Posts Tagged BCI

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

Abstract

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.

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

Highlights

  • 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.

Abstract

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

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[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

ABSTRACT

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.

INTRODUCTION

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.

PASSIVE VS. ACTIVE DELIVERY OF FES

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.

AN ACTIVE EEG-BASED BCI-FES SYSTEM

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.

DISCUSSION

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.

REFERENCES

(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.

ACKNOWLEDGMENT

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

 

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[WEB PAGE] Upper arm rehabilitation after severe stroke: where are we? – Physics World

10 Sep 2019 Andrea Rampin 
EEG cap

Stroke is the second leading cause of death worldwide and the third cause of induced disability, according to estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study. Treatments based on constraint-induced movement therapy, occupational practice, virtual reality and brain stimulation can work well for patients with mild impairment of upper limb movement, but they are not as effective for those burdened by severe disability. Therefore, novel individualized approaches are needed for this patient group.

Martina Coscia from the Wyss Center for Bio and Neuroengineering in Geneva, and colleagues from several other Swiss institutes, have published a review paper summarizing the most advanced techniques in use today for treatment of severe, chronic stroke patients. The researchers describe techniques being developed for upper limb motor rehabilitation: from robotics and muscular electrical stimulation, to brain stimulation and brain–computer/machine interfaces (Brain 10.1093/brain/awz181).

Robot-aided rehabilitation approaches include movement-assisting exoskeletons and end-effector devices, which enable upper arm movement by stimulating the peripheral nervous system. These techniques can also trigger reorganization of the impaired peripheral nervous system and encourage rehabilitation of the damaged somatosensory system. Several studies have reported the efficiency of robot-aided rehabilitation, alone or in combination with other techniques, in the treatment of upper limb motor impairment. One study that included severely impaired individuals also demonstrated encouraging results.

Muscular electrical stimulation can help improve the connection of motor neurons to the spinal cord and the motor cortex. Researchers have also demonstrated that application of electrical stimuli to the muscles provides positive effects on the neurons responsible for sensory signal transduction to the brain, thereby improving the motion control loop function. By modulating motor neurons’ sensitivity, muscular electrical stimulation inhibits the muscle spasms observed in other treatments.

More recently, therapies have moved on from the simple use of currents to harnessing coordinated stimuli to orchestrate more complex, task-related movements. Although this particular set of techniques didn’t show a particular advantage over physiotherapy in long-term studies of patients with mild upper limb impairment, it did seem to have a stronger effect for chronic severe patients.

Stimulating the brain

Brain stimulation, meanwhile, stimulates cortical neurons in order to improve their ability to form new connections within the affected neural network. Brain stimulation techniques can be divided into two branches – electrical and magnetic – both of which can activate or inhibit neural activity, depending on the polarity and intensity of the stimulus.

Transcranial magnetic stimulation

Researchers have achieved encouraging results using both techniques. In particular, magnetic field-triggered inhibition of the contralesional hemisphere (the hemisphere that was not affected by the stroke) activity yielded positive results. Magnetic, low-frequency stimulation of the contralesional hemisphere also proved encouraging – improving the reach to grasp ability of patients, although only for small objects. Excitingly, some studies suggest that coupling contralesional cortex inhibition with magnetic stimulation of the chronically affected area could achieve effective results.

Within these techniques, one promising approach is invasive brain stimulation, in which a device is surgically implanted in a superficial region of the brain. Such techniques allow for more sustained and spatially-oriented stimulation of the desired brain regions. The Everest trial used such methods and showed significant improvement for a larger percentage of patients after 24 weeks, compared with standard rehabilitation protocols.

Another promising recent development is non-invasive deep-brain stimulation, achieved by temporally interfering electric fields. The authors envision that a deeper understanding of the complex mechanisms involved in the brain’s reactions to magnetic and electrical stimulation will provide an important assistance in clinical application of these techniques.

The final category, brain–computer or brain–machine interfaces (BCIs or BMIs), exploit electroencephalogram (EEG) patterns to trigger feedback or an action output from an external device. Devices that produce feedback are used to train the patient to recruit the correct zone of the brain and help reorganize its interconnections. These techniques have only recently transitioned to the clinic; however, early results and observations are promising. For example, a BCI technique coupled with muscular electrical stimulation restored patients’ ability to extend their fingers.

