Modeling the Temporal Dynamics of EEG Signals in Selective Listening

Human brain possesses an extraordinary ability to attend to a specific sound source in a multi-talk, noisy environment such as a cocktail party. Auditory attention detection (AAD) aims to automatically identify such attentive neural activity from brain signals, such as electroencephalography (EEG). Given the dynamic and nonlinear nature of EEG signals, we propose a spiking long short-term memory (LSTM) network to capture the temporal features from EEG data. Additionally, we introduce a spiking temporal attention mechanism that dynamically assigns differentiated weights, thereby enhancing the representation of EEG features. We evaluate our proposed spiking temporal LSTM model, named ST-LSTM, on a widely used AAD dataset through a wide range of experiments. The experiments demonstrate that ST-LSTM outperforms other competing models, especially in low-latency settings. Moreover, with low power consumption, ST-LSTM offers a practical solution for edge computing implementations such as neuro-steered hearing aids, and other portable brain-computer interfaces.