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

翻译：由于认知复杂性和生理障碍，从内部信息需求构建查询在所有信息检索范式中仍然具有根本性挑战。脑通道检索通过直接将脑电图信号映射到段落表示，无需中间文本翻译来解决此问题。然而，现有BPR研究仅使用视觉刺激，留下关键问题未解：听觉脑电图能否为基于语音的界面和视障用户实现有效检索？在不同感官模态的脑电图数据集严重稀缺的情况下，联合训练能否提升性能？我们首次系统研究了听觉脑电图在BPR中的应用，并评估跨感官训练的效益。采用具有四种池化策略（CLS、均值、最大值、多向量）的双编码器架构，我们在Alice（听觉）和Nieuwland（视觉）数据集上进行了对比听觉单独训练、视觉单独训练及联合训练的受控实验。结果表明：听觉脑电图持续优于视觉脑电图，且采用CLS池化的跨感官训练相比单独训练实现显著提升——MRR提升31%（达0.474）、Hit@1提升43%（达0.314）、Hit@10提升28%（达0.858）。关键的是，联合听觉脑电图模型超越了BM25文本基线（MRR：0.474 vs 0.428），在实现无障碍界面的同时确立了神经查询与传统检索的竞争力。这些发现验证了听觉神经界面在信息检索任务中的有效性，并证明跨感官训练在解决数据稀缺问题的同时超越了单模态方法。代码：https://github.com/NiallMcguire/Audio_BPR