In recent years, researchers have also tested combinations of the techniques described above. For example, combinations of robotics and muscular electrical stimulation have shown encouraging results, especially when more than one articulation was targeted by the treatment. Combining brain stimulation with muscular electrical stimulation and robotics has proved more effective in severe than in moderate cases. Also, coupling of muscular electrical stimulation with magnetic inhibitory brain stimulation provided better results than either individual technique. Interestingly, addition of electrical brain stimulation to a BCI system coupled with a robotic motor feedback enhanced the outcome, helping to achieve adaptive brain remodelling at the expense of inappropriate reorganization.

Coscia and co-authors highlight that all the techniques studied share a range of limitations that should be addressed, such as small sample size, limited understanding of the underlying mechanisms, lack of treatment personalization and minimal attention to the training task, which they note is often of limited importance for daily life. Addressing these limitations might be key to improving the clinical outcome for patients with severe stroke-induced upper limb paralysis treated with neurotechnology-aided interventions. Moreover, the authors plan to begin a clinical trial to test the use of a novel personalized therapy approach that will include a combination of the described techniques.

 

via Upper arm rehabilitation after severe stroke: where are we? – Physics World

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[Abstract + References] eConHand: A Wearable Brain-Computer Interface System for Stroke Rehabilitation

Abstract

Brain-Computer Interface (BCI) combined with assistive robots has been developed as a promising method for stroke rehabilitation. However, most of the current studies are based on complex system setup, expensive and bulky devices. In this work, we designed a wearable Electroencephalography(EEG)-based BCI system for hand function rehabilitation of the stroke. The system consists of a customized EEG cap, a small-sized commercial amplifer and a lightweight hand exoskeleton. In addition, visualized interface was designed for easy use. Six healthy subjects and two stroke patients were recruited to validate the safety and effectiveness of our proposed system. Up to 79.38% averaged online BCI classification accuracy was achieved. This study is a proof of concept, suggesting potential clinical applications in outpatient environments.

2. E. Donchin , K. Spencer and R. Wijesinghe , “The mental prosthesis: assessing the speed of a P300-based brain-computer interface”, IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 174-179, 2000.

3. D. McFarland and J. Wolpaw , “Brain-Computer Interface Operation of Robotic and Prosthetic Devices”, Computer, vol. 41, no. 10, pp. 52-56, 2008.

4. Xiaorong Gao , Dingfeng Xu , Ming Cheng and Shangkai Gao , “A bci-based environmental controller for the motion-disabled”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 137-140, 2003.

5. A. Ramos-Murguialday , D. Broetz , M. Rea et al “Brain-machine interface in chronic stroke rehabilitation: A controlled study”, Annals of Neurology, vol. 74, no. 1, pp. 100-108, 2013.

6. F. Pichiorri , G. Morone , M. Petti et al “Brain-computer interface boosts motor imagery practice during stroke recovery”, Annals of Neurology, vol. 77, no. 5, pp. 851-865, 2015.

7. M. A. Cervera , S. R. Soekadar , J. Ushiba et al “Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis”, Annals of Clinical and Translational Neurology, vol. 5, no. 5, pp. 651-663, 2018.

8. K. Ang , K. Chua , K. Phua et al “A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke”, Clinical EEG and Neuroscience, vol. 46, no. 4, pp. 310-320, 2014.

9. N. Bhagat , A. Venkatakrishnan , B. Abibullaev et al “Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors”, Frontiers in Neuroscience, vol. 10, pp. 122, 2016.

10. J. Webb , Z. G. Xiao , K. P. Aschenbrenner , G. Herrnstadt , and C. Menon , “Towards a portable assistive arm exoskeleton for stroke patient rehabilitation controlled through a brain computer interface”, in Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS International Conference, pp. 1299-1304, 2012.

11. A. L. Coffey , D. J. Leamy , and T. E. Ward , “A novel BCI-controlled pneumatic glove system for home-based neurorehabilitation”, in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pp. 3622-3625, 2014.

12. D. Bundy , L. Souders , K. Baranyai et al “Contralesional Brain-Computer Interface Control of a Powered Exoskeleton for Motor Recovery in Chronic Stroke Survivors”, Stroke, vol. 48, no. 7, pp. 1908-1915, 2017.

