Posts Tagged electroencephalography

[Abstract+References] Longitudinal Structural and Functional Differences Between Proportional and Poor Motor Recovery After Stroke

Background. Evolution of motor function during the first months after stroke is stereotypically bifurcated, consisting of either recovery to about 70% of maximum possible improvement (“proportional recovery, PROP”) or in little to no improvement (“poor recovery, POOR”). There is currently no evidence that any rehabilitation treatment will prevent POOR and favor PROP. Objective. To perform a longitudinal and multimodal assessment of functional and structural changes in brain organization associated with PROP. Methods. Fugl-Meyer Assessments of the upper extremity and high-density electroencephalography (EEG) were obtained from 63 patients, diffusion tensor imaging from 46 patients, at 2 and 4 weeks (T0) and at 3 months (T1) after stroke onset. Results. We confirmed the presence of 2 distinct recovery patterns (PROP and POOR) in our sample. At T0, PROP patients had greater integrity of the corticospinal tract (CST) and greater EEG functional connectivity (FC) between the affected hemisphere and rest of the brain, in particular between the ventral premotor and the primary motor cortex. POOR patients suffered from degradation of corticocortical and corticofugal fiber tracts in the affected hemisphere between T0 and T1, which was not observed in PROP patients. Better initial CST integrity correlated with greater initial global FC, which was in turn associated with less white matter degradation between T0 and T1. Conclusions. These findings suggest links between initial CST integrity, systems-level cortical network plasticity, reduction of white matter atrophy, and clinical motor recovery after stroke. This identifies candidate treatment targets.

