Auditory Brain Passage Retrieval: Cross-Sensory EEG Training for Neural Information Retrieval
This enables accessible voice-based interfaces for visually impaired users, though it is incremental as it builds on existing BPR methods.
The paper tackled the problem of query formulation in Information Retrieval by investigating auditory EEG signals for Brain Passage Retrieval, showing that auditory EEG outperforms visual EEG and cross-sensory training improves retrieval metrics, with combined models surpassing BM25 baselines (e.g., MRR 0.474 vs 0.428).
Query formulation from internal information needs remains fundamentally challenging across all Information Retrieval paradigms due to cognitive complexity and physical impairments. Brain Passage Retrieval (BPR) addresses this by directly mapping EEG signals to passage representations without intermediate text translation. However, existing BPR research exclusively uses visual stimuli, leaving critical questions unanswered: Can auditory EEG enable effective retrieval for voice-based interfaces and visually impaired users? Can training on combined EEG datasets from different sensory modalities improve performance despite severe data scarcity? We present the first systematic investigation of auditory EEG for BPR and evaluate cross-sensory training benefits. Using dual encoder architectures with four pooling strategies (CLS, mean, max, multi-vector), we conduct controlled experiments comparing auditory-only, visual-only, and combined training on the Alice (auditory) and Nieuwland (visual) datasets. Results demonstrate that auditory EEG consistently outperforms visual EEG, and cross-sensory training with CLS pooling achieves substantial improvements over individual training: 31% in MRR (0.474), 43% in Hit@1 (0.314), and 28% in Hit@10 (0.858). Critically, combined auditory EEG models surpass BM25 text baselines (MRR: 0.474 vs 0.428), establishing neural queries as competitive with traditional retrieval whilst enabling accessible interfaces. These findings validate auditory neural interfaces for IR tasks and demonstrate that cross-sensory training addresses data scarcity whilst outperforming single-modality approaches Code: https://github.com/NiallMcguire/Audio_BPR