13. X. Shu , S. Chen , L. Yao et al “Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients”, Frontiers in Neuroscience, vol. 12, pp. 93, 2018.

14. A. Delorme , T. Mullen , C. Kothe et al “EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing”, Computational Intelligence and Neuroscience, vol. 2011, pp. 1-12, 2011.

15. G. Schalk , D. McFarland , T. Hinterberger , N. Birbaumer and J. Wolpaw , “BCI2000: A General-Purpose Brain-Computer Interface (BCI) System”, IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 1034-1043, 2004.

16. M. H. B. Azhar , A. Casey , and M. Sakel , “A cost-effective BCI assisted technology framework for neurorehabilitation”, The Seventh International Conference on Global Health Challenges, 18th-22nd November, 2018. (In Press)

17. C. M. McCrimmon , M. Wang , L. S. Lopes et al “A small, portable, battery-powered brain-computer interface system for motor rehabilitation”, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2776-2779, 2016.

18. J. Meng , B. Edelman , J. Olsoe et al “A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance”, Frontiers in Neuroscience, vol. 12, pp. 227, 2018.

19. T. Mullen , C. Kothe , Y. Chi et al “Real-time neuroimaging and cognitive monitoring using wearable dry EEG”, IEEE Transactions on Biomedical Engineering, vol. 62, no. 11, pp. 2553-2567, 2015.

 

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[Abstract + References] Self-paced movement intention recognition from EEG signals during upper limb robot-assisted rehabilitation

Abstract

Currently, one of the challenges in EEG-based brain-computer interfaces (BCI) for neurorehabilitation is the recognition of the intention to perform different movements from same limb. This would allow finer control of neurorehabilitation and motor recovery devices by end-users [1]. To address this issue, we assess the feasibility of recognizing two self-paced movement intentions of the right upper limb plus a rest state from EEG signals recorded during robot-assisted rehabilitation therapy. In addition, the work proposes the use of Multi-CSP features and deep learning classifiers to recognize movement intentions of the same limb. The results showed performance peaked greater at (80%) using a novel classification models implemented in a multiclass classification scenario. On the basis of these results, the decoding of the movement intention could potentially be used to develop more natural and intuitive robot assisted neurorehabilitation therapies
1. S. R. Soekadar , N. Birbaumer , M. W. Slutzky , and L. G. Cohen , “Brain machine interfaces in neurorehabilitation of stroke,” Neurobiology of Disease, vol. 83, pp. 172-179, 2015.

2. P. Ofner , A. Schwarz , J. Pereira , and G. R. Müller-Putz , “Upper limb movements can be decoded from the time-domain of low-frequency EEG,” PLoS One, vol. 12, no. 8, p. e0182578, Aug 2017, poNE-D- 17-04785[PII].

3. F. Shiman , E. Lopez-Larraz , A. Sarasola-Sanz , N. Irastorza-Landa , M. Spler , N. Birbaumer , and A. Ramos-Murguialday , “Classification of different reaching movements from the same limb using EEG,” Journal of Neural Engineering, vol. 14, no. 4, p. 046018, 2017.

4. J. Pereira , A. I. Sburlea , and G. R. Müller-Putz , “EEG patterns of self- paced movement imaginations towards externally-cued and internally- selected targets,” Scientific Reports, vol. 8, no. 1, p. 13394, 2018.

5. R. Vega , T. Sajed , K. W. Mathewson , K. Khare , P. M. Pilarski , R. Greiner , G. Sanchez-Ante , and J. M. Antelis , “Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals,” Artif. Intell. Research, vol. 6, no. 1, p. 37, 2017.

6. I. Figueroa-Garcia et al , “Platform for the study of virtual task- oriented motion and its evaluation by EEG and EMG biopotentials,” in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug 2014, pp. 1174–1177.

7. B. Graimann and G. Pfurtscheller , “Quantification and visualization of event-related changes in oscillatory brain activity in the timefrequency domain,” in Event-Related Dynamics of Brain Oscillations, ser. Progress in Brain Research, C. Neuper and W. Klimesch , Eds. Elsevier, 2006, vol. 159, pp. 79 – 97.

8. G. Pfurtscheller and F. L. da Silva , “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clinical Neurophysiology, vol. 110, no. 11, pp. 1842 – 1857, 1999.

9. G. Dornhege , B. Blankertz , G. Curio , and K. Muller , “Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 993–1002, 2004.

