MLAR-Net: A Multilevel Attention-Based ResNet Module for the Automated Recognition of Emotions Using Single-Channel EEG Signals

• Published in: IEEE access Vol. 13; pp. 99122 - 99144
• Main Authors: Maithri, M., Raghavendra, U., Gudigar, Anjan, Kumar Praharaj, Samir, Sriram, Karthikeyan, Salvi, Massimo, Hong Yeong, Chai, Molinari, Filippo, Rajendra Acharya, U.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; attention-based ResNet; Availability; Biomedical imaging; Brain; Brain modeling; Channels; Computational modeling; Datasets; Electrodes; Electroencephalography; Emotion recognition; Emotions; explainable AI; Feature extraction; Modules; Multilevel; Real time; Real-time systems; single-channel EEG; Spectrogram; Spectrograms; Support vector machines

Human emotion recognition is important as it finds applications in multiple domains such as medicine, entertainment, and military. However, accurately identifying emotions remains challenging due to humans' ability to hide or suppress their emotional expressions. Hence it becomes important to r...