2026-10-13T04:07:51Z https://www.scidb.cn/oai

10.57760/sciencedb.12128 2025-07-10T18:09:22Z

2025-07-10 CuBiAAD-v2 doi:10.57760/sciencedb.12128 en Investigating auditory attention in multiple speech streams is crucial for the research on language comprehension and the development of neuro-steered hearing devices. This study proposed a multimodal dataset recording brain activities based on a cue-masked auditory attention paradigm, which avoid information leakage before the experiment for better simulating real-world scenarios. The dataset included electroencephalography (EEG) data from 30 subjects. The dataset would benefit the studies on the mechanism of speech comprehension, the sensor optimization and algorithm design of auditory attention decoding, facilitating the technology development of auditory-based brain computer interface (BCI). BCI; EEG; fnirs; AAD; bimodal Keren Shi Xue Yuan Xu Liu Ruiting Dai Na Li Yunfa Fu Ning Jiang Jiayuan He PUBLIC https://creativecommons.org/licenses/by-nc-nd/4.0/ dataset Science Data Bank