10. X. Yong and C. Menon , “EEG classification of different imaginary movements within the same limb,” PLOS ONE, vol. 10, no. 4, pp. 1–24, 04 2015.

11. L. G. Hernandez , O. M. Mozos , J. M. Ferrandez , and J. M. Antelis , “EEG-based detection of braking intention under different car driving conditions,” Frontiers in Neuroinformatics, vol. 12, p. 29, 2018. [Online]. Available: https://www.frontiersin.org/article/10.3389/fninf.2018.00029

12. L. G. Hernandez and J. M. Antelis , “A comparison of deep neural network algorithms for recognition of EEG motor imagery signals,” in Pattern Recognition, 2018, pp. 126–134.

13. M. Abadi et al , “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org/

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[WEB SITE] FDA Approves MindMotion GO, Mobile Neurorehabilitation Product

The US Food and Drug Administration (FDA) has granted clearance to MindMotion GO, a portable neurorehabilitation product, for launch in the United States.

MindMotion GO utilizes technology that is designed to be used by patients with mild to lightly severe neurological impairments, as well as in the recovery phase of rehabilitation. Produced by the Swiss neurogaming company MindMaze, the mobile rehabilitation product is an outpatient addition to its MindMotion PRO, which received FDA approval in May 2017.

The PRO version differs from the recently approved MindMotion GO in that it is intended for use in patients with severe impairments as well as in early hospital care—in an inpatient setting—with therapeutic activities able to take place within 4 days after a neurological incident.

“Now that both MindMotion products have FDA clearance, MindMaze delivers a full spectrum of neuro-care solutions for both inpatient and outpatient recovery for patients in the United States,” said Tej Tadi, PhD, the CEO and founder of MindMaze, in a statement. “Our unique capability to safely and securely acquire data through our platform is essential for patient recovery and performance, and positions MindMaze as a powerhouse for the future of brain-machine interfaces. Beyond healthcare, this will enable powerful AI-based applications. We are working on a range of brain-tech initiatives at MindMaze to build the infrastructure for innovations to improve patients’ quality of life.”

The mobile MindMotion GO allows for real-time audio and visual feedback, aiding physicians in the assessment of progress and tailoring of therapy to their individual patient’s performance, according to MindMaze. Additionally, it enables the patients to see their progress as well. The set-up and calibration can be done in less than 5 minutes, so patients can begin rehabilitation sessions while physicians facilitate case management.

The program is equipped with a variety of gamified engaging activities which cover motor and task functions and includes a 3D virtual environment. As a result, early findings have suggested that both patient engagement and adherence to therapy have been amplified. Thus far, MindMotion GO has been trialed with upward of 300 patients across therapy centers in the UK, Italy, Germany, and Switzerland.

Neurological impairments are the main cause of long-term disability in the United States, with a recent study estimating direct and indirect costs associated with neurological diseases cost roughly $800 billion annually. For stroke alone, there are almost 800,000 cases each year, with direct annual costs estimated at $22.8 billion.

MindMaze’s Continuum of Care seeks to support earlier, and ongoing, intervention to enable by healthcare providers in the United States to have access to a cost-effective solution for improving neurorehabilitation results.

Even more resources pertaining to stroke prevention and care can be found on MD Magazine‘s new sister site, NeurologyLive.

via FDA Approves MindMotion GO, Mobile Neurorehabilitation Product | MD Magazine

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[WEB SITE] Restoring the function of arms that have been disconnected from the brain

Advances in the control of prosthetic arms, or even exoskeletal arms, continue to amaze. Yet someone with a severe neck injury doesn’t need any such device since the greatest arm they could imagine is sitting right there hanging off their shoulder — but unable to perform. Efforts to control an artificial arm may seem impotent to these folks, when a bridge spanning just a couple centimeters of scar tissue in the spinal column can not even be made. A way forward is now taking shape at Case Western University in Ohio. Researchers there are gearing up to combine the Braingate cortical chip developed at Brown University with their own Functional Electric Stimulation (FES) platform.

It has long been known that electrical stimulation can directly control muscles. The problem is that it is fairly inaccurate, and can be painful or damaging. Stimulating the nerves directly using precisely positioned arrays is a much better approach. One group of Case Western researchers recently demonstrated a remarkable device called a nerve cuff electrode that can be placed around small segments of nerve. They used the cuff to provide an interface for sending data from sensors in the hand back to the brain using sensory nerves in the arm. With FES, the same kind of cuff electrode can also be used to stimulate nerves going the other direction, in other words, to the muscles.

