Selective Auditory Attention Decoding with a Two-Node Wireless EEG Sensor Network

Abstract

Selective auditory attention decoding (sAAD) enables neuro-steered hearing devices by identifying the attended speaker in a multi-speaker environment from neural activity recorded with electroencephalography (EEG). Despite algorithmic progress, practical deployment remains constrained by a lack of wearable, unobtrusive, and fully wireless EEG acquisition solutions. Therefore, this work aims to evaluate whether reliable sAAD can be achieved under realistic hardware constraints imposed by using a wireless EEG sensor network (WESN) consisting of miniaturized, galvanically isolated EEG sensor nodes. Here, we use such a WESN consisting of two synchronized, compact around-ear EEG sensor nodes worn bilaterally. Each node provides four local EEG channels derived from five pre-gelled electrodes, including a local reference. Sample-wise wireless synchronization of data from both nodes enables joint processing as an eight-channel EEG. On a newly recorded dataset acquired with this setup, correlation-based stimulus decoding achieves an average sAAD accuracy of 69.24% on 60s decision windows, comparable to wired around-ear EEG systems that measure long-distance scalp potentials. Hidden Markov model-based post-processing further improves to a steady-state accuracy of 77.17% with an average simulated attention switch detection time of 32.79s. Combining sensor nodes at both ears outperforms single-ear configurations, primarily by providing redundancy that increases robustness rather than by exploiting complementary spatial information. Finally, we show that a fixed bipolar configuration using four electrodes per ear, yielding three channels, suffices to maintain performance. These results demonstrate the practical feasibility of sAAD using a fully wireless, galvanically isolated around-ear WESN and establish a realistic performance benchmark under practical hardware constraints.