing, robot controllers equipped with bio-sensing are able to achieve intelligence with less complex algorithms. One of the most recent applications of BCI is in the domain of orthoses.1517 Newer instances of orthoses combine latest advances in control theory and brain activity. Berlin Technical University in cooperation with Korean University created an exoskeleton to maneuver lower limbs. A feature of this work is the use of non-invasive electroencephalography (EEG). The study involved 11 healthy men aged 25 to 32 years.18 First upper limb exoskeleton controlled by BCI was proposed by AA Frolov et al.19 Authors concluded that BCI inclusion improves the movements of the paretic hand in post-stroke patients irrespective of severity and localization of the disease. In addition, it was shown that duration of the training also increases effectiveness of rehabilitation.

Based on the letters on the screen, it was possible to determine native language of the patient in the work done by Vasileva.20 In this work, non-invasive EEG had been used. However, it was noted that non-invasive devices have less accuracy than professional medical EEG equipment. To improve signal detection, Agapov et al.21 have developed advanced algorithm of processing visually evoked potentials. To visualize stimuli, “eSpeller” software was developed.

Motivated by the above-mentioned successes and advances, in the present work, possible use of BCI is investigated in the rehabilitation robots for the treatment of stroke survivors. The aim of this work is to develop EEG-based mechatronic system that can receive electrical brain signals, detect emotions and gestures of the patient, and intelligently control robotic arm. In addition, to ensure smooth and compliant movement of the rehabilitation robot and improve treatment efficacy, AAN control paradigm is also considered. This research used EEG package and a controller to develop BCI system and realize AAN-based control. Developed system can help patients to control robot with their thoughts and enhance their participation in the rehabilitation process. Methodology of the current work is explained in the “Methodology” section, and in the subsequent sections, results are discussed before drawing conclusions from this research work.

EEG sensor

In order to register the brain activity, 16 EEG electrodes distributed around the patient’s head have been used. To provide more information which is related to motor imaginary signals, the frequency characteristics were extracted from the data by converting them from the time domain to the frequency domain. Furthermore, to distinguish between movement intentions and rest positions, bandpass filter in the range of 5 to 40 Hz was used.22,23 Since EEG data set recording can be very large, the powerful surface Laplacian technique was applied to lower the risk of influence from the neighboring neurons on the crucial cerebral cortex neurons.24 Finally, only dominant frequency of 13 to 30 Hz, also known as beta wave frequency, was featured according to Gropper et al.25 This band distinction was benchmarker as a sensible area of resting brain activity.

Abiding by the previous works associated with EEG signal processing in Iáñez et al.26 and Hortal et al.,27 the feature selection was reduced to the group of 29 features, which later were used for the further classification and predictive model construction.

After receiving data using an EEG, algorithm needs to determine the desired effect for the user. Input data for this algorithm are EEG signals recorded during the demonstration of stimuli. In most of the currently existing studies on this subject, the problem of classifying signals is divided into three large subtasks:

  • Preprocessing the signal (in order to remove noise components);
  • Formation of a feature space;
  • Classification of objects in the constructed feature space.

It should be noted that the greatest influence on the final quality of the classification is made by the extent to which the task of forming the feature space was successfully accomplished. The general scheme of operation of BCI is depicted in Figure 1.


Figure 1. Block diagram of BCI interface.



Continue —>  Brain–computer interface and assist-as-needed model for upper limb robotic arm – Akim Kapsalyamov, Shahid Hussain, Askhat Sharipov, Prashant Jamwal, 2019


Figure 4. (a) ELA actuated upper limb rehabilitation robot, (b) upper limb rehabilitation robot in use, and (c) robotic orthosis in use with EEG sensor.