1. Prabhakaran, S, Zarahn, E, Riley, C. Inter-individual variability in the capacity for motor recovery after ischemic stroke. Neurorehabil Neural Repair. 2008;22:6471Google ScholarLink
2. Winters, C, van Wegen, EE, Daffertshofer, A, Kwakkel, G. Generalizability of the proportional recovery model for the upper extremity after an ischemic stroke. Neurorehabil Neural Repair. 2015;29:614622Google ScholarLink
3. Buch, ER, Rizk, S, Nicolo, P, Cohen, LG, Schnider, A, Guggisberg, AG. Predicting motor improvement after stroke with clinical assessment and diffusion tensor imaging. Neurology. 2016;86:19241925Google ScholarCrossrefMedline
4. Feng, W, Wang, J, Chhatbar, PY. Corticospinal tract lesion load: An imaging biomarker for stroke motor outcomes. Ann Neurol. 2015;78:860870Google ScholarCrossrefMedline
5. Byblow, WD, Stinear, CM, Barber, PA, Petoe, MA, Ackerley, SJ. Proportional recovery after stroke depends on corticomotor integrity. Ann Neurol. 2015;78:848859Google ScholarCrossrefMedline
6. Ma, C, Liu, A, Li, Z, Zhou, X, Zhou, S. Longitudinal study of diffusion tensor imaging properties of affected cortical spinal tracts in acute and chronic hemorrhagic stroke. J Clin Neurosci. 2014;21:13881392Google ScholarCrossrefMedline
7. Thomalla, G, Glauche, V, Koch, MA, Beaulieu, C, Weiller, C, Rother, J. Diffusion tensor imaging detects early Wallerian degeneration of the pyramidal tract after ischemic stroke. Neuroimage. 2004;22:17671774Google ScholarCrossrefMedline
8. Thomalla, G, Glauche, V, Weiller, C, Röther, J. Time course of Wallerian degeneration after ischaemic stroke revealed by diffusion tensor imaging. J Neurol Neurosurg Psychiatry. 2005;76:266268Google ScholarCrossrefMedline
9. Zarahn, E, Alon, L, Ryan, SL. Prediction of motor recovery using initial impairment and fMRI 48 h poststroke. Cereb Cortex. 2011;21:27122721Google ScholarCrossrefMedline
10. Nudo, RJ. Postinfarct cortical plasticity and behavioral recovery. Stroke. 2007;38(2 suppl):840845Google ScholarCrossrefMedline
11. Nudo, RJ, Wise, BM, SiFuentes, F, Milliken, GW. Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Science. 1996;272:17911794Google ScholarCrossrefMedline
12. Carmichael, ST. Cellular and molecular mechanisms of neural repair after stroke: making waves. Ann Neurol. 2006;59:735742Google ScholarCrossrefMedline
13. Murphy, TH, Corbett, D. Plasticity during stroke recovery: from synapse to behaviour. Nat Rev Neurosci. 2009;10:861872Google ScholarCrossrefMedline
14. Carmichael, ST, Chesselet, MF. Synchronous neuronal activity is a signal for axonal sprouting after cortical lesions in the adult. J Neurosci. 2002;22:60626070Google ScholarMedline
15. Buch, ER, Liew, SL, Cohen, LG. Plasticity of sensorimotor networks: multiple overlapping mechanisms. Neuroscientist. 2017;23:185196Google ScholarLink
16. Smith, SM, Jenkinson, M, Johansen-Berg, H. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006;31:14871505Google ScholarCrossrefMedline
17. Keihaninejad, S, Zhang, H, Ryan, NS. An unbiased longitudinal analysis framework for tracking white matter changes using diffusion tensor imaging with application to Alzheimer’s disease. Neuroimage. 2013;72:153163Google ScholarCrossrefMedline
18. Nicolo, P, Rizk, S, Magnin, C, Pietro, MD, Schnider, A, Guggisberg, AG. Coherent neural oscillations predict future motor and language improvement after stroke. Brain. 2015;138(pt 10):30483060Google ScholarCrossrefMedline
19. Grefkes, C, Nowak, DA, Eickhoff, SB. Cortical connectivity after subcortical stroke assessed with functional magnetic resonance imaging. Ann Neurol. 2008;63:236246Google ScholarCrossrefMedline
20. Rehme, AK, Eickhoff, SB, Wang, LE, Fink, GR, Grefkes, C. Dynamic causal modeling of cortical activity from the acute to the chronic stage after stroke. Neuroimage. 2011;55:11471158Google ScholarCrossrefMedline
21. Fugl-Meyer, AR, Jääskö, L, Leyman, I, Olsson, S, Steglind, S. The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. Scand J Rehabil Med. 1975;7:1331Google ScholarMedline
22. Gladstone, DJ, Danells, CJ, Black, SE. The Fugl-Meyer assessment of motor recovery after stroke: a critical review of its measurement properties. Neurorehabil Neural Repair. 2002;16:232240Google ScholarLink
