In the domain of neuroscience, electroencephalography (EEG) holds a pivotal role in determining the inner workings of the human brain, offering real-time insights into cognitive processes, emotions, and neurological disorders. While numerous EEG channels are available in a typical EEG brain-computer interface, selecting the optimal subset for emotion classification poses a significant challenge. Conventional channel selection methods overlook the biological relevance of specific brain lobes in emotional processing, leading to a lack of contextual specificity. This paper introduces a novel approach, by using a biologically informed channel selection approach in the EEG signals. The brain is segmented into various groups and sub-groups and the ability of the channels associated with those groups is determined using time and frequency domain features. The ability of each of these groups and sub-groups to attain higher performance is determined through the accuracy outcomes driven by the support vector machines (SVM). The ability of the selected channels in making accurate classification has been determined using a deep learning model in determining valence and arousal classes, and making a comparison with the selected channels-led classification methods. The approach is validated using the DEAP dataset, demonstrating its potential to enhance EEG-based emotion classification accuracy and efficiency. This innovative methodology offers a promising avenue for future EEG research, allowing customization based on the specific emotions under study, psychological intervention, and streamlining the setup process while maintaining the highest levels of accuracy, reaching an average of 95.7% for intra-subject and 94.65% for cross-subject emotion classification.

Original languageEnglish
Pages (from-to)856-868
Number of pages13
JournalInternational Journal of Intelligent Engineering and Systems
Issue number1
Publication statusPublished - 2024


  • Brain area
  • Channel selection
  • Cross-subject
  • EEG
  • Emotion recognition
  • Weighting


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