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Feature Extraction and Classification in Brain-Computer Interfacing: Future Research Issues and Challenges

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Natural Computing for Unsupervised Learning

Part of the book series: Unsupervised and Semi-Supervised Learning ((UNSESUL))

Abstract

Brain-computer interfacing (BCI) is a communication bridge between human brain and computer. BCI system consisted of four sections (signal acquisition, signal processing, feature extraction and classifications, application Interface). In this survey paper, we try to elaborate the entire structure of BCI process especially emphasizing on feature extraction and classification area. We have briefly described different types of brain signals and their properties. For the stationary type of signal, we have used autoregressor and Fourier transform, and for nonstationary signal, we have used wavelet transformation as feature extraction policy. There have been various techniques introduced for EEG signal classification in the literature from low-cost methods (LDA, logistic regression, KNN) to computationally expensive techniques (SVM, artificial neural networks). We have also discussed ensemble and complex classifiers. In this paper, we have explained the basic concepts of all the classifiers and describe their key properties and applications. We have thoroughly analyzed all possible types of comparisons between classifiers using statistical plotting (bar chart, line chart) so that future researchers can identify the suitable classifier for a specific task. Finally, this paper deals with the various open challenges and future research issues with respect to feature extraction and classification in BCI system.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Institute of Engineering and Management, Kolkata, India

    Debashis Das Chakladar

  2. Department of Information Technology, Techno India, Kolkata, India

    Sanjay Chakraborty

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  1. Debashis Das Chakladar
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  1. Department of Information Sciences and Technology, Northeast Normal University, Changchun, Jilin, China

    Xiangtao Li

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Ka-Chun Wong

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Chakladar, D.D., Chakraborty, S. (2019). Feature Extraction and Classification in Brain-Computer Interfacing: Future Research Issues and Challenges. In: Li, X., Wong, KC. (eds) Natural Computing for Unsupervised Learning. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-98566-4_5

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