Original paper
Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification
Abstract
Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art approaches face the issue of spatially unstable structure of highly noised EEG signals. To address this problem, this paper presents a multi-channel convolutional neural network architecture with adaptively optimized parameters. Our solution outperforms alternative methods in terms of classification...
Paper Details
Title
Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification
Published Date
Oct 14, 2018
Journal
Volume
18
Issue
10
Pages
3451 - 3451
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