Study of Machine Learning Techniques for EEG Eye State Detection

• Main Authors: Francisco Laport, Paula M. Castro, Adriana Dapena, Francisco J. Vazquez-Araujo, Daniel Iglesia
• Format: Article
• Language: English
• Published: MDPI AG 2020-08-01
• Series: Proceedings
• Subjects: Discrete Fourier Transform; Discrete Wavelet Transform; Linear Discriminant Analysis; Support Vector Machine; Independent Component Analysis
• Online Access: https://www.mdpi.com/2504-3900/54/1/53

A comparison of different machine learning techniques for eye state identification through Electroencephalography (EEG) signals is presented in this paper. (1) Background: We extend our previous work by studying several techniques for the extraction of the features corresponding to the mental states of open and closed eyes and their subsequent classification; (2) Methods: A prototype developed by the authors is used to capture the brain signals. We consider the Discrete Fourier Transform (DFT) and the Discrete Wavelet Transform (DWT) for feature extraction; Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) for state classification; and Independent Component Analysis (ICA) for preprocessing the data; (3) Results: The results obtained from some subjects show the good performance of the proposed methods; and (4) Conclusion: The combination of several techniques allows us to obtain a high accuracy of eye identification.