文章基本信息

标题：Thinking out loud, an open-access EEG-based BCI dataset for inner speech recognition
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作者：Nicolás Nieto ; Victoria Peterson ; Hugo Leonardo Rufner 等
期刊名称：Scientific Data
电子版ISSN：2052-4463
出版年度：2022
卷号：9
期号：1
页码：1-17
DOI：10.1038/s41597-022-01147-2
语种：English
出版社：Nature Publishing Group
摘要：Surface electroencephalography is a standard and noninvasive way to measure electrical brain activity. Recent advances in artifcial intelligence led to signifcant improvements in the automatic detection of brain patterns, allowing increasingly faster, more reliable and accessible Brain-Computer Interfaces. Diferent paradigms have been used to enable the human-machine interaction and the last few years have broad a mark increase in the interest for interpreting and characterizing the “inner voice” phenomenon. this paradigm, called inner speech, raises the possibility of executing an order just by thinking about it, allowing a “natural” way of controlling external devices. Unfortunately, the lack of publicly available electroencephalography datasets, restricts the development of new techniques for inner speech recognition. a ten-participant dataset acquired under this and two others related paradigms, recorded with an acquisition system of 136 channels, is presented . The main purpose of this work is to provide the scientifc community with an open-access multiclass electroencephalography database of inner speech commands that could be used for better understanding of the related brain mechanisms.