, , , , , , , ,

Leave a comment

[ARTICLE] Efficacy and Brain Imaging Correlates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report – Full Text

To maximize brain plasticity after stroke, a plethora of rehabilitation strategies have been explored. These include the use of intensive motor training, motor-imagery (MI), and action-observation (AO). Growing evidence of the positive impact of virtual reality (VR) techniques on recovery following stroke has been shown. However, most VR tools are designed to exploit active movement, and hence patients with low level of motor control cannot fully benefit from them. Consequently, the idea of directly training the central nervous system has been promoted by utilizing MI with electroencephalography (EEG)-based brain-computer interfaces (BCIs). To date, detailed information on which VR strategies lead to successful functional recovery is still largely missing and very little is known on how to optimally integrate EEG-based BCIs and VR paradigms for stroke rehabilitation. The purpose of this study was to examine the efficacy of an EEG-based BCI-VR system using a MI paradigm for post-stroke upper limb rehabilitation on functional assessments, and related changes in MI ability and brain imaging. To achieve this, a 60 years old male chronic stroke patient was recruited. The patient underwent a 3-week intervention in a clinical environment, resulting in 10 BCI-VR training sessions. The patient was assessed before and after intervention, as well as on a one-month follow-up, in terms of clinical scales and brain imaging using functional MRI (fMRI). Consistent with prior research, we found important improvements in upper extremity scores (Fugl-Meyer) and identified increases in brain activation measured by fMRI that suggest neuroplastic changes in brain motor networks. This study expands on the current body of evidence, as more data are needed on the effect of this type of interventions not only on functional improvement but also on the effect of the intervention on plasticity through brain imaging.


Worldwide, stroke is a leading cause of adult long-term disability (Mozaffarian et al., 2015). From those who survive, an increased number is suffering with severe cognitive and motor impairments, resulting in loss of independence in their daily life such as self-care tasks and participation in social activities (Miller et al., 2010). Rehabilitation following stroke is a multidisciplinary approach to disability which focuses on recovery of independence. There is increasing evidence that chronic stoke patients maintain brain plasticity, meaning that there is still potential for additional recovery (Page et al., 2004). Traditional motor rehabilitation is applied through physical therapy and/or occupational therapy. Current approaches of motor rehabilitation include functional training, strengthening exercises, and range of movement exercises. In addition, techniques based on postural control, stages of motor learning, and movement patterns have been proposed such as in the Bobath concept and Bunnstrom approach (amongst others) (Bobath, 1990). After patients complete subacute rehabilitation programs, many still show significant upper limb motor impairment. This has important functional implications that ultimately reduce their quality of life. Therefore, alternative methods to maximize brain plasticity after stroke need to be developed.

So far, there is growing evidence that action observation (AO) (Celnik et al., 2008) and motor imagery (MI) improve motor function (Mizuguchi and Kanosue, 2017) but techniques based on this paradigm are not widespread in clinical settings. As motor recovery is a learning process, the potential of MI as a training paradigm relies on the availability of an efficient feedback system. To date, a number of studies have demonstrated the positive impact of virtual-reality (VR) based on neuroscientific grounds on recovery, with proven effectiveness in the stroke population (Bermúdez i Badia et al., 2016). However, patients with no active movement cannot benefit from current VR tools due to low range of motion, pain, fatigue, etc. (Trompetto et al., 2014). Consequently, the idea of directly training the central nervous system was promoted by establishing an alternative pathway between the user’s brain and a computer system.

This is possible by using electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), since they can provide an alternative non-muscular channel for communication and control to the external world (Wolpaw et al., 2002), while they could also provide a cost-effective solution for training (Vourvopoulos and Bermúdez, 2016b). In rehabilitation, BCIs could offer a unique tool for rehabilitation since they can stimulate neural networks through the activation of mirror neurons (Rizzolatti and Craighero, 2004) by means of action-observation (Kim et al., 2016), motor-intent and motor-imagery (Neuper et al., 2009), that could potentially lead to post-stroke motor recovery. Thus, BCIs could provide a backdoor to the activation of motor neural circuits that are not stimulated through traditional rehabilitation techniques.

In EEG-based BCI systems for motor rehabilitation, Alpha (8–12 Hz) and Beta (12–30 Hz) EEG rhythms are utilized since they are related to motor planning and execution (McFarland et al., 2000). During a motor attempt or motor imagery, the temporal pattern of the Alpha rhythms desynchronizes. This rhythm is also named Rolandic Mu-rhythm or the sensorimotor rhythm (SMR) because of its localization over the sensorimotor cortices. Mu-rhythms are considered indirect indications of functioning of the mirror neuron system and general sensorimotor activity (Kropotov, 2016). These are often detected together with Beta rhythm changes in the form of an event-related desynchronization (ERD) when a motor action is executed (Pfurtscheller and Lopes da Silva, 1999). These EEG patterns are primarily detected during task-based EEG (e.g., when the participant is actively moving or imagining movement) and they are of high importance in MI-BCIs for motor rehabilitation.