23. Pierpaoli, C, Walker, L, Irfanoglu, MO. TORTOISE: an integrated software package for processing of diffusion MRI dataPaper presented at: ISMRM–ESMRMB Joint Annual MeetingMay 1-7, 2010Stockholm, SwedenGoogle Scholar
24. Rohde, GK, Barnett, AS, Basser, PJ, Pierpaoli, C. Estimating intensity variance due to noise in registered images: applications to diffusion tensor MRI. Neuroimage. 2005;26:673684Google ScholarCrossrefMedline
25. Leemans, A, Jones, DK. The B-matrix must be rotated when correcting for subject motion in DTI data. Magn Reson Med. 2009;61:13361349Google ScholarCrossrefMedline
26. Wu, M, Chang, LC, Walker, L. Comparison of EPI distortion correction methods in diffusion tensor MRI using a novel framework. Med Image Comput Comput Assist Interv. 2008;11(pt 2):321329Google ScholarMedline
27. Chang, LC, Jones, DK, Pierpaoli, C. RESTORE: robust estimation of tensors by outlier rejection. Magn Reson Med. 2005;53:10881095Google ScholarCrossrefMedline
28. Zhang, H, Yushkevich, PA, Alexander, DC, Gee, JC. Deformable registration of diffusion tensor MR images with explicit orientation optimization. Med Image Anal. 2006;10:764785Google ScholarCrossrefMedline
29. Zhang, H, Yushkevich, PA, Rueckert, D, Gee, JC. A computational white matter atlas for aging with surface-based representation of fasciculi. In: Fischer, B, Dawant, B, Lorenz, C, eds. Proceedings of the 4th Workshop on Biomedical Image RegistrationBerlin, GermanySpringer2010:8390Google ScholarCrossref
30. Winkler, AM, Ridgway, GR, Webster, MA, Smith, SM, Nichols, TE. Permutation inference for the general linear model. Neuroimage. 2014;92:381397Google ScholarCrossrefMedline
31. Mori, S, Wakana, S, van Zijl, PCM, Nagae-Poetscher, LM. MRI Atlas of Human White Matter. 1st ed.Amsterdam, NetherlandsElsevier2005Google Scholar
32. Stinear, CM, Barber, PA, Smale, PR, Coxon, JP, Fleming, MK, Byblow, WD. Functional potential in chronic stroke patients depends on corticospinal tract integrity. Brain. 2007;130(pt 1):170180Google ScholarCrossrefMedline
33. Dalal, SS, Zumer, JM, Guggisberg, AG. MEG/EEG source reconstruction, statistical evaluation, and visualization with NUTMEG. Comput Intell Neurosci. 2011;2011:758973Google ScholarCrossrefMedline
34. Guggisberg, AG, Dalal, SS, Zumer, JM. Localization of cortico-peripheral coherence with electroencephalography. Neuroimage. 2011;57:13481357Google ScholarCrossrefMedline
35. Stenroos, M, Mäntynen, V, Nenonen, J. A Matlab library for solving quasi-static volume conduction problems using the boundary element method. Comput Methods Programs Biomed. 2007;88:256263Google ScholarCrossrefMedline
36. Sekihara, K, Nagarajan, SS, Poeppel, D, Marantz, A. Asymptotic SNR of scalar and vector minimum-variance beamformers for neuromagnetic source reconstruction. IEEE Trans Biomed Eng. 2004;51:17261734Google ScholarCrossrefMedline
37. Nolte, G, Bai, O, Wheaton, L, Mari, Z, Vorbach, S, Hallett, M. Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin Neurophysiol. 2004;115:22922307Google ScholarCrossrefMedline
38. Sekihara, K, Owen, JP, Trisno, S, Nagarajan, SS. Removal of spurious coherence in MEG source-space coherence analysis. IEEE Trans Biomed Eng. 2011;58:31213129Google ScholarCrossrefMedline
39. Newman, ME. Analysis of weighted networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2004;70(5 pt 2):056131Google ScholarCrossrefMedline
40. Stam, CJ, van Straaten, EC. The organization of physiological brain networks. Clin Neurophysiol. 2012;123:10671087Google ScholarCrossrefMedline
41. De Vico Fallani, F, Richiardi, J, Chavez, M, Achard, S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philos Trans R Soc Lond B Biol Sci. 2014;369:20130521. doi:10.1098/rstb.2013.0521. Google ScholarCrossrefMedline
42. Mayka, MA, Corcos, DM, Leurgans, SE, Vaillancourt, DE. Three-dimensional locations and boundaries of motor and premotor cortices as defined by functional brain imaging: a meta-analysis. Neuroimage. 2006;31:14531474Google ScholarCrossrefMedline
43. Dubovik, S, Pignat, JM, Ptak, R. The behavioral significance of coherent resting-state oscillations after stroke. Neuroimage. 2012;61:249257Google ScholarCrossrefMedline
