Optimal Channel Selection Using Correlation Coefficient for CSP Based EEG Classification

In this paper, we present an optimal channel selection method to improve common spatial pattern (CSP) related features for motor imagery (MI) classification. In contrast to existing channel selection methods, in which channels significantly contributing to the classification in terms of the signal power are selected, distinctive channels in terms of correlation coefficient values are selected in the proposed method. The distinctiveness of a channel is quantified by the number of channels with which it yields large difference in correlation coefficient values for binary motor imagery (MI) tasks, rather than by the largeness of the difference itself. For each distinctive channel, a group of channels is formed by gathering strongly correlated channels and the Fisher score is computed using the feature output, based on the filter-bank CSP (FBCSP) exclusively applied to the channel group. Finally, the channel group with the highest Fisher score is chosen as the selected channels. The proposed method selects the fewest channels on average and outperforms existing channel selection approaches. The simulation results confirm performance improvement for two publicly available BCI datasets, BCI competition III dataset IVa and BCI competition IV dataset I, in comparison with existing methods.

[1]  Qin Tang,et al.  L1-Norm-Based Common Spatial Patterns , 2012, IEEE Transactions on Biomedical Engineering.

[2]  Martin Krzywinski,et al.  Significance, P values and t-tests , 2013, Nature Methods.

[3]  Fakhreddine Ghaffari,et al.  An embedded implementation based on adaptive filter bank for brain–computer interface systems , 2018, Journal of Neuroscience Methods.

[4]  Wonzoo Chung,et al.  Selective Feature Generation Method Based on Time Domain Parameters and Correlation Coefficients for Filter-Bank-CSP BCI Systems , 2019, Sensors.

[5]  Liqing Zhang,et al.  A Boosting-Based Spatial-Spectral Model for Stroke Patients’ EEG Analysis in Rehabilitation Training , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[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]  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).

[8]  Mahyar Hamedi,et al.  Electroencephalographic Motor Imagery Brain Connectivity Analysis for BCI: A Review , 2016, Neural Computation.

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

[10]  Xingyu Wang,et al.  Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface , 2015, Journal of Neuroscience Methods.

[11]  Roberto Battiti,et al.  Using mutual information for selecting features in supervised neural net learning , 1994, IEEE Trans. Neural Networks.

[12]  Xingyu Wang,et al.  Temporally Constrained Sparse Group Spatial Patterns for Motor Imagery BCI , 2019, IEEE Transactions on Cybernetics.

[13]  Klaus-Robert Müller,et al.  The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects , 2007, NeuroImage.

[14]  Wei Jiang,et al.  A New Motor Imagery EEG Classification Method FB-TRCSP+RF Based on CSP and Random Forest , 2018, IEEE Access.

[15]  Klaus-Robert Müller,et al.  The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects , 2008, IEEE Transactions on Biomedical Engineering.

[16]  Feiping Nie,et al.  Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization , 2010, NIPS.

[17]  Isabelle Bloch,et al.  Subject-specific channel selection for classification of motor imagery electroencephalographic data , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[18]  Xingyu Wang,et al.  An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System , 2019, Comput. Intell. Neurosci..

[19]  Nicole Krämer,et al.  Time Domain Parameters as a feature for EEG-based Brain-Computer Interfaces , 2009, Neural Networks.

[20]  Wonzoo Chung,et al.  Frequency-Optimized Local Region Common Spatial Pattern Approach for Motor Imagery Classification , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[21]  J. Q. Gan,et al.  Multiresolution analysis over simple graphs for brain computer interfaces , 2013, Journal of neural engineering.

[22]  Cuntai Guan,et al.  Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI , 2011, IEEE Transactions on Biomedical Engineering.

[23]  Andrzej Cichocki,et al.  Correlation-based channel selection and regularized feature optimization for MI-based BCI , 2019, Neural Networks.

[24]  David Lee,et al.  Filter Bank Regularized Common Spatial Pattern Ensemble for Small Sample Motor Imagery Classification , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[25]  Laura Astolfi,et al.  Changes in EEG Power Spectral Density and Cortical Connectivity in Healthy and Tetraplegic Patients during a Motor Imagery Task , 2009, Comput. Intell. Neurosci..

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

[27]  Yu Zhang,et al.  Sparse Group Representation Model for Motor Imagery EEG Classification , 2019, IEEE Journal of Biomedical and Health Informatics.

[28]  Sang-Hoon Park,et al.  Small Sample Setting and Frequency Band Selection Problem Solving Using Subband Regularized Common Spatial Pattern , 2017, IEEE Sensors Journal.

[29]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[30]  Jieping Ye,et al.  SVM versus Least Squares SVM , 2007, AISTATS.

[31]  Brendan Z. Allison,et al.  Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction , 2013 .

[32]  Jin Zhou,et al.  A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking , 2018, Front. Neurosci..

[33]  K. Lafleur,et al.  Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface , 2013, Journal of neural engineering.

[34]  P. Rapp,et al.  Time domain measures of inter-channel EEG correlations: a comparison of linear, nonparametric and nonlinear measures , 2013, Cognitive Neurodynamics.