Fractal Spiking Neural Network Scheme for EEG-Based Emotion Recognition

• Published in: IEEE journal of translational engineering in health and medicine Vol. 12; pp. 106 - 118
• Main Authors: Li, Wei, Fang, Cheng, Zhu, Zhihao, Chen, Chuyi, Song, Aiguo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biological neural networks; Electroencephalogram; Electroencephalography; Emotion recognition; Emotions; Feature extraction; fractal spiking neural network; Fractals; inverted drop-path; Neural networks; Neural Networks, Computer; Neurons; Recognition, Psychology; Spatial data; Three-dimensional displays; Electroencephalogram; emotion recognition; inverted drop-path; fractal spiking neural network
• ISSN: 2168-2372; 2168-2372
• DOI: 10.1109/JTEHM.2023.3320132

Electroencephalogram (EEG)-based emotion recognition is of great significance for aiding in clinical diagnosis, treatment, nursing and rehabilitation. Current research on this issue mainly focuses on utilizing various network architectures with different types of neurons to exploit the temporal, spectral, or spatial information from EEG for classification. However, most studies fail to take full advantage of the useful Temporal-Spectral-Spatial (TSS) information of EEG signals. In this paper, we propose a novel and effective Fractal Spike Neural Network (Fractal-SNN) scheme, which can exploit the multi-scale TSS information from EEG, for emotion recognition. Our designed Fractal-SNN block in the proposed scheme approximately simulates the biological neural connection structures based on spiking neurons and a new fractal rule, allowing for the extraction of discriminative multi-scale TSS features from the signals. Our designed training technique, inverted drop-path, can enhance the generalization ability of the Fractal-SNN scheme. Sufficient experiments on four public benchmark databases, DREAMER, DEAP, SEED-IV and MPED, under the subject-dependent protocols demonstrate the superiority of the proposed scheme over the related advanced methods. In summary, the proposed scheme provides a promising solution for EEG-based emotion recognition.