44. Guggisberg, AG, Rizk, S, Ptak, R. Two intrinsic coupling types for resting-state integration in the human brain. Brain Topogr. 2015;28:318329Google ScholarCrossrefMedline
45. Singh, KD, Barnes, GR, Hillebrand, A. Group imaging of task-related changes in cortical synchronisation using nonparametric permutation testing. Neuroimage. 2003;19:15891601Google ScholarCrossrefMedline
46. Gunn, SR. Support vector machines for classification and regression (Technical report)University of Southampton Published May 101998. Accessed October 17, 2017. Google Scholar
47. Kim, KH, Kim, YH, Kim, MS, Park, CH, Lee, A, Chang, WH. Prediction of motor recovery using diffusion tensor tractography in supratentorial stroke patients with severe motor involvement. Ann Rehabil Med. 2015;39:570576Google ScholarCrossrefMedline
48. Rondina, JM, Park, CH, Ward, NS. Brain regions important for recovery after severe post-stroke upper limb paresis. J Neurol Neurosurg Psychiatry. 2017;88:737743Google ScholarCrossrefMedline
49. Winters, C, van Wegen, EE, Daffertshofer, A, Kwakkel, G. Generalizability of the maximum proportional recovery rule to visuospatial neglect early poststroke. Neurorehabil Neural Repair. 2017;31:334342Google ScholarLink
50. Marchi, NA, Ptak, R, Di Pietro, M, Schnider, A, Guggisberg, AG. Principles of proportional recovery after stroke generalize to neglect and aphasia. Eur J Neurol. 2017;24:10841087Google ScholarCrossrefMedline
51. Fields, RD, Woo, DH, Basser, PJ. Glial regulation of the neuronal connectome through local and long-distant communication. Neuron. 2015;86:374386Google ScholarCrossrefMedline
52. Volz, LJ, Sarfeld, AS, Diekhoff, S. Motor cortex excitability and connectivity in chronic stroke: a multimodal model of functional reorganization. Brain Struct Funct. 2015;220:10931107Google ScholarCrossrefMedline
53. Ward, NS, Newton, JM, Swayne, OB. Motor system activation after subcortical stroke depends on corticospinal system integrity. Brain. 2006;129(pt 3):809819Google ScholarCrossrefMedline
54. Cunningham, DA, Machado, A, Janini, D. Assessment of inter-hemispheric imbalance using imaging and noninvasive brain stimulation in patients with chronic stroke. Arch Phys Med Rehabil. 2015;96(4 suppl):S94S103Google ScholarCrossrefMedline
55. Carter, AR, Patel, KR, Astafiev, SV. Upstream dysfunction of somatomotor functional connectivity after corticospinal damage in stroke. Neurorehabil Neural Repair. 2012;26:719Google ScholarLink
56. Rizk, S, Ptak, R, Nyffeler, T, Schnider, A, Guggisberg, AG. Network mechanisms of responsiveness to continuous theta-burst stimulation. Eur J Neurosci. 2013;38:32303238Google ScholarCrossrefMedline
57. Amadi, U, Ilie, A, Johansen-Berg, H, Stagg, CJ. Polarity-specific effects of motor transcranial direct current stimulation on fMRI resting state networks. Neuroimage. 2014;88:155161Google ScholarCrossrefMedline
58. Grefkes, C, Nowak, DA, Wang, LE, Dafotakis, M, Eickhoff, SB, Fink, GR. Modulating cortical connectivity in stroke patients by rTMS assessed with fMRI and dynamic causal modeling. Neuroimage. 2010;50:233242Google ScholarCrossrefMedline
59. Volz, LJ, Rehme, AK, Michely, J. Shaping early reorganization of neural networks promotes motor function after stroke. Cereb Cortex. 2016;26:28822894Google ScholarCrossrefMedline
60. Sehm, B, Schäfer, A, Kipping, J. Dynamic modulation of intrinsic functional connectivity by transcranial direct current stimulation. J Neurophysiol. 2012;108:32533263Google ScholarCrossrefMedline
61. Mottaz, A, Solcà, M, Magnin, C, Corbet, T, Schnider, A, Guggisberg, AG. Neurofeedback training of alpha-band coherence enhances motor performance. Clin Neurophysiol. 2015;126:17541760Google ScholarCrossrefMedline
62. Vukelic, M, Gharabaghi, A. Self-regulation of circumscribed brain activity modulates spatially selective and frequency specific connectivity of distributed resting state networks. Front Behav Neurosci. 2015;9:181Google ScholarCrossrefMedline
63. Liew, SL, Rana, M, Cornelsen, S. Improving motor corticothalamic communication after stroke using real-time fMRI connectivity-based neurofeedback. Neurorehabil Neural Repair. 2016;30:671675Google ScholarLink

