Analysis of classification methods suitable for band limited spatially filtered EEG signal applicable to non-invasive BCI
Sanjay R. Ganorkar1, Vrushali G. Raut2

1Sanjay R. Ganorkar, Electronics and Telecommunication, Sinhgad College of Engg., Pune, India.
2Vrushali G. Raut, Electronics and Telecommunication, Sinhgad College of Engg., Pune, India.

Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1689-1694 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8170078919/19©BEIESP | DOI: 10.35940/ijitee.I8170.078919
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Abstract: FElectroencephalographic (EEG) signals are the preferred input for non-invasive Brain-Computer Interface (BCI). Efficient signal processing strategies, including feature extraction and classification, are required to distinguish the underlying task of BCI. This work proposes the optimized common spatial pattern(CSP) filtering technique as the feature extraction method for collecting the spatially spread variation of the signal. The bandpass filter (BPF) designed for this work assures the availability of event-related synchronized (ERS) and event-related desynchronized (ERD) signal as input to the spatial filter. This work takes consideration of the area-specific electrodes for feature formation. This work further proposes a comparative analysis of classifier algorithms for classification accuracy(CA), sensitivity and specificity and the considered algorithms are Support Vector Machine(SVM), Linear Discriminant Analysis(LDA), and K-Nearest Neighbor(KNN). Performance parameters considered are CA, sensitivity, and selectivity, which can judge the method not only for high CA but also inclining towards the particular class. Thus it will direct in the selection of appropriate classifier as well as tuning the classifier to get the balanced results. In this work, CA, the prior performance parameter is obtained to be 88.2% sensitivity of 94.2% and selectivity 82.2% for the cosine KNN classifier. SVM with linear kernel function also gives the comparable results, thus concluding that the robust classifiers perform well for all parameters in case of CSP for feature extraction.
keyword: Brain-Computer Interface(BCI), Electroencephalography(EEG), Common spatial pattern(CSP), event-related synchronized(ERS), event-related desynchronized (ERD)

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