An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information

• Published in: BMC bioinformatics Vol. 18; no. Suppl 16; p. 545
• Main Authors: Kumar, Shiu, Sharma, Alok, Tsunoda, Tatsuhiko
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 28-12-2017; BioMed Central; BMC
• Subjects: Banks (Finance); Brain computer interface; Common spatial pattern; Electroencephalography; Electroencephalography - classification; Electroencephalography - methods; Frequency band; Humans; Methods; Motor imagery; Mutual information; Signal Processing, Computer-Assisted - instrumentation; Brain computer interface; Electroencephalography; Motor imagery; Common spatial pattern; Frequency band; Mutual information
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-017-1964-6

Common spatial pattern (CSP) has been an effective technique for feature extraction in electroencephalography (EEG) based brain computer interfaces (BCIs). However, motor imagery EEG signal feature extraction using CSP generally depends on the selection of the frequency bands to a great extent.
In this study, we propose a mutual information based frequency band selection approach. The idea of the proposed method is to utilize the information from all the available channels for effectively selecting the most discriminative filter banks. CSP features are extracted from multiple overlapping sub-bands. An additional sub-band has been introduced that cover the wide frequency band (7-30 Hz) and two different types of features are extracted using CSP and common spatio-spectral pattern techniques, respectively. Mutual information is then computed from the extracted features of each of these bands and the top filter banks are selected for further processing. Linear discriminant analysis is applied to the features extracted from each of the filter banks. The scores are fused together, and classification is done using support vector machine.
The proposed method is evaluated using BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, and it outperformed all other competing methods achieving the lowest misclassification rate and the highest kappa coefficient on all three datasets.
Introducing a wide sub-band and using mutual information for selecting the most discriminative sub-bands, the proposed method shows improvement in motor imagery EEG signal classification.