via Longitudinal Structural and Functional Differences Between Proportional and Poor Motor Recovery After StrokeNeurorehabilitation and Neural Repair – Adrian G. Guggisberg, Pierre Nicolo, Leonardo G. Cohen, Armin Schnider, Ethan R. Buch, 2017


, , , , , , , ,

Leave a comment

[BLOG POST] Study: Transcranial e-stim beneficial in mild traumatic brain injury

Researchers from the University of California San Diego and from the Veterans Affairs San Diego Healthcare System have improved neural function in a group of people with mild traumatic brain injury using low-impulse electrical stimulation to the brain, according to a study published in Brain Injury.

Although little is understood about the pathology of mild TBI, the team of researchers noted that previous work has shown that passive neuro-feedback, low-intensity pulses applied to the brain through transcranial electrical stimulation, has promise as a potential treatment.

The team’s pilot study enrolled six people with mild TBI who were experiencing post-concussion symptoms. Researchers used a form of LIP-tES combined with concurrent electroencephalography monitoring and assessed the treatment’s effect using a non-invasive functional imaging technique, magnetoencephalography, before and after treatment.

“Our previous publications have shown that MEG detection of abnormal brain slow-waves is one of the most sensitive biomarkers for mild traumatic brain injury (concussions), with about 85 percent sensitivity in detecting concussions and, essentially, no false-positives in normal patients,” senior author Dr. Roland Lee said in prepared remarks. “This makes it an ideal technique to monitor the effects of concussion treatments such as LIP-tES.”

Researchers reported that the brains in all six patients had abnormal slow-waves at the time of initial scans. After treatment, MEG scans showed reduced abnormal slow-waves and the study participants reported a significant reduction in post-concussion scores.

“For the first time, we’ve been able to document with neuroimaging the effects of LIP-tES treatment on brain functioning in mild TBI,” first author Ming-Xiong Huang added. “It’s a small study, which certainly must be expanded, but it suggests new potential for effectively speeding the healing process in mild traumatic brain injuries.”

Source: Study: Transcranial e-stim beneficial in mild traumatic brain injury – MassDevice

, , , , , ,

Leave a comment

[Abstract] EEG-guided robotic mirror therapy system for lower limb rehabilitation – IEEE Conference Publication


Lower extremity function recovery is one of the most important goals in stroke rehabilitation. Many paradigms and technologies have been introduced for the lower limb rehabilitation over the past decades, but their outcomes indicate a need to develop a complementary approach. One attempt to accomplish a better functional recovery is to combine bottom-up and top-down approaches by means of brain-computer interfaces (BCIs). In this study, a BCI-controlled robotic mirror therapy system is proposed for lower limb recovery following stroke. An experimental paradigm including four states is introduced to combine robotic training (bottom-up) and mirror therapy (top-down) approaches. A BCI system is presented to classify the electroencephalography (EEG) evidence. In addition, a probabilistic model is presented to assist patients in transition across the experiment states based on their intent. To demonstrate the feasibility of the system, both offline and online analyses are performed for five healthy subjects. The experiment results show a promising performance for the system, with average accuracy of 94% in offline and 75% in online sessions.

Source: EEG-guided robotic mirror therapy system for lower limb rehabilitation – IEEE Conference Publication

, , , , , , ,

Leave a comment

[WEB SITE] BrainTrain


BRAINTRAIN will improve and adapt the methods of real-time fMRI neurofeedback (fMRI-NF) for clinical use, including the combination with electroencephalography (EEG) and the development of standardised procedures for the mapping of brain networks that can be targeted with neurofeedback.

Its core component will be the exploration of the efficacy of fMRI-NF in selected mental and neurodevelopmental disorders that involve motivational, emotional and social neural systems. The ultimate goals of BRAINTRAIN are therefore to :

tete ampoule v3

  • Develop new or optimize existing imaging technologies,
  • Validate their application as a therapeutic tool to mental and behavioural disorders by integrating imaging data with complementarity knowledge resulting bioinformatics and clinical data,
  • Allow the diagnosis of mental disorders at the pre-symptomatic stage or early during development,
  • Better measure disease progression.
  • Develop transfer technologies for fMRI-NF through EEG and serious games.

BRAINTRAIN is innovative in the development of new real-time imaging technologies e.g. new sequences, image reconstruction methods and data analysis software. This will also be the first clinical testing of fMRI-NF in a set of disorders with extraordinary socioeconomic and public health impact.

The project started in November 2013 and will last four years. It is coordinated by Cardiff University (Professor David Linden, Wales, UK).

BRAINTRAIN is a European research network (Collaborative Project) supported by the European Commission under the Health Cooperation Work Programme of the 7th Framework Programme, under the Grant Agreement n°602186.

