Brain-Computer Interfaces for stroke rehabilitation: Evaluation of feedback and classification strategies in healthy users

A Brain-Computer Interface (BCI) is a tool for reading and interpreting signals that are derived from the user's brain, for example using Electroencephalography (EEG) to record signals from the user's scalp. Based on these signals, applications and external devices can be controlled. In the last decades a variety of different BCIs for communication and control applications were developed. A quite new and promising idea is to utilize BCIs as a tool for stroke rehabilitation. The BCI detects the intention to move and provides online feedback to the user, who is therefore able to train the correct motor control of the affected parts of the body. The aim of this publication is to optimize current BCI-strategies for stroke rehabilitation. Therefore a new method of providing immersive feedback via a 3-D virtual reality environment is evaluated. The second crucial aspect is to gain higher classification accuracy of the BCI. In the past years, in terms of signal processing huge improvements have already been done. Besides this, the latest development in EEG-hardware allows using a higher number of EEG-electrodes compared to former years. Hence, we also tested the influence of an increased number of EEG-electrodes.

[1]  K.-R. Muller,et al.  Optimizing Spatial filters for Robust EEG Single-Trial Analysis , 2008, IEEE Signal Processing Magazine.

[2]  G. Pfurtscheller,et al.  How many people are able to operate an EEG-based brain-computer interface (BCI)? , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[4]  Z J Koles,et al.  The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG. , 1991, Electroencephalography and clinical neurophysiology.

[5]  G. Pfurtscheller,et al.  Motor imagery and action observation: Modulation of sensorimotor brain rhythms during mental control of a brain–computer interface , 2009, Clinical Neurophysiology.

[6]  M. Nuttin,et al.  A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots , 2008, Clinical Neurophysiology.

[7]  Robert Leeb,et al.  Towards natural non-invasive hand neuroprostheses for daily living , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[8]  J. Ushiba,et al.  Effects of neurofeedback training with an electroencephalogram-based brain-computer interface for hand paralysis in patients with chronic stroke: a preliminary case series study. , 2011, Journal of rehabilitation medicine.

[9]  Xuedong Chen,et al.  Decoding human motor activity from EEG single trials for a discrete two-dimensional cursor control , 2009, Journal of neural engineering.

[10]  E. Sellers,et al.  How many people are able to control a P300-based brain–computer interface (BCI)? , 2009, Neuroscience Letters.

[11]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[12]  Cuntai Guan,et al.  A clinical study of motor imagery-based brain-computer interface for upper limb robotic rehabilitation , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  Christa Neuper,et al.  Neurofeedback Training for BCI Control , 2009 .

[14]  G. Pfurtscheller,et al.  Designing optimal spatial filters for single-trial EEG classification in a movement task , 1999, Clinical Neurophysiology.

[15]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[16]  Moritz Grosse-Wentrup,et al.  Using brain–computer interfaces to induce neural plasticity and restore function , 2011, Journal of neural engineering.