Comparison of designs towards a subject-independent brain-computer interface based on motor imagery

A major limitation of current Brain-Computer Interfaces (BCI) based on Motor Imagery (MI) is that they are subject-specific BCI, which require data recording and system training for each new user. This process is time consuming and inconvenient, especially for casual users or portable BCI with limited computational resources. In this paper, we explore the design of a Subject-Independent (SI) MI-based BCI, i.e., a BCI that can be used immediately by any new user without training the BCI with the user’s data. This is achieved by training the BCI on data acquired from several other subjects. In order to assess the possibility to build such a BCI, we compared several designs based on different features and classifiers, on data from 9 subjects. Our results suggested that linear classifiers were the most appropriate for the design of MI-based SI-BCI. We also proposed a filter bank common spatial patterns feature extraction method based on a multi-resolution frequency decomposition which achieved the highest accuracy.

[1]  N. Birbaumer.  Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. , 2006, Psychophysiology.

[2]  Reinhold Scherer,et al.  A fully on-line adaptive BCI , 2006, IEEE Transactions on Biomedical Engineering.

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

[4]  G Pfurtscheller,et al.  Seperability of four-class motor imagery data using independent components analysis , 2006, Journal of neural engineering.

[5]  David Chiu,et al.  BOOK REVIEW: "PATTERN CLASSIFICATION", R. O. DUDA, P. E. HART and D. G. STORK, Second Edition , 2001 .

[6]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[7]  Anatole Lécuyer,et al.  FuRIA: An Inverse Solution Based Feature Extraction Algorithm Using Fuzzy Set Theory for Brain–Computer Interfaces , 2009, IEEE Transactions on Signal Processing.

[8]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[9]  Cuntai Guan,et al.  Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[10]  L. Fabien,et al.  Studying the Use of Fuzzy Inference Systems for Motor Imagery Classification , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[12]  Cuntai Guan,et al.  Unsupervised brain computer interface based on inter-subject information , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  Michitaka Hirose,et al.  Brain-Computer Interfaces, Virtual Reality, and Videogames , 2008, Computer.

[14]  Anatole Lécuyer,et al.  Classifying EEG for brain computer interfaces using Gaussian processes , 2008, Pattern Recognit. Lett..

[15]  Rabab K Ward,et al.  A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals , 2007, Journal of neural engineering.