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Machine Learning Methods as a Test Bed for EEG Analysis in BCI Paradigms
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Author(s): Kusuma Mohanchandra (Dayananda Sagar College of Engineering, India)and Snehanshu Saha (PESIT-South, India)
Copyright: 2017
Pages: 21
Source title:
Handbook of Research on Applied Cybernetics and Systems Science
Source Author(s)/Editor(s): Snehanshu Saha (PESIT South Campus, India), Abhyuday Mandal (University of Georgia, USA), Anand Narasimhamurthy (BITS Hyderabad, India), Sarasvathi V (PESIT- Bangalore South Campus, India)and Shivappa Sangam (UGC, India)
DOI: 10.4018/978-1-5225-2498-4.ch008
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Abstract
Machine learning techniques, is a crucial tool to build analytical models in EEG data analysis. These models are an excellent choice for analyzing the high variability in EEG signals. The advancement in EEG-based Brain-Computer Interfaces (BCI) demands advanced processing tools and algorithms for exploration of EEG signals. In the context of the EEG-based BCI for speech communication, few classification and clustering techniques is presented in this book chapter. A broad perspective of the techniques and implementation of the weighted k-Nearest Neighbor (k-NN), Support vector machine (SVM), Decision Tree (DT) and Random Forest (RF) is explained and their usage in EEG signal analysis is mentioned. We suggest that these machine learning techniques provides not only potentially valuable control mechanism for BCI but also a deeper understanding of neuropathological mechanisms underlying the brain in ways that are not possible by conventional linear analysis.
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