An Asynchronous Neuromorphic Architecture for Wearable EEG Emotion Recognition

Abstract

Neural activity associated with emotion is event-driven and temporally sparse, whereas electroencephalography (EEG) is acquired as a continuous signal rather than a usable event stream. Conventional synchronous hardware processes all time steps densely, increasing energy costs during prolonged wearable monitoring. We propose an asynchronous neuromorphic architecture in which a dynamic adaptive spiking neural network converts features derived from EEG into sparse spike events. The model combines a depthwise separable gating module, division-free time-to-first-spike encoding, and adaptive temporal window regulation. The resulting events drive a globally asynchronous locally synchronous (GALS) accelerator that activates data movement and computation only for valid events, reducing switching during idle periods. DA-SNN outperforms lightweight models on SEED, SEED-IV, SEED-V, DEAP, and DREAMER. The accelerator completes inference on a preprocessed EEG tensor in 18 μs, with total power consumption of 148 mW and energy consumption of 2.66 μJ per inference.