Low-power and lightweight spiking transformer for EEG-based auditory attention detection.

EEG signal analysis can be used to study brain activity and the function and structure of neural networks, helping to understand neural mechanisms such as cognition, emotion, and behavior. EEG-based auditory attention detection is using EEG signals to determine an individual's level of attention to specific auditory stimuli. In this technique, researchers record and analyze a subject's electrical activity to infer whether an individual is paying attention to a specific auditory stimulus. The model deployed in edge devices will be greatly convenient for subjects to use. However, most of the existing EEG-based auditory attention detection models use traditional neural network models, and their high computing load makes deployment on edge devices challenging. We present a pioneering approach in the form of a binarized spiking Transformer for EEG-based auditory attention detection, which is characterized by high accuracy, low power consumption, and lightweight design, making it highly suitable for deployment on edge devices. In terms of low power consumption, the network is constructed using spiking neurons, which emit sparse and binary spike sequences, which can effectively reduce computing power consumption. In terms of lightweight, we use a post-training quantization strategy to quantize the full-precision network weights into binary weights, which greatly reduces the model size. In addition, the structure of the Transformer ensures that the model can learn effective information and ensure its high performance. We verify the model through mainstream datasets, and experimental results show that our model performance can exceed the existing state-of-the-art models, and the model size can be reduced by more than 21 times compared with the original full-precision network counterpart.