Combining Spatial Filters for the Classification of Single-Trial EEG in a Finger Movement Task

Brain-computer interface (BCI) is to provide a communication channel that translates human intention reflected by a brain signal such as electroencephalogram (EEG) into a control signal for an output device. In recent years, the event-related desynchronization (ERD) and movement-related potentials (MRPs) are utilized as important features in motor related BCI system, and the common spatial patterns (CSP) algorithm has shown to be very useful for ERD-based classification. However, as MRPs are slow nonoscillatory EEG potential shifts, CSP is not an appropriate approach for MRPs-based classification. Here, another spatial filtering algorithm, discriminative spatial patterns (DSP), is newly introduced for better extraction of the difference in the amplitudes of MRPs, and it is integrated with CSP to extract the features from the EEG signals recorded during voluntary left versus right finger movement tasks. A support vector machines (SVM) based framework is designed as the classifier for the features. The results show that, for MRPs and ERD features, the combined spatial filters can realize the single-trial EEG classification better than anyone of DSP and CSP alone does. Thus, we propose an EEG-based BCI system with the two feature sets, one based on CSP (ERD) and the other based on DSP (MRPs), classified by SVM

[1]  Klaus-Robert Müller,et al.  The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials , 2004, IEEE Transactions on Biomedical Engineering.

[2]  Touradj Ebrahimi,et al.  Spatial filters for the classification of event-related potentials , 2006, ESANN.

[3]  William Z Rymer,et al.  Brain-computer interface technology: a review of the Second International Meeting. , 2003, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[4]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[5]  Robert Oostenveld,et al.  A comparative study of different references for EEG spectral mapping: the issue of the neutral reference and the use of the infinity reference , 2005, Physiological measurement.

[6]  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.

[7]  Mehrdad Fatourechi,et al.  Evaluating the Performance of Self-Paced Brain-Computer Interface Technology , 2006 .

[8]  Klaus-Robert Müller,et al.  Classifying Single Trial EEG: Towards Brain Computer Interfacing , 2001, NIPS.

[9]  D. Tucker Spatial sampling of head electrical fields: the geodesic sensor net. , 1993, Electroencephalography and clinical neurophysiology.

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

[11]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[12]  K.-R. Muller,et al.  The Berlin brain-computer interface: EEG-based communication without subject training , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[13]  M Kukleta,et al.  Steep early negative slopes can be demonstrated in pre-movement bereitschaftspotential , 2001, Clinical Neurophysiology.

[14]  K.-R. Muller,et al.  Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  M. Laubach,et al.  Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task , 2022 .

[16]  Klaus-Robert Müller,et al.  Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms , 2004, IEEE Transactions on Biomedical Engineering.

[17]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

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

[19]  Dawn M. Taylor,et al.  Direct Cortical Control of 3D Neuroprosthetic Devices , 2002, Science.

[20]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[21]  B. Allison,et al.  The effects of self-movement, observation, and imagination on mu rhythms and readiness potentials (RP's): toward a brain-computer interface (BCI). , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[22]  Gilles Blanchard,et al.  BCI competition 2003-data set IIa: spatial patterns of self-controlled brain rhythm modulations , 2004, IEEE Transactions on Biomedical Engineering.

[23]  K.-R. Muller,et al.  Linear and nonlinear methods for brain-computer interfaces , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[24]  Klaus-Robert Müller,et al.  Combining Features for BCI , 2002, NIPS.