EEG channel selection-based binary particle swarm optimization with recurrent convolutional autoencoder for emotion recognition

• Published in: Biomedical signal processing and control Vol. 84; p. 104783
• Main Authors: Kouka, Najwa, Fourati, Rahma, Fdhila, Raja, Siarry, Patrick, Alimi, Adel M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2023; Elsevier
• Subjects: Binary particle swarm optimization; Channel selection; Computer Science; Convolutional LSTM; Local learning strategy; Unsupervised EEG feature learning; Binary particle swarm optimization; Local learning strategy; Channel selection; Convolutional LSTM; Unsupervised EEG feature learning
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2023.104783

Electroencephalography (EEG) signals can demonstrate the activities of the human brain and recognize different emotional states. Emotion recognition based on full EEG channels leads to the use of redundant data and increases the hardware complexity. This paper presents a channel selection method based on a new Binary Many-Objective Particle Swarm Optimization with Cooperative Agents (BMaOPSO-CA). More specifically, we perform unsupervised feature learning with recurrent convolutional layers based on autoencoder architecture directly from clean EEG signals. Extensive validation on three public effective benchmarks, i.e. DASPS, DEAP, and SEED which are different in channel number used stimuli, and participant ratings, was carried out with subject independent scheme. As result, the experimental study highlights the optimal electrode locations related to emotions, leading to analyze the relationship between specific brain regions and emotions. •The design of EEG channel selection as an optimization problem with four objectives.•A ConvLSTM-based autoencoder model is proposed for spatio-temporal feature learning.•A memory updating is proposed to improve the particle’s local exploitation ability.•A multiple-swarm-based learning strategy is introduced.•An extensive validation on three public datasets with varied characteristics.