A meta-analysis of nine studies (combined N = 235, sample size variation 14 to 47) evaluated the clinical effectiveness of BCI-based rehabilitation of patients with post-stroke hemiparesis/hemiplegia and concluded that BCI technology could be effective compared to conventional treatment (Cervera et al., 2018). This included ischemic and hemorrhagic stroke in both subacute and chronic stages of stoke, between 2 to 8 weeks. Moreover, there is evidence that BCI-based rehabilitation promotes long-lasting improvements in motor function of chronic stroke patients with severe paresis (Ramos-Murguialday et al., 2019), while overall BCI’s are starting to prove their efficacy as rehabilitative technologies in patients with severe motor impairments (Chaudhary et al., 2016).

The feedback modalities used for BCI motor rehabilitation include: non-embodied simple two-dimensional tariffs on a screen (Prasad et al., 2010Mihara et al., 2013), embodied avatar representation of the patient on a screen or with augmented reality (Holper et al., 2010Pichiorri et al., 2015), neuromuscular electrical stimulation (NMES) (Kim et al., 2016Biasiucci et al., 2018). and robotic exoskeletal orthotic movement facilitation (Ramos-Murguialday et al., 2013Várkuti et al., 2013Ang et al., 2015). In addition, it has been shown that multimodal feedback lead to a significantly better performance in motor-imagery (Sollfrank et al., 2016) but also multimodal feedback combined with motor-priming, (Vourvopoulos and Bermúdez, 2016a). However, there is no evidence which modalities are more efficient in stroke rehabilitation are.

Taking into account all previous findings in the effects of multimodal feedback in MI training, the purpose of this case study is to examine the effect of the MI paradigm as a treatment for post-stroke upper limb motor dysfunction using the NeuRow BCI-VR system. This is achieved through the acquisition of clinical scales, dynamics of EEG during the BCI treatment, and brain activation as measured by functional MRI (fMRI). NeuRow is an immersive VR environment for MI-BCI training that uses an embodied avatar representation of the patient arms and haptic feedback. The combination of MI-BCIs with VR can reinforce activation of motor brain areas, by promoting the illusion of physical movement and the sense of embodiment in VR (Slater, 2017), and hence further engaging specific neural networks and mobilizing the desired neuroplastic changes. Virtual representation of body parts paves the way to include action observation during treatment. Moreover, haptic feedback is added since a combination of feedback modalities could prove to be more effective in terms of motor-learning (Sigrist et al., 2013). Therefore, the target of this system is to be used by patients with low or no levels of motor control. With this integrated BCI-VR approach, severe cases of stroke survivors may be admitted to a VR rehabilitation program, complementing traditional treatment.


Patient Profile

In this pilot study we recruited a 60 years old male patient with left hemiparesis following cerebral infarct in the right temporoparietal region 10 months before. The participant had corrected vision through eyewear, he had 4 years of schooling and his experience with computers was reported as low. Moreover, the patient was on a low dose of diazepam (5 mg at night to help sleep), dual antiplatelet therapy, anti-hypertensive drug and metformin. Hemiparesis was associated with reduced dexterity and fine motor function; however, sensitivity was not affected. Other sequelae of the stroke included hemiparetic gait and dysarthria. Moreover, a mild cognitive impairment was identified which did not interfere with his ability to perform the BCI-VR training. The patient had no other relevant comorbidities. Finally, the patient was undergoing physiotherapy and occupational therapy at the time of recruitment and had been treated with botulinum toxin infiltration 2 months before due to focal spasticity of the biceps brachii.

Intervention Protocol

The patient underwent a 3-weeks intervention with NeuRow, resulting in 10 BCI sessions of a 15 min of exposure in VR training per session. Clinical scales, motor imagery capability assessment, and functional -together with structural- MRI data had been gathered in three time-periods: (1) before (serving as baseline), (2) shortly after the intervention and (3) one-month after the intervention (to assess the presence of long-term changes). Finally, electroencephalographic (EEG) data had been gathered during all sessions, resulting in more than 20 datasets of brain electrical activity.

