Low-latency auditory spatial attention detection based on spectro-spatial features from EEG
This addresses the cocktail party effect in speech processing for applications like brain-computer interfaces, but it is incremental as it builds on prior work using alpha band signals.
The paper tackled the problem of detecting auditory spatial attention (left/right) from EEG signals without needing auditory stimuli as references, achieving accuracies of 81.7% for 1-second and 94.6% for 10-second decision windows.
Detecting auditory attention based on brain signals enables many everyday applications, and serves as part of the solution to the cocktail party effect in speech processing. Several studies leverage the correlation between brain signals and auditory stimuli to detect the auditory attention of listeners. Recently, studies show that the alpha band (8-13 Hz) EEG signals enable the localization of auditory stimuli. We believe that it is possible to detect auditory spatial attention without the need of auditory stimuli as references. In this work, we use alpha power signals for automatic auditory spatial attention detection. To the best of our knowledge, this is the first attempt to detect spatial attention based on alpha power neural signals. We propose a spectro-spatial feature extraction technique to detect the auditory spatial attention (left/right) based on the topographic specificity of alpha power. Experiments show that the proposed neural approach achieves 81.7% and 94.6% accuracy for 1-second and 10-second decision windows, respectively. Our comparative results show that this neural approach outperforms other competitive models by a large margin in all test cases.