Visit Site

, , , , , , ,

Leave a comment

[Abstract] Digital mirror box: An interactive hand-motor BMI rehabilitation tool for stroke patients


We develop a brain-machine interface for the hand-motor rehabilitation of stroke patients. The interface provides both visual and proprioceptive feedback to the user based upon the successful generation of cortical motor commands. We discuss the details of the proposed system and provide a summary of the preliminary experiment. The experiment investigates the importance of simultaneous visual and proprioceptive feedback to the delivery of motor commands from the affected motor cortex of the patients. We also discuss a case study involving a chronic stroke patient who trained with the system for 14 days to recover functional movement in the hand. The results obtained by this study suggest that the developed system is effective at accelerating the recovery of motor function in stroke patients with hand paralysis.

Date of Conference: 13-16 Dec. 2016

Date Added to IEEE Xplore: 19 January 2017

ISBN Information:

Electronic ISBN: 978-9-8814-7682-1

Print on Demand(PoD) ISBN: 978-1-5090-2401-8


References Cited:


Publisher: IEEE

Related Articles

Source: Digital mirror box: An interactive hand-motor BMI rehabilitation tool for stroke patients – IEEE Xplore Document

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

Leave a comment

[Abstract] Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient


Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the user’s movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies. Also, the Feedback Error Learning controller implemented in this system could adjust the required FES intensity to perform the task.

I. Introduction

Stroke is a leading cause of adult disability around the world. A large number of stroke survivors are left with a unilateral arm or leg paralysis. After completing conventional rehabilitation therapy, a significant number of stroke survivors are left with limited reaching and grasping capabilities [1].

Source: Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient – IEEE Xplore Document

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

Leave a comment

[Abstract] Fatigue detection and estimation using auto-regression analysis in EEG


Estimation of fatigue is a required criteria in the field of physiology. The estimation of muscle fatigue and its development in the brain signals can provide a level of endurance among athletes and limits of a persons in doing physical tasks. In this paper a technique for detecting and estimating the fatigue development using regression parameters for EEG signals is discussed. The study of 14 subjects was undertaken and analysed for the fatigue development using Auto-Regression(AR) model. The behaviour of the error function obtained is analysed for the prediction of the stages and limits of muscle fatigue development.

I. Introduction

Muscle fatigue is a phenomenon associated with the muscle contraction. It is understood as the reduction in the ability of maximal force generation by the muscle with time, during its stressing, as the muscle contraction keeps on increasing. The nervous system’s limitation to generate sustainable signals and the reduction of ability of muscle fiber to contract are two major factors contributing to fatigue development [1]. Fatigue development limits the performance and capability of the individual in sports, long stretch driving conditions and in rigourous day to day activities. Hence a parameter that can estimate the fatigue levels and provide a break point for maximum fatigue can be useful for physiology and in other areas such as labour. People working under mines can be monitored for the fatigue break point and the overall productivity of such areas can be increased by proper analysis. The fatigue development in a person can be analysed via number of methods based on physiological changes. These include Electroencephalogram (EEG), Elec-tromyography(EMG), and Heart Rate Variability(HRV). Zadry [2] reported the increase in alpha band power level of EEG with time for fatigue development [3]. Ali also reported increase in RMS values of different bands in EEG [4]. Few studies measure brain activity in light repetitive task using EEG [5] to measure drowsiness or fatigue on drivers [6] [7] and night work [8] [9]. The EEG analysis for overall fatigue has been the focus of research, but research for specific muscle fatigue detection has been limited. The EEG based detection of fatigue has the advantage of quantitative based assessment. But, for real time application perspective faster computational power and signal processing methods are required. One of the challenges based on EEG based approach is the disturbances and contamination of the signal from eyes blinking action, muscle noise by movements and instrumental noises like line noise, electronic interferences [10]. Another problem is imposed by the inter-variability and intra-variability in EEG dynamics accompanying loss of alertness [11].

