Electroencephalogram Based Motor Imagery Brain Computer Interface Using Multivariate Iterative Filtering and Spatial Filtering

In motor imagery (MI) based brain-computer interface (BCI), common spatial pattern (CSP) is most popularly used for discriminant feature extraction. However, the performance of CSP depends on the operational frequency bands, which are selected manually or set to a broad frequency range in most of th...

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Bibliographic Details
Published in:IEEE transactions on cognitive and developmental systems 2023-09, Vol.15 (3), p.1-1
Main Authors: Das, Kritiprasanna, Pachori, Ram Bilas
Format: Article
Language:English
Subjects:
Brain computer interface
Classification
common spatial pattern
Discriminant analysis
EEG
Electrodes
Electroencephalography
Feature extraction
Filtering
Frequencies
Frequency ranges
Human-computer interface
Imagery
iterative filtering
Iterative methods
linear discriminant analysis
motor imagery
Multivariate analysis
multivariate iterative filtering
Signal processing algorithms
Signal resolution
Spatial filtering
Task analysis
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Summary:In motor imagery (MI) based brain-computer interface (BCI), common spatial pattern (CSP) is most popularly used for discriminant feature extraction. However, the performance of CSP depends on the operational frequency bands, which are selected manually or set to a broad frequency range in most of the previously developed applications. Due to subject to subject or even trial to trial variability of frequency band affected by MI task, these methods suffer from poor performance. We have proposed a novel approach, using combination of multivariate iterative filtering (MIF) and CSP (MIFCSP), to automatically select optimal frequency bands based on MIF which can be further used for discriminant feature extraction. MIF decomposes the signal into several multivariate intrinsic mode functions, from which features are extracted using CSP. We select the minimum number of most significant features for which highest classification accuracy is achieved. Subsequently, linear discriminant analysis (LDA) classifier is used to classify different MI tasks. Experimental results for BCI competition IV dataset 2a and BCI competition III-IIIa are presented. For left hand versus right hand MI classification, proposed MIFCSP method provides 83.18% and 84.44% average accuracy, respectively. Superior classification performance confirms that MIFCSP is a promising candidate for MI BCI application.
ISSN:2379-8920
2379-8939
DOI:10.1109/TCDS.2022.3214081