OpenMIIR - a public domain dataset of EEG recordings for music imagery information retrieval

Music imagery information retrieval (MIIR) systems may one day be able to recognize a song just as we think of it. As a step towards such technology, we are presenting a public domain dataset of electroencephalography (EEG) recordings taken during music perception and imagination. We acquired this data during an ongoing study that so far comprised 10 subjects listening to and imagining 12 short music fragments - each 7s-16s long - taken from well-known pieces. These stimuli were selected from different genres and systematically span several musical dimensions such as meter, tempo and the presence of lyrics. This way, various retrieval and classification scenarios can be addressed. The dataset is primarily aimed to enable music information retrieval researchers interested in these new MIIR challenges to easily test and adapt their existing approaches for music analysis like fingerprinting, beat tracking or tempo estimation on this new kind of data. We also hope that the OpenMIIR dataset will facilitate a stronger interdisciplinary collaboration between music information retrieval researchers and neuroscientists.音乐意象信息检索（Music Imagery Information Retrieval, MIIR）系统未来或可仅凭人类的音乐思维感知就识别出对应歌曲。为推进该技术的发展，我们发布了一套面向公有领域的脑电（Electroencephalography, EEG）数据集，记录了受试者在音乐感知与音乐想象过程中的脑电信号。本数据集采集自一项进行中的研究：截至目前，该研究共有10名受试者参与，他们需聆听并想象12段取自经典作品的短音乐片段，每段时长为7秒至16秒不等。这些音乐刺激素材选自不同音乐流派，并系统覆盖了节拍、速度、是否带有歌词等多个音乐维度，借此可适配多种检索与分类任务场景。本数据集的核心目标，是帮助关注音乐意象信息检索全新挑战的音乐信息检索研究者，能够便捷地在这类新型脑电数据上测试并适配他们现有的音乐分析方法，比如音频指纹提取、节拍追踪或速度估计算法。此外，我们期望本OpenMIIR数据集能够推动音乐信息检索研究者与神经科学家之间更深入的跨学科合作。