The experimental protocol was designed in collaboration with the local healthcare system of Madeira, Portugal (SESARAM) and approved by the scientific and ethic committees of the Central Hospital of Funchal. Finally, written informed consent was obtained from the participant upon recruitment for participating to the study but also for the publication of the case report in accordance with the 1964 Declaration of Helsinki.

Assessment Tools

A set of clinical scales were acquired including the following:

1. Montreal Cognitive Assessment (MoCA). MoCA is a cognitive screening tool, with a score range between 0 and 30 (a score greater than 26 is considered to be normal) validated also for the Portuguese population, (Nasreddine et al., 2005).

2. Modified Ashworth scale (MAS). MAS is a 6-point rating scale for measuring spasticity. The score range is 0, 1, 1+, 2, 3, and 4 (Ansari et al., 2008).

3. Fugl-Meyer Assessment (FMA). FMA is a stroke specific scale that assesses motor function, sensation, balance, joint range of motion and joint pain. The motor domain for the upper limb has a maximum score of 66 (Fugl-Meyer et al., 1975).

4. Stroke Impact Scale (SIS). SIS is a subjective scale of the perceived stroke impact and recovery as reported by the patient, validated for the Portuguese population. The score of each domain of the questionnaire ranges from 0 to 100 (Duncan et al., 1999).

5. Vividness of Movement Imagery Questionnaire (VMIQ2). VMIQ2 is an instrument that assess the capability of the participant to perform imagined movements from external perspective (EVI), internal perspective imagined movements (IVI) and finally, kinesthetic imagery (KI) (Roberts et al., 2008).

NeuRow BCI-VR System

EEG Acquisition

For EEG data acquisition, the Enobio 8 (Neuroelectrics, Barcelona, Spain) system was used. Enobio is a wearable wireless EEG sensor with 8 EEG channels for the recording and visualization of 24-bit EEG data at 500 Hz and a triaxial accelerometer. The spatial distribution of the electrodes followed the 10–20 system configuration (Klem et al., 1999) with the following electrodes over the somatosensory and motor areas: Frontal-Central (FC5, FC6), Central (C1, C2, C3, C4), and Central-Parietal (CP5, CP6) (Figure 1A). The EEG system was connected via Bluetooth to a dedicated desktop computer, responsible for the EEG signal processing and classification, streaming the data via UDP through the Reh@Panel (RehabNet Control Panel) for controlling the virtual environment. The Reh@Panel is a free tool that acts as a middleware between multiple interfaces and virtual environments (Vourvopoulos et al., 2013).


Figure 1. Experimental setup, including: (A) the wireless EEG system; (B) the Oculus HMD, together with headphones reproducing the ambient sound from the virtual environment; (C) the vibrotactile modules supported by a custom-made table-tray, similar to the wheelchair trays used for support; (D) the visual feedback with NeuRow game. A written informed consent was obtained for the publication of this image.


Continue —->  Frontiers | Efficacy and Brain Imaging Correlates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report | Frontiers in Human Neuroscience

, , , , , , , , , , , , ,

Leave a comment

[Abstract] Attention-controlled assistive wrist rehabilitation using a low-cost EEG Sensor


It is essential to make sure patients be actively involved in motor training using robot-assisted rehabilitation to achieve better rehabilitation outcomes. This paper introduces an attention-controlled wrist rehabilitation method using a low-cost EEG sensor. Active rehabilitation training is realized using a threshold of the attention level measured by the low-cost EEG sensor as a switch for a flexible wrist exoskeleton assisting wrist ?exion/extension and radial/ulnar deviation. We present a prototype implementation of this active training method and provide a preliminary evaluation. The feasibility of the attention-based control was proven with the overall actuation success rate of 95%. The experimental results also proved that the visual guidance was helpful for the users to concentrate on the wrist rehabilitation training; two types of visual guidance, namely looking at the hand motion shown on a video and looking at the user’s own hand, had no significant performance difference; a general threshold of a certain group of users can be utilized in the wrist robot control rather than a customized threshold to simplify the procedure.

via Attention-controlled assistive wrist rehabilitation using a low-cost EEG Sensor – IEEE Journals & Magazine

, , , , , , , ,

Leave a comment

[NEWS] The brain-computer interface at UCLA – from the 1970s to today

Apr 19, 2019 By UCLA Samueli Newsroom

In 1973, UCLA computer science professor Jacques Vidal published a landmark paper, “Toward direct brain-computer communication” that both coined the term “brain-computer interface” and set the foundation for an emerging field.

