EEG channel correlation based model for emotion recognition

Article

Islam, Md Rabiul, Islam, Md Milon, Rahman, Md Mustafizur, Mondal, Chayan, Singha, Suvojit Kumar, Ahmad, Mohiuddin, Awal, Abdul, Islam, Md Saiful and Moni, Mohammad Ali. 2021. "EEG channel correlation based model for emotion recognition." Computers in Biology and Medicine. 136. https://doi.org/10.1016/j.compbiomed.2021.104757
Article Title

EEG channel correlation based model for emotion recognition

ERA Journal ID5040
Article CategoryArticle
AuthorsIslam, Md Rabiul, Islam, Md Milon, Rahman, Md Mustafizur, Mondal, Chayan, Singha, Suvojit Kumar, Ahmad, Mohiuddin, Awal, Abdul, Islam, Md Saiful and Moni, Mohammad Ali
Journal TitleComputers in Biology and Medicine
Journal Citation136
Article Number104757
Number of Pages11
Year2021
PublisherElsevier
Place of PublicationUnited Kingdom
ISSN0010-4825
1879-0534
Digital Object Identifier (DOI)https://doi.org/10.1016/j.compbiomed.2021.104757
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0010482521005515
Abstract

Emotion recognition using Artificial Intelligence (AI) is a fundamental prerequisite to improve Human-Computer Interaction (HCI). Recognizing emotion from Electroencephalogram (EEG) has been globally accepted in many applications such as intelligent thinking, decision-making, social communication, feeling detection, affective computing, etc. Nevertheless, due to having too low amplitude variation related to time on EEG signal, the proper recognition of emotion from this signal has become too challenging. Usually, considerable effort is required to identify the proper feature or feature set for an effective feature-based emotion recognition system. To extenuate the manual human effort of feature extraction, we proposed a deep machine-learning-based model with Convolutional Neural Network (CNN). At first, the one-dimensional EEG data were converted to Pearson's Correlation Coefficient (PCC) featured images of channel correlation of EEG sub-bands. Then the images were fed into the CNN model to recognize emotion. Two protocols were conducted, namely, protocol-1 to identify two levels and protocol-2 to recognize three levels of valence and arousal that demonstrate emotion. We investigated that only the upper triangular portion of the PCC featured images reduced the computational complexity and size of memory without hampering the model accuracy. The maximum accuracy of 78.22% on valence and 74.92% on arousal were obtained using the internationally authorized DEAP dataset.

KeywordsEmotion; Convolutional neural network ; Feature extraction ; EEG; Pearson’s correlation coefficient ; Complexity
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020461103. Deep learning
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Byline AffiliationsBangladesh Army University of Engineering & Technology, Bangladesh
Khulna University of Engineering and Technology, Bangladesh
Jashore University of Science and Technology, Bangladesh
Khulna University, Bangladesh
Griffith University
University of Queensland
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