Arm Muscles

The difficulty in such a scheme, is that even if the motor nerves can be physically separated from the sensory nerves and traced to specific muscles, the exact stimulation sequences needed to make a proper movement are hard to find. To achieve this, another group at Case Western has developed a detailed simulation of how different muscles work together to control the arm and hand. Their model consists of 138 muscle elements distributed over 29 muscles, which act on 11 joints. The operational procedure is for the patient to watch the image of the virtual arm while they naturally generate neural commands that the BrainGate chip picks up to move the arm. (In practice, this means trying to make the virtual arm touch a red spot to make it turn green.) Currently in clinical trials, the Braingate2 chip has an array of 96 hair-thin electrodes that is used to stimulate a small region of motor cortex.

The trick here is not just to find any sequence that gets the arm from point A to point B, but to find sequences similar to those that real arms actually use in particular tasks. This is important because each muscle has not only a limited contraction range, but also a limited range where it can actually deliver significant force, and generate feedback signals about those forces. When muscles contract they obviously change shape, but less obvious perhaps, is that their shape at any given moment affects how the other muscles leverage the joints they work. Just as important is the effect of the opposing muscles that control counter movements.

ArmSim

Few movements that we make, even low-force movements, consist of pure contractions of the active muscle and pure inhibition of the opposing muscle. In actuality, muscle units on both sides can be firing in alternating bursts to quickly ratchet joint angles open, particularly when the vector of end-point movement is oblique to the axes of individual arm segments. In other words, even in a simple movement like a bench press, both the biceps and triceps generate forces alternately at various points in the lift, despite the fact that the weight rises uniformly in the upward direction.

If artificial methods of control are going to be used for flesh-and-blood systems, particularly ones that have been idle for some time, overstimulation (or mis-stimulation) when lifting anything even slightly heavy is something to be guarded against. Many sports injuries, such as those in older people performing unfamiliar moves, happen not because they reach too far or too hard, but because their nervous system is not sufficiently practiced to be able to protect the muscle.

While no model for limb movement can be perfect, for the majority of everyday tasks, close may be good enough. The eventual plan is that the patient and the control algorithm will learn together in tandem so that the training screen will not be needed at all. At that point, we might say that Case Western will have a pretty slick interface to offer.

via Restoring the function of arms that have been disconnected from the brain – ExtremeTech

 

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[ARTICLE] Behavioral Outcomes Following Brain–Computer Interface Intervention for Upper Extremity Rehabilitation in Stroke: A Randomized Controlled Trial – Full Text

Stroke is a leading cause of persistent upper extremity (UE) motor disability in adults. Brain–computer interface (BCI) intervention has demonstrated potential as a motor rehabilitation strategy for stroke survivors. This sub-analysis of ongoing clinical trial (NCT02098265) examines rehabilitative efficacy of this BCI design and seeks to identify stroke participant characteristics associated with behavioral improvement. Stroke participants (n = 21) with UE impairment were assessed using Action Research Arm Test (ARAT) and measures of function. Nine participants completed three assessments during the experimental BCI intervention period and at 1-month follow-up. Twelve other participants first completed three assessments over a parallel time-matched control period and then crossed over into the BCI intervention condition 1-month later. Participants who realized positive change (≥1 point) in total ARAT performance of the stroke affected UE between the first and third assessments of the intervention period were dichotomized as “responders” (<1 = “non-responders”) and similarly analyzed. Of the 14 participants with room for ARAT improvement, 64% (9/14) showed some positive change at completion and approximately 43% (6/14) of the participants had changes of minimal detectable change (MDC = 3 pts) or minimally clinical important difference (MCID = 5.7 points). Participants with room for improvement in the primary outcome measure made significant mean gains in ARATtotalscore at completion (ΔARATtotal = 2, p = 0.028) and 1-month follow-up (ΔARATtotal = 3.4, p= 0.0010), controlling for severity, gender, chronicity, and concordance. Secondary outcome measures, SISmobility, SISadl, SISstrength, and 9HPTaffected, also showed significant improvement over time during intervention. Participants in intervention through follow-up showed a significantly increased improvement rate in SISstrength compared to controls (p = 0.0117), controlling for severity, chronicity, gender, as well as the individual effects of time and intervention type. Participants who best responded to BCI intervention, as evaluated by ARAT score improvement, showed significantly increased outcome values through completion and follow-up for SISmobility (p = 0.0002, p = 0.002) and SISstrength (p = 0.04995, p = 0.0483). These findings may suggest possible secondary outcome measure patterns indicative of increased improvement resulting from this BCI intervention regimen as well as demonstrating primary efficacy of this BCI design for treatment of UE impairment in stroke survivors.

