Low-latency auditory spatial attention detection based on spectro-spatial features from EEG

TL;DR

This paper introduces a novel method for detecting auditory spatial attention using alpha power EEG signals without auditory stimuli references, achieving high accuracy and outperforming existing models.

Contribution

It is the first to detect spatial attention solely based on alpha power neural signals using a spectro-spatial feature extraction technique.

Findings

Achieves 81.7% accuracy with 1-second windows.

Achieves 94.6% accuracy with 10-second windows.

Outperforms other models significantly in all tests.

Abstract

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.