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 II configuration. 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