Introduction

Stroke

Each year there are approximately 800,000 new incidences of stroke in the United States (Benjamin et al., 2017), and in 2010 there were an estimated 16.9 million stroke events globally (Mozaffarian et al., 2015). Stroke occurs as a result of a blockage of blood flow in an area of the brain or by rupture of brain vasculature causing death or damage to local and distal brain tissue. In either etiology, survivors may experience some level of upper extremity (UE) physical impairment. Despite recent advances in acute care, an increasing number of stroke survivors face long-term motor deficits (Benjamin et al., 2017). Costs of care for long-term disability resulting from stroke are substantial with the direct medical costs of stroke estimated to $17.9 billion in 2013 (Benjamin et al., 2017). It is crucial that motor therapy for stroke enhances a survivor’s capacity to autonomously participate in activities of daily living (ADLs), thereby decreasing dependency on caregivers as well as the cost and level of care necessary (Dombovy, 2009Stinear, 2016). Efficacious motor therapy should be designed to improve the overall quality of life for the individual survivor based on their goals and needs (Remsik et al., 2016Stinear, 2016).

Need for Treatment

Survivors in the chronic stage of stroke are the most desperate for rehabilitation. Existing pharmacological treatments and behavioral therapy methods primarily serve to treat symptoms associated with stroke (Benjamin et al., 2017) and may not bring about optimal changes in brain function or connectivity (Power et al., 2011Nair et al., 2015). While a growing population of research suggests the greatest potential for recovery in the post-stroke brain occurs within the first months after insult (Stinear and Byblow, 2014), neuroplastic capacity has been demonstrated in both acute and chronic phases (Caria et al., 2011Ang et al., 2015). Spontaneous biological recovery (SBR) (Beebe and Lang, 2009Cramer and Nudo, 2010) in the initial days and weeks following stoke (acute phase) is thought to represent a critical period in the complex progression of motor recovery, which combines neurobiological processes and learning-related elements. After this window of SBR, it is posited a sensitive period of neurorecovery persists, plateauing around 6 months post-stroke (Wolf et al., 20062010Dromerick et al., 2009Cramer and Nudo, 2010). Traditional rehabilitation therapies generally lose efficacy after such time and the course of standard of care treatment options is exhausted leaving chronically impaired persons with few options.

Potential for Treatment

Motor and cognitive recovery after these initial windows may no longer occur in the same spontaneous nature as is observed during SBR. However, innovative therapeutic techniques show some efficacy generating functional motor recovery beyond the traditional rehabilitation windows (Cramer and Nudo, 2010Ang et al., 2015Irimia et al., 2016). Brain–computer interfaces (BCIs), a novel rehabilitation tool, have shown proof of concept for rehabilitating volitional movements in stroke survivors (Muralidharan et al., 2011Song et al., 20142015Young et al., 2014a,b,c,d2015Irimia et al., 2016). In this growing area of research, developing technologies demonstrate promising potential for treating hemiparesis in a clinically viable and efficient manner and they may offer an avenue to increased autonomy for patients reducing their cost and burden of care.

Effectiveness of Current BCI Therapies

There is currently considerable variability in design and efficacy of BCI therapies as well as little consensus with respect to proper arrangement, administration, and dosing (Muralidharan et al., 2011Ang and Guan, 2013Young et al., 2014aAng et al., 2015Irimia et al., 2016Remsik et al., 2016Bundy et al., 2017Dodd et al., 2017). Although acute stroke care has improved morbidity outcomes significantly, current treatments for persistent UE motor impairment resulting from stroke offer only limited restoration of UE motor function the further from stroke a survivor progresses (Wolf et al., 20062010Dromerick et al., 2009Benjamin et al., 2017Stinear et al., 2017). Evidence suggests both acute and chronic stroke patients respond to various neuro-rehabilitative BCI therapy strategies and can achieve clinically significant changes in measures of UE impairment (Young et al., 2014cIrimia et al., 2016Remsik et al., 2016). Furthermore, recent research also suggests that BCI therapy targeted at motor recovery may provide benefits in other brain regions outside of only the motor network (Mohanty et al., 2018).

