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Publication . Part of book or chapter of book . 2021

Classification of VEP-Based EEG Signals Using Time and Time-Frequency Domain Features

M. Bhuvaneshwari; E. Grace Mary Kanaga;
Closed Access
Published: 01 Jan 2021
Publisher: Springer Singapore
Abstract
The Evoked Potential (EP) is a term that refers to the response generated by the brain in effect to the external stimuli. The response is measured by the strength of the electric potential generated by the brain. The measured response varies depends upon the flickering speed of the stimuli through which the stimuli can be identified. In this study, the activity of the brain while perceiving the visual stimuli of varying frequency has been investigated. The study was made on the Electroencephalography (EEG) dataset designed by the authors that were acquired from the healthy seven subjects whose mean age is 22. The acquired is transformed to the time-frequency domain by applying wavelet transforms, and the statistical features were extracted from the acquired and transformed EEG signals for classification The proposed study applied machine learning algorithms to classify the appropriate stimuli. The study has experimented with machine learning algorithms like Support Vector Machines (SVM), random forest, K-nearest neighbour, multi-layer perceptron, linear discriminant analysis with the accuracy of 93.14%, 97.85%, 71.08%, 79.65%, 81.46% and 94.09%, 99.06%, 90.02%, 85.16%, 82.91% in time and time-frequency domain, respectively.
Subjects by Vocabulary

Microsoft Academic Graph classification: Pattern recognition Electroencephalography medicine.diagnostic_test medicine Linear discriminant analysis Artificial intelligence business.industry business Computer science Perceptron Wavelet transform Random forest Support vector machine Evoked potential Time domain

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Neuroinformatics
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https://doi.org/10.1007/978-98...
Part of book or chapter of book . 2021
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