EEG-Based Fast Auditory Attention Detection in Real-Life Scenarios Using Time-Frequency Attention Mechanism

Auditory attention detection (AAD) based on electroencephalogram (EEG) helps recognize the target speaker in a cocktail party scenario, advancing auditory brain-computer interface development. Previous EEG studies on AAD were largely based on data collected in laboratory settings. In this study, we investigated the AAD with EEG data collected when subjects were walking and sitting in real-life scenarios. To improve the detection accuracy, we proposed the time-frequency attention mechanism to the convolution neural network on EEG data. Experimental results show that the proposed model outperforms the state-of-the-art models, with an accuracy of 98.1% on a decision window of 2s. When we used a 0.1s time window for fast decoding, the accuracy remained at 91.8%, suggesting the potential for real application. Further study on ablation experiments demonstrates the effectiveness of the proposed time-frequency attention mechanism. Analysis of the key EEG features indicates that the β band plays a vital role in AAD.