Overview of This Study

This post hoc analysis of an ongoing clinical trial (NCT02098265) (Song et al., 20142015Young et al., 2014a,b,c,d2015) evaluates the effects of an interventional, non-invasive closed-loop electroencephalography (EEG)-based BCI intervention for the restoration of distal UE motor function in stroke survivors. Participants who showed measurable change in the primary outcome measure were grouped post hoc. This sub-analysis seeks to identify whether there are participant characteristics strongly associated with motor improvement as measured by primary and secondary outcome measures of UE function. These analyses are intended to inform future BCI research approaches and intervention designs as well as suggest and encourage appropriate participant selection.[…]

 

Continue —>  Frontiers | Behavioral Outcomes Following Brain–Computer Interface Intervention for Upper Extremity Rehabilitation in Stroke: A Randomized Controlled Trial | Neuroscience

FIGURE 2. BCI intervention block design: (1) A pre-session open-loop screening task of two attempted and then two imagined grasping tasks (left, right, rest) is used to set control features (BCI classifier) for the forthcoming intervention task (Cursor Task). (2) The closed-loop cursor and target (visual only) intervention condition consists of at least 10 runs of 10 trials of attempted grasping movements for the purpose of guiding a virtual cursor (Ball) either left, or right as cued by the target (Goal) presentation on the horizontal edge of the screen. (3) Following 10 successfully completed runs of the visual only condition, adjuvant stimuli are added to enrich the feedback environment and facilitate volitional movement of the affected extremity (grasping). Subsequent runs are attempted at the preferred pace of the participant, completing as many runs as time allows. (4) With 15 min remaining in the 2-h intervention session, the participant is switched into the post-session open-loop screening task of two imagined and then two attempted grasping tasks (left, right, rest).

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[WEB SITE] Building a better brain-computer interface

Building a better brain-computer interface

Photo of a dummy BrainGate interface. Credit: Paul Wick/Wikimedia Commons

October 2, 2018 by Matt Miles, Medical Xpress

Brain-computer interfaces, or BCIs, represent relatively recent advances in neurotechnology that allow computer systems to interact directly with human or animal brains. This technology is particularly promising for use in cases of spinal cord injury or paralysis. In these situations, patients may be able to use neural decoders that access part of their brain to operate a prosthetic limb or even to re-animate a paralyzed limb through functional electrical stimulation (FES).

Michael A. Schwemmer and colleagues, in a recent Nature Medicine article, detail their research on BCIs using  decoders with a participant with tetraplegia due to spinal cord injury. Their research focuses on addressing several key needs identified by end-users of BCI systems, namely: high , minimal daily setup, rapid response time, and multifunctionality—all of which are characteristics heavily influenced by a BCI’s particular neural decoding algorithm.

Schwemmer’s group describes several different approaches to training and testing three variations on neural network decoders (NN-BCI) in comparison with each other and a benchmark support vector machine (SVM) decoder. The four BCI decoder paradigms were developed and tested over the course of several years in association with a 27-year-old male participant with tetraplegia. The participant had a 96-channel microelectrode array implanted in the area of his left primary motor cortex corresponding to the hand and arm. Using intracortical data collected from 80 sessions over 865 days, the investigators trained and evaluated these BCI decoders. These sessions consisted of two 104-second blocks of a four-movement task: index extension, index flexion, wrist extension, and wrist flexion.

The initial neural network (NN)  was developed and calibrated using data from the first 40 sessions (80 blocks); it was not updated over the second half of the training/testing period, and is referred to here as the fixed neural network (fNN) model. From the fNN, two other neural network models were created: a supervised updating (sNN) model and an unsupervised updating (uNN) model. Both models used data from the first block of the second 40-session (updating/testing) period. The sNN model’s algorithm relies on explicit training labels, that is, known timing and type of movement, whereas the uNN model relies on undifferentiated or unknown direct input in relation to intended action of the limb. The second block of the second 40-session period was used for accuracy testing of all models—fNN, sNN, uNN, and SVM.

