Computerized Multidomain EEG Classification System: A New Paradigm

Xiaojun Yu, Muhammad Zulkifal Aziz, Muhammad Tariq Sadiq, Ke Jia, Zeming Fan, Gaoxi Xiao

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)3626 - 3637
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Issue number8
Publication statusPublished - 14 Feb 2022


  • Computer-aided diagnosis
  • biomedical signal processing
  • electroence- phalography
  • multi-domain EEG classification


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