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Computerized Multidomain EEG Classification System: A New Paradigm
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DOI：10.1109/JBHI.2022.3151570.png)
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摘要
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The recent advancements in electroencepha- logram (EEG) signals classification largely center around the domain-specific solutions that hinder the algorithm cross-discipline adaptability. This study introduces a computer-aided broad learning EEG system (CABLES) for the classification of six distinct EEG domains under a unified sequential framework. Specifically, this paper proposes three novel modules namely, complex variational mode de- composition (CVMD), ensemble optimization-based featu- res selection (EOFS), and t-distributed stochastic neighbor embedding-based samples reduction (tSNE-SR) methods respectively for the realization of CABLES. Extensive expe- riments are carried out on seven different datasets from diverse disciplines using different variants of the neural network, extreme learning machine, and machine learning classifiers employing a 10-fold cross-validation strategy. Results compared with existing studies reveal that the highest classification accuracy of 99.1%, 97.8%, 94.3%, 91.5%, 98.9%, 95.3%, and 92% is achieved for the motor imagery dataset A, dataset B, slow cortical potentials, epilepsy, alcoholic, and schizophrenia EEG datasets res- pectively. The overall empirical analysis authenticates that the proposed CABLES framework outperforms the existing domain-specific methods in terms of classification accuracies and multirole adaptability, thus can be endorsed as an effective automated neural rehabilitation system.
Keyword:
Electroencephalography
Feature extraction
Epilepsy
Electric potential
Bioinformatics
Brain modeling
Alcoholism
Computer-aided diagnosis
electroence- phalography
biomedical signal processing
multi-domain EEG classification
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6.8
论文数:
4.6K
被引数:
2.0W
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分享收藏Motor Imagery EEG Signals Classification Based on Mode Amplitude and Frequency Components Using Empirical Wavelet Transform.png)
IEEE ACCESS
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