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Impact of Pairwise Electrode Features in the Classification of Emotions for EEG Signal Analysis
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Author(s): M. Suchetha (Centre for Healthcare Advancement, Innovation, and Research, Vellore Institute of Technology, Chennai, India), V. V. Rama Raghavan (Vellore Institute of Technology, Chennai, India), Shaik Fardeen (Vellore Institute of Technology, Chennai, India), P. V. S. Nithish (Vellore Institute of Technology, Chennai, India), S. Preethi (Centre for Healthcare Advancement, Innovation, and Research, Vellore Institute of Technology, Chennai, India)and D. Edwin Dhas (Centre for Healthcare Advancement, Innovation, and Research, Vellore Institute of Technology, Chennai, India)
Copyright: 2024
Pages: 13
Source title:
Quantum Innovations at the Nexus of Biomedical Intelligence
Source Author(s)/Editor(s): Vishal Dutt (AVN Innovations Pvt. Ltd., India), Abhishek Kumar (Department of CSE, UIE, Chandigarh University, Punjab, India), Sachin Ahuja (Chandigarh University, India), Anupam Baliyan (Geeta University, India)and Narayan Vyas (AVN Innovations Pvt. Ltd., India)
DOI: 10.4018/979-8-3693-1479-1.ch006
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Abstract
Emotion recognition is the capacity to recognize and interpret an individual's emotional state through a variety of techniques, one of which is the detection and interpretation of patterns of brain activity linked to specific emotional states. Applications for emotion recognition are numerous and include human-computer interaction, marketing research, and mental health diagnosis. Electroencephalography (EEG) signals are another name for the patterns of brain activity. To extract features from EEG waves, many techniques have been used. The wavelet transform (WT), differential entropy (DE), statistical features (SF), and convolutional neural network (CNN) are some of the feature extraction techniques performed. This proposed method utilizes a custom CNN model to train and test on the preprocessed SEED data.
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