Selected channel based multiclass emotion classification from wearable human brain EEG signal
Year: 2025
Type: article
Abstract: Emotion recognition is a crucial issue in human-computer interaction, and EEG (electroencephalography) plays a significant role in deciphering human emotions based on physiological data. However, the complex and non-stationary nature of EEG signals, coupled with redundant information from multi-channel recordings, poses challenges in accurate emotion classification. To address this, we propose a hybrid 1DCNN-Bi-LSTM model that integrates spatial feature extraction (1DCNN) with temporal dependency learning (Bi-LSTM), enhancing the robustness of emotion classification. Furthermore, we present a channel selection mechanism to find the most pertinent EEG channels for emotion recognition, hence lowering computing complexity without compromising accuracy. With the chosen-channel model (8 channels) attaining 85.16 % accuracy, a notable improvement over standard full-channel approaches, experimental results on the DEAP dataset show that the suggested methodology provides significant performance gains. This work fits wearable devices and real-time affective computing systems since it offers a scalable and effective method for EEG-based emotion recognition. • Propose a novel approach for multi-channel EEG emotion recognition based on a hybrid 1DCNN-Bi-LSTM model. • The model tackles challenges in wearable EEG emotion identification, particularly unpredictable signal patterns caused by intricate neural processes. • Handle the challenge posed by irrelevant EEG channels accuracy.
Source: Measurement Sensors
Institution: Maulana Azad National Institute of Technology
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,910
OpenAlex ID: W4408975519