Joint selection of time and frequency segments for classifying multiclass EEG data in motor imagery based BCIs

The time and frequency intervals of EEG signals have crucial influence on the classification performance of motor imagery based brain-computer interfaces (BCIs). This paper proposes a novel algorithm for joint selection of time segment and frequency band for multiclass motor imagery based BCIs. A task-independent sliding window method, which does not require any prior knowledge, is used for the choice. Approximation joint diagonalization (AJD) based multiclass common spatial pattern (CSP) algorithm is utilized for feature extraction and k-nearest neighbor (KNN) is used for classification. The algorithm was tested and compared with three typical methods on a four-class motor imagery data set The experimental results suggested that based on the chosen time segment and frequency band, the proposed algorithm achieved superior performance with regard to classification accuracy.

[1]  Amit Konar,et al.  Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose , 2014, Medical & Biological Engineering & Computing.

[2]  Arie Yeredor,et al.  A fast approximate joint diagonalization algorithm using a criterion with a block diagonal weight matrix , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  R. Leeb,et al.  BCI Competition 2008 { Graz data set B , 2008 .

[4]  Jaime Gómez Gil,et al.  Brain Computer Interfaces, a Review , 2012, Sensors.

[5]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[6]  Brian Lithgow,et al.  Wavelet Common Spatial Pattern in asynchronous offline brain computer interfaces , 2011, Biomed. Signal Process. Control..

[7]  Yuhui Ma,et al.  An Unweighted Exhaustive Diagonalization Based Multi-Class Common Spatial Pattern Algorithm in Brain-Computer Interfaces , 2010, 2010 2nd International Conference on Information Engineering and Computer Science.

[8]  Klaus-Robert Müller,et al.  Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing , 2006, IEEE Transactions on Biomedical Engineering.

[9]  Francesca Bovolo,et al.  Automatic selection of frequency and time intervals for classification of EEG signals , 2007 .

[10]  J R Wolpaw,et al.  Spatial filter selection for EEG-based communication. , 1997, Electroencephalography and clinical neurophysiology.

[11]  Haixian Wang,et al.  Local Temporal Common Spatial Patterns for Robust Single-Trial EEG Classification , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  Lipo Wang.  Support vector machines : theory and applications , 2005 .

[13]  Heung-Il Suk,et al.  Subject and class specific frequency bands selection for multiclass motor imagery classification , 2011, Int. J. Imaging Syst. Technol..

[14]  Bin He,et al.  Classifying EEG-based motor imagery tasks by means of time–frequency synthesized spatial patterns , 2004, Clinical Neurophysiology.

[15]  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).

[16]  Minkyu Ahn,et al.  Journal of Neuroscience Methods , 2015 .

[17]  Moritz Grosse-Wentrup,et al.  Multiclass Common Spatial Patterns and Information Theoretic Feature Extraction , 2008, IEEE Transactions on Biomedical Engineering.

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

[19]  Cuntai Guan,et al.  Mutual information-based selection of optimal spatial-temporal patterns for single-trial EEG-based BCIs , 2012, Pattern Recognit..

[20]  Lipo Wang,et al.  On competitive learning , 1997, IEEE Trans. Neural Networks.

[21]  S. Coyle,et al.  Brain–computer interfaces: a review , 2003 .