The purpose of using four separate models here was to test and demonstrate various aspects of the three neural network models in relation to each other and the benchmark SVM model. For instance, the supervised neural network (sNN) model was updated daily (during the first block of the second 40-session period) and compared directly with the daily-retrained SVM model. The fixed neural network (fNN) model was provided to demonstrate that a BCI could sustain accuracy for over a year with no updates.

The unsupervised neural network (uNN) was perhaps the most interesting comparator, as we shall see, because it attempted to combine the improved accuracy gained from daily updates but without the consequent daily setup time required by the sNN model. Accuracy was the key performance measure in all tests, defined here as a percentage of correctly predicted time-bins in the second block of the second 40 sessions; the criterion of greater than 90% accuracy was one of the four end-user requirements originally articulated at the outset of the study.

The sNN consistently outperformed the daily-retrained SVM: in 37 out of 40 sessions, its accuracy was > 90%, whereas the SVM only achieved > 90% accuracy in 12 sessions. The fNN also outperformed the SVM in 36 of 40 sessions; it achieved > 90% accuracy in 32 sessions. The fNN accuracy was, not surprisingly, lower than the accuracy of the sNN, and both fixed decoders, fNN and SVM, declined in accuracy over the course of the study period, in contrast to the daily-updated decoders.

Perhaps the most interesting finding of this research however, is the performance of the unsupervised neural network (uNN), which outperformed both fixed models in terms of accuracy, while also meeting the end-user requirement of minimal daily set-up. Where the sNN model required explicit daily training, the uNN incorporated data from general use in its update schema, which required no such daily set-up. In comparison with the fNN, a performance gap emerged over time, and the benefits of the uNN distinguished themselves. The uNN also outperformed the SVM in terms of response time, another key end-user requirement.

Another important aspect of this study with regard to NNs focused on transfer learning, whereby new movements can be added to the existing repertoire with minimal additional training and data. In this case, “hand open” and “hand close” were added to the previous four movements, and all decoders were rebuilt. Here too, unsupervised updating was used to build an unsupervised transfer  (utNN), which, after only one session of training oupterformed the SVM model.

Finally, the previous research—all of which was conducted in an “offline” setting—was applied, via the participant’s FES-controlled hand and forearm, to show that a transfer learning uNN trained on the original four-movement task could be used to quickly create a new decoder to control, in real time, an open hand and three grips (can, fork, and peg). In a test of the system, the participant was able to perform all three hand movement grip tasks, with no failures, in 45 attempts. Previously, he was only able to perform one grip task successfully.

In summarizing how the results of their study relate to the main end-user expectations previously described, the investigators cite the following achievements: “(i) using deep NNs to create robust neural decoders that sustain high fidelity BCI control for more than a year without retraining; (ii) introducing a new updating procedure that can improve performance using data obtained through regular system use; (iii) extension of functionality through transfer learning using minimal additional data; and (iv) introducing a decoding framework that simultaneously addresses these four competing aspects of BCI performance (accuracy, speed, longevity, and multifunctionality). In addition, we provide a clinical demonstration that a decoder calibrated using historical data of imagined hand movements with no feedback can be successfully used in real-time to control FES-evoked grasp function for object manipulation.”

Schwemmer and colleagues go on to offer a more in-depth discussion of their results amidst the broader landscape of BCI research, and offer commentary on some of the specific challenges and limitations of their experiment. While noting that the median response time for uNN decoders (0.9 s) is still faster than that of SVM decoders (1.1 s), they acknowledge that a target of 750 ms or less is probably closer to realistic end-user expectations.

Ultimately they conclude: “We have demonstrated that decoders based on NNs may be superior to other implementations because new functions can be easily added after the initial decoder calibration using transfer learning. Crucially, we show that this secondary update to add more movements requires a minimal amount of additional data.” And “insights gained from offline data and analyses can carry over to a realistic online BCI scenario with minimal additional data collection.”

 Explore further: Using multi-task learning for low-latency speech translation

More information: Michael A. Schwemmer et al. Meeting brain–computer interface user performance expectations using a deep neural network decoding framework, Nature Medicine(2018). DOI: 10.1038/s41591-018-0171-y

via Building a better brain-computer interface

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