Source: Fatigue detection and estimation using auto-regression analysis in EEG – IEEE Xplore Document

, , , , , , , , ,

Leave a comment

[Abstract] Technical validation of an integrated robotic hand rehabilitation device: Finger independent movement, EMG control, and EEG-based biofeedback


The objective of this work was to design and experiment a robotic hand rehabilitation device integrated with a wireless EEG system, going towards patient active participation maximization during the exercise. This has been done through i) hand movement actively triggered by patients muscular activity as revealed by electromyographic signals (i.e., a target hand movement for the rehabilitation session is defined, the patient is required to start the movement and only when the muscular activity overcomes a predefined threshold, the patient-initiated movement is supported); ii) an EEG-based biofeedback implemented to make the user aware of his/her level of engagement (i.e., brain rhythms power ratio Beta/Alpha). The designed system is composed by the Gloreha hand rehabilitation glove, a device for electromyographic signals recording, and a wireless EEG headset. A strong multidisciplinary approach was the base to reach this goal, which is the fruitful background of the Think and Go project. Within this project, research institutes (Politecnico di Milano), clinical centers (INRCA-IRCCS), and companies (ab medica s.p.a., Idrogent, SXT) have worked together throughout the development of the integrated robotic hand rehabilitation device. The integrated device has been tested on a small pilot group of healthy volunteers. All the users were able to calibrate and correctly use the system, and they reported that the system was more challenging to be used with respect to the standard passive hand mobilization session, and required more attention and involvement. The results obtained during the preliminary tests are encouraging, and demonstrate the feasibility of the proposed approach.

Source: Technical validation of an integrated robotic hand rehabilitation device: Finger independent movement, EMG control, and EEG-based biofeedback – IEEE Xplore Document

, , , , , , , , , , ,

Leave a comment

[Abstract] Detecting voluntary gait intention of chronic stroke patients towards top-down gait rehabilitation using EEG


One of the recent trends in gait rehabilitation is to incorporate bio-signals, such as electromyography (EMG) or electroencephalography (EEG), for facilitating neuroplasticity, i.e. top-down approach. In this study, we investigated decoding stroke patients’ gait intention through a wireless EEG system. To overcome patient-specific EEG patterns due to impaired cerebral cortices, common spatial patterns (CSP) was employed. We demonstrated that CSP filter can be used to maximize the EEG signal variance-ratio of gait and standing conditions. Finally, linear discriminant analysis (LDA) classification was conducted, whereby the average accuracy of 73.2% and the average delay of 0.13 s were achieved for 3 chronic stroke patients. Additionally, we also found out that the inverse CSP matrix topography of stroke patients’ EEG showed good agreement with the patients’ paretic side.

Source: IEEE Xplore Document – Detecting voluntary gait intention of chronic stroke patients towards top-down gait rehabilitation using EEG

, , , , , , , , , , ,

Leave a comment

[Abstract] Effects of action observation therapy on hand dexterity and EEG-based cortical activation patterns in patients with post-stroke hemiparesis.


Background: Previous reports have suggested that action observation training (AOT) is beneficial in enhancing the early learning of new motor tasks; however, EEG-based investigation has received little attention for AOT.
Objective: The purpose of this study was to illustrate the effects of AOT on hand dexterity and cortical activation in patients with post-stroke hemiparesis.
Method: Twenty patients with post-stroke hemiparesis were randomly divided into either the experimental group (EG) or control group (CG), with 10 patients in each group. Prior to the execution of motor tasks (carrying wooden blocks from one box to another), subjects in the EG and CG observed a video clip displaying the execution of the same motor task and pictures showing landscapes, respectively. Outcome measures included the box and block test (BBT) to evaluate hand dexterity and EEG-based brain mapping to detect changes in cortical activation.
Results: The BBT scores (EG: 20.50 ± 6.62 at pre-test and 24.40 ± 5.42 at post-test; CG: 20.20 ± 6.12 at pre-test and 20.60 ± 7.17 at post-test) revealed significant main effects for the time and group and significant time-by-group interactions (p < 0.05). For the subjects in the EG, topographical representations obtained with the EEG-based brain mapping system were different in each session of the AOT and remarkable changes occurred from the 2nd session of AOT. Furthermore, the middle frontal gyrus was less active at post-test than at pre-test.
Conclusions: These findings support that AOT may be beneficial in altering cortical activation patterns and hand dexterity.


Source: Effects of action observation therapy on hand dexterity and EEG-based cortical activation patterns in patients with post-stroke hemiparesis – Topics in Stroke Rehabilitation –

, , , , , , , , , ,

Leave a comment

%d bloggers like this: