1000小时日语语音产生的EEG-EMG-音频数据集
A 1000-hour EEG-EMG-audio dataset of Japanese speech production
AI总结：
本研究构建了一个包含1020小时同步头皮脑电图、面部肌电图和语音音频的多模态数据集，来自三名健康日语母语者在开放词汇有声语音过程中的记录，旨在支持语音解码、多模态信号处理及脑电图表示学习等研究。
摘要：
我们提出了一个多模态数据集，包含来自三名健康日语母语者在开放词汇有声语音过程中同步记录的1020小时头皮脑电图（EEG）、面部肌电图（EMG）和语音音频。记录使用三种EEG系统——超高密度系统（g.Pangolin）和两种帽式系统（g.SCARABEO和eegosports），通道数从62到128不等——在数月内跨多个会话采集。每个会话提供时间同步的EEG、面部EMG和音频，以及语音事件注释和转录。尽管数据集的主要动机是语音解码，但它也支持多模态信号处理、伪影建模、纵向和跨设备适应以及EEG表示学习等工作。技术验证包括跨参与者、设备和任务的功率谱密度和事件相关电位分析，显示了预期的1/f频谱轮廓、任务相关的alpha频段衰减和时间锁定的诱发响应。该数据集以脑成像数据结构（BIDS）格式通过OpenNeuro在CC0豁免下发布，以支持语音相关及更广泛的EEG研究。
英文摘要：
We present a multimodal dataset of 1020 hours of simultaneously recorded scalp electroencephalography (EEG), facial electromyography (EMG), and speech audio from three healthy native Japanese speakers during open-vocabulary overt speech. Recordings were acquired with three EEG systems-an ultra-high-density system (g.Pangolin) and two cap-type systems (g.SCARABEO and eegosports), spanning 62-128 channels-across many sessions over several months. Each session provides time-synchronized EEG, facial EMG, and audio, together with speech-event annotations and transcriptions. Although collected with speech decoding as a primary motivation, the dataset also supports work on multimodal signal processing, artifact modeling, longitudinal and cross-device adaptation, and EEG representation learning. Technical validation included power spectral density and event-related potential analyses across participants, devices, and tasks, which showed the expected 1/f spectral profile, task-related alpha-band attenuation, and time-locked evoked responses. The dataset is released in Brain Imaging Data Structure (BIDS) format via OpenNeuro under a CC0 waiver to support both speech-related and broader EEG research.
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