Novel Architecture For EEG Emotion Classification Using Neurofuzzy Spike Net

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 7, 2025

Emotion recognition from Electroencephalogram (EEG) signals is one of the fastest-growing and challenging fields, with a huge prospect for future application in mental health monitoring, human-computer interaction, personalized learning environments. Conventional Neural Networks (CNN) traditional signal processing techniques have usually been performed EEG emotion classification, which face difficulty capturing complicated temporal dynamics inherent uncertainty signals. The proposed work overcomes challenges using new architecture merging Spiking (SNN) Fuzzy Hierarchical Attention Membership (FHAM), NeuroFuzzy SpikeNet (NFS-Net). NFS-Net takes advantage SNNs' event-driven nature signals, are treated independently as asynchronous, spike-based events like biological neurons. It allows patterns data high precision, rather important correct recognition. local spiking feature SNNs encourages sparse coding, making whole system computational power energy highly effective it very suitable wearable devices real-time applications.

Language: Английский

Electroencephalogram (EEG) classification using a bio-inspired deep oscillatory neural network

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 103, P. 107379 - 107379

Published: Dec. 26, 2024

Language: Английский