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

Objective. 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. We therefore evaluate whether reliable sAAD can be achieved under the realistic hardware constraints of a wireless EEG sensor network (WESN) of miniaturized, galvanically isolated EEG sensor nodes. Approach. We use a WESN 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, we apply correlation-based stimulus decoding, followed by hidden Markov model (HMM)-based post-processing. Main results. Stimulus decoding achieves an average sAAD accuracy of 69.24% on 60s decision windows, comparable to previously reported wired around-ear EEG systems that measure long-distance scalp potentials. HMM-based post-processing further improves this 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 through redundancy rather than complementary spatial information. Finally, a fixed bipolar configuration using four electrodes per ear, yielding three channels, suffices to maintain performance. Significance. 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.