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Classification Algorithms for EEG-Based Brain-Computer Interface: A Review
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Author(s): Sravanth Kumar Ramakuri (VNR Vignana Jyothi Institute of Engineering and Technology, India), Chinmay Chakraboirty (Birla Institute of Technology Mesra, India), Anudeep Peddi (VNR Vignana Jyothi Institute of Engineering and Technology, India)and Bharat Gupta (National Institute of Technology Patna, India)
Copyright: 2019
Pages: 22
Source title:
Advanced Classification Techniques for Healthcare Analysis
Source Author(s)/Editor(s): Chinmay Chakraborty (Birla Institute of Technology Mesra, India)
DOI: 10.4018/978-1-5225-7796-6.ch003
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Abstract
In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.
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