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Introduction to Motor Imagery-Based Brain-Computer Interface: Time, Frequency, and Phase Analysis-Based Feature Extraction for Two Class MI Classification
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Author(s): Nitesh Singh Malan (Indian Institute of Technology (Banaras Hindu University), India)and Shiru Sharma (Indian Institute of Technology (Banaras Hindu University), India)
Copyright: 2020
Pages: 30
Source title:
Biomedical and Clinical Engineering for Healthcare Advancement
Source Author(s)/Editor(s): N. Sriraam (Ramaiah Institute of Technology, India)
DOI: 10.4018/978-1-7998-0326-3.ch009
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Abstract
In this chapter, motor imagery (MI) based brain-computer interface (BCI) is introduced incorporating the explanation of key components required to design a practical BCI device. Its application to the medical and nonmedical sector is discussed in detail. In the experimental study, a feature extraction method using time, frequency, and phase analysis of Motor imagery EEG is presented. For the classification of MI task, EEG signals are decomposed using a dual-tree complex wavelet transform (DTCWT) and then time, frequency, and phase features are extracted. The validation of the proposed method is conducted using BCI competition IV dataset 2b. A Support vector machine (SVM) classifier is used to perform the classification task. Performance of the proposed method is compared with the standard feature extraction methods. The proposed scheme achieved a larger average classification accuracy of 82.81% which is better than that obtained by other methods.
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