Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks
Abstract
The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of motor imagery EEG types with high accuracy, a classification methodology using the wavelet packet decomposition (WPD) and the proposed deep residual convolutional networks (DRes-CNN) is proposed. Firstly, EEG waveforms are segmented into sub-signals. Then the EEG signal...
Paper Details
Title
Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks
Published Date
Nov 17, 2021
Journal
Volume
15
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