“That whole concept of interacting with and sensing the brain – interpreting signals with a computer and controlling the cursor on a computer with the mind – that paper is pretty much the essence of it,” said Dejan Markovic, a professor of electrical and computer engineering and leader of the Parallel Data Architectures Laboratory. “The real question is: Can we build technologies that enable those types of things that are clinically sustainable, efficacious, and attractive to patients?”

Looking to answer that question, Markovic carries on the legacy of brain-computer interface research at the UCLA Samueli School of Engineering. For nearly a decade, he has been leading the development of a device that would be implanted in the brain to help people with a range of neurological conditions, such as anxiety, depression, or post-traumatic stress disorder.  And he’s been working closely with doctors and scientists at UCLA and UC San Francisco who study the brain.

“The concepts laid out in 1973 by Vidal haven’t changed too much,” he added. “The brain and a computer can ‘talk’ to each other through electrical signals. The big thing that we are trying to change is to be able to quantify what those signals are, and affect functional networks of the brain.”

Markovic’s prototype is a small implantable device with sixty-four electrodes that fan out onto the brain’s surface. With four modules for each electrode, it constitutes a 256-channel system. The system measures tiny electric signals that tell what’s happening in the brain. The device then interprets that data, and responds with electrical pulses, which research has shown can alter mood.

In several ways, it is leaps and bounds more advanced than implants that have come before it. It’s much smaller for one. In fact it’s not immediately noticeable, unless someone’s really looking for it. It has a tiny battery than can be wirelessly charged. The device is also much more sensitive, able to detect and decipher very faint signals from the brain.

Finally, it’s a closed loop system – meaning that while still picking up the brain’s signals, it can modify the frequency and amplitude of the stimulating signal. The system brings much more data into the loop, giving  doctors and scientists more information about what’s happening in real time . Other devices only deliver a constant electric signal, while this new system offers a therapy  that can be more personalized to a particular patient

“Our technology could revolutionize non-pharmacological treatment of brain disorders,” Markovic said. “We want to be able to understand how various indications are expressed in the actual time waveforms, from specific points inside the brain.”

Markovic and UC San Francisco colleagues saw a major breakthrough in an experiment, which was funded by the Defense Advanced Research Projects Agency. A patient with severe anxiety was recorded before and after electrical stimulation was applied. The change in mood following stimulation was immediate and striking.

“For a person to say, ‘now I feel normal, this is me,’ that was the biggest impact point,” he said.

With a series of successful demonstrations, Markovic is now looking to commercialize the technology.  This includes miniaturizing the external device down to just four cubic centimeters. But first, why go with a brain implant in the first place?

“The brain is an electrochemical organ and the vast majority of our treatments for neurological and psychiatric diseases focus on the chemical part,” explained Dr. Nader Pouratian, a UCLA neurosurgeon working with Markovic. “The goal with devices like the one that Dr. Markovic is creating is to target the electrical abnormalities that occur in the brain as a result of neurological and psychiatric disease.”

Added Markovic, “We are looking into patients that have tried pharmaceuticals. In some people, pharmaceuticals have some effect, but there are a sizeable amount of people where pharmaceuticals do not help.”

On a parallel track, Markovic’s technology also offers scientists a powerful magnifying glass into the inner workings of the brain. One of his collaborators is Nanthia Suthana, a UCLA assistant professor at the Jane and Terry Semel Institute for Neuroscience and Human Behavior who studies neuromodulation and neuroimaging.

“The research potential is really endless with such a device,” Suthana said. “Relevant to my own research field, we will be able to investigate the role of single neuron and local field potential activity in freely moving human behaviors such as in spatial navigation, learning and memory.”

“These newer details will allow us to better understand the neuronal mechanisms that support typical human brain functions as well as abnormalities that may occur in neurologic and psychiatric disorders such epilepsy,” she added.


via The brain-computer interface at UCLA – from the 1970s to today | UCLA Samueli School Of Engineering


Leave a comment

%d bloggers like this: