The electroencephalogram (EEG) offers a non-invasive means by which a listener's auditory system may be monitored during continuous speech perception. Reliable auditory-EEG decoders could facilitate the objective diagnosis of hearing disorders, or find applications in cognitively-steered hearing aids. Previously, we developed decoders for the ICASSP Auditory EEG Signal Processing Grand Challenge (SPGC). These decoders aimed to solve the match-mismatch task: given a short temporal segment of EEG recordings, and two candidate speech segments, the task is to identify which of the two speech segments is temporally aligned, or matched, with the EEG segment. The decoders made use of cortical responses to the speech envelope, as well as speech-related frequency-following responses, to relate the EEG recordings to the speech stimuli. Here we comprehensively document the methods by which the decoders were developed. We extend our previous analysis by exploring the association between speaker characteristics (pitch and sex) and classification accuracy, and provide a full statistical analysis of the final performance of the decoders as evaluated on a heldout portion of the dataset. Finally, the generalisation capabilities of the decoders are characterised, by evaluating them using an entirely different dataset which contains EEG recorded under a variety of speech-listening conditions. The results show that the match-mismatch decoders achieve accurate and robust classification accuracies, and they can even serve as auditory attention decoders without additional training.

翻译：脑电图提供了一种非侵入性手段，可在连续语音感知过程中监测听者的听觉系统。可靠的听觉-脑电图解码器有助于客观诊断听力障碍，或应用于认知控制的助听器。此前，我们为ICASSP听觉脑电信号处理大挑战赛开发了解码器。这些解码器旨在解决匹配-不匹配任务：给定短时段的脑电记录和两个候选语音片段，任务是识别哪个语音片段与该脑电片段在时间上对齐（即匹配）。该解码器利用语音包络的皮层响应以及言语相关的频率跟随响应，将脑电记录与语音刺激相关联。本文全面记录了解码器的开发方法。通过探索说话人特征（音高和性别）与分类准确率之间的关联，我们扩展了先前的分析，并对解码器在数据集预留部分上的最终性能进行了完整的统计分析。最后，通过在不同语音聆听条件下的脑电数据集上评估解码器，表征了其泛化能力。结果表明，匹配-不匹配解码器实现了精确且稳健的分类准确率，甚至可在无需额外训练的情况下充当听觉注意力解码器。