Emotional Stress Classification Using Spiking Neural Networks

Emotional Stress Classification Using Spiking Neural Networks

Objective: This study examined the data modeling capability of spiking neural networks (SNN) in classifying stressed versus relaxed brain states using electroencephalogram (EEG) data. The input spatiotemporal dynamics were explored to obtain further knowledge regarding the two-brain states. Method: A publicly available EEG data set for emotion analysis using psychological signals (DEAP) collected from 32 participants (50% females) with an average age of 26.9 is used in this study. Firstly, data extraction is performed using a criterion that defines stress and relaxation states using self-reported valence and arousal scores. Two hundred eight such extracted samples were selected to train and evaluate a novel three-layer feedforward SNN. This SNN consisted of leaky-integrate and fire neurons and learned from incoming data using spike-time-dependent plasticity (STDP) and dynamically evolving SNN algorithms. The SNN performance was evaluated using both fivefold cross-validation and a 60:40 training testing split. To explore input spatiotemporal dynamics, a specialized SNN architecture for brain data processing named NeuCube was used. Results: The highest-performing model of the novel SNN algorithm produced 88% average accuracy (F1 score: 86.21%, Matthews correlation coefficient: 0.78). This SNN outperformed traditional machine learning (ML) techniques without the use of manual feature extraction. Moreover, the input dynamics revealed higher prefrontal activation during relaxation states compared to stress states. Conclusions: The results showed the capability of the SNN algorithm to recognize stressed and relaxed states of the brain, using temporal learning techniques. Furthermore, the findings obtained from NeuCube suggested a potential approach for brain data analysis, setting SNNs apart from black box approaches used for brain data processing.

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