Novel Architecture For EEG Emotion Classification Using Neurofuzzy Spike Net
DOI:
https://doi.org/10.22399/ijcesen.829Keywords:
Spiking Neural Networks, Fuzzy Hierarchical Attention Membership, Neuro Fuzzy SpikeNet, Temporal Pattern Detection, Wearable DevicesAbstract
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, and personalized learning environments. Conventional Neural Networks (CNN) and traditional signal processing techniques have usually been performed for EEG emotion classification, which face difficulty in capturing complicated temporal dynamics and inherent uncertainty in EEG signals. The proposed work overcomes challenges using a new architecture merging Spiking Neural Networks (SNN) with a Fuzzy Hierarchical Attention Membership (FHAM), the NeuroFuzzy SpikeNet (NFS-Net). NFS-Net takes advantage of SNNs' event-driven nature in the processing of EEG signals, which are treated independently as asynchronous, spike-based events like the biological neurons. It allows capturing temporal patterns in EEG data with high precision, which is rather important for correct emotion recognition. The local spiking feature of SNNs encourages sparse coding, making the whole system computational power and energy highly effective and it is very suitable for wearable devices in real-time applications.
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