fNIRS-SpikeNet: A Spiking Neural Network Framework for Cognitive Load Classification in Cooperative Learning Environments

Functional near-infrared spectroscopy (fNIRS) is widely used to monitor cognitive load, yet classification in dynamic, cooperative settings remains challenging due to noise and hemodynamic delays. This article aims to develop a biologically inspired classification framework for fNIRS data that is suitable for both individual and collaborative learning environments. We propose fNIRS-SpikeNet, a spiking neural network (SNN) framework that integrates rate, latency, and delta spike encoding strategies with a residual-SNN architecture to capture spatiotem-poral dynamics. We evaluate our method on three public fNIRS datasets involving mental and motor tasks. Experimental results demonstrate that fNIRS-SpikeNet, particularly under rate encoding, significantly outperforms conventional machine learning and deep learning baselines in accuracy, efficiency, and real-time adaptability. These outcomes highlight the potential of SNNs for low-power, real-time neuroimaging in socially interactive applications.