ICM EEG Dataset: EEG Recordings During Perception of Indian Classical Music Ragas

This dataset contains raw electroencephalography (EEG) recordings from nine untrained participants (7 male, 2 female, ages 23–26) collected during the perception of three Indian Classical Music (ICM) ragas. It was created as part of the study: Das, N. & Chakraborty, M. (2025). “Optimal multimodal feature combination and classifier selection for music-based EEG signal analysis.” Computers in Biology and Medicine, 196, 110696. https://doi.org/10.1016/j.compbiomed.2025.110696 Participants listened to 2‑minute alaap sections of Raga Ahir Bhairav, Bhimpalashri, and Yaman Kalyan (performed by Pandit Ravi Shankar), each presented twice in randomized order. The 17‑minute experimental protocol began with a 2‑minute baseline rest, followed by six music segments interleaved with 30‑second rest periods. Raga names were announced ~5 s before each music segment to reorient attention. Detailed description of the experimental protocol is given in the cited study. EEG was recorded with the RMS Maximus 24 system (19 scalp electrodes, international 10–20 system, reference at nasion, hardware band‑pass 0.5–70 Hz, sampling rate 256 Hz). ECG was acquired simultaneously in a standard Lead I configuration using bipolar electrodes. Participants sat with eyes closed in a dimly lit, sound‑controlled room. All participants provided informed consent to participate in this study, and all identifiable information was anonymized. The dataset is distributed as a single archive containing: One unprocessed CSV file per subject (raw_data/), each with 261,120 × 22 samples (1020 s × 256 Hz). phase_events.csv: start/end times (in seconds) for each rest and music segment. music_order.csv: mapping of generic labels (music_1–music_6) to actual ragas for each participant. Detailed documentation (README.md). The EEG data are raw and unprocessed except for trimming to the exact experimental duration. Citation: If you use this dataset in your research, please cite both this dataset (Zenodo DOI) and the associated paper:Das, N. & Chakraborty, M. (2025). Optimal multimodal feature combination and classifier selection for music-based EEG signal analysis. Computers in Biology and Medicine, 196, 110696. https://doi.org/10.1016/j.compbiomed.2025.110696本数据集包含9名未经过训练的受试者（7名男性，2名女性，年龄23~26岁）在感知3种印度古典音乐（Indian Classical Music，ICM）拉格时采集的原始脑电图（electroencephalography，EEG）记录。本数据集源于以下研究： Das, N. 与 Chakraborty, M. (2025). "Optimal multimodal feature combination and classifier selection for music-based EEG signal analysis." 《Computers in Biology and Medicine》, 196, 110696. https://doi.org/10.1016/j.compbiomed.2025.110696 受试者聆听了由Pandit Ravi Shankar演奏的拉格Ahir Bhairav、Bhimpalashri以及Yaman Kalyan的2分钟阿拉普（alaap）段落，每种段落以随机顺序呈现两次。整个实验流程时长17分钟，起始为2分钟的基线静息状态，随后是6段音乐片段，每段之间穿插30秒的静息期。在每段音乐片段开始前约5秒，会播报拉格名称以引导受试者注意力。实验流程的详细说明请参见上述引用研究。 脑电图采用RMS Maximus 24系统采集（19个头皮电极，采用国际10-20系统，参考电极置于鼻根，硬件带通滤波范围0.5~70 Hz，采样率256 Hz）。同时采用双极电极以标准I导联配置同步采集心电图（electrocardiography，ECG）信号。受试者在光线昏暗、声音受控的房间中闭眼端坐。所有受试者均已签署知情同意书，且所有可识别信息均已匿名化处理。 本数据集以单个归档文件形式分发，包含以下内容： 1. 每个受试者对应一份未处理的CSV文件（存放于raw_data/目录下），每份文件包含261120 × 22个样本（对应1020秒 × 256 Hz采样率）。 2. phase_events.csv：记录各静息期与音乐片段的起始/结束时间（单位：秒）。 3. music_order.csv：将通用标签（music_1~music_6）映射至每位受试者实际聆听的拉格名称。 4. 详细文档（README.md）。 本数据集的脑电图数据为原始未处理状态，仅经过裁剪以匹配精确的实验时长。 引用说明：若在研究中使用本数据集，请同时引用本数据集（Zenodo DOI）以及上述关联论文：Das, N. & Chakraborty, M. (2025). "Optimal multimodal feature combination and classifier selection for music-based EEG signal analysis." Computers in Biology and Medicine, 196, 110696. https://doi.org/10.1016/j.compbiomed.2025.110696