NeuroOM: EEG Dataset Capturing Brain Responses to Musical and Non-Musical OM ("ॐ" ) Sound

Electroencephalography (EEG) is a popular non-invasive technology for recording electrical activity in
the brain, which can provide useful insights on cognitive and emotional responses to auditory stimuli.
In this work, we offer a unique EEG dataset gathered during OM ("ॐ" ) sound listening sessions, which is
intended to evaluate the brain processes related with musical and non-musical auditory experiences. The
data was collected using the Emotiv Insight headset, a five-channel EEG equipment that collects signals
at the AF3, AF4, T7, T8, and Pz electrode sites. A total of 132 EEG recordings are provided in European

Data Format (.edf) and classified into three levels of data processing: raw, standardized, and pre-
processed. Each stage contains data from two auditory conditions—musical OM ("ॐ" ) and non-musical OM ("ॐ" )—

which allow for an organized and comparative investigation of brain responses.
The raw data is divided into 44 files, 22 labeled as rm_.edf, representing raw EEG signals recorded
during musical OM ("ॐ" ) sound exposure, and 22 labeled as rn_.edf, indicating raw EEG signals recorded
during non-musical OM ("ॐ" ) sound exposure. These files preserve the original signal properties, including
noise and artifacts, and are designed for researchers working on custom preprocessing, signal quality
assessment, and artifact removal algorithms. The second stage includes standardized data, which consists
of 44 files designated as m_.edf and n_.edf, representing musical and non-musical OM ("ॐ" ) situations,
respectively. These files have been normalized and formatted to maintain uniformity across preparation
techniques and enable reproducible analysis. Finally, the dataset contains 44 pre-processed files labelled
pm_.edf and pn_.edf, which represent musical and non-musical OM ("ॐ" ) EEG data that has been filtered,
artifact-reduced, and segmented. These files are designed for direct input into machine learning models,
enabling tasks like classification, clustering, and feature extraction.
This dataset offers a unique chance to investigate the cognitive and affective impacts of OM ("ॐ" ) sound
listening, distinguishing between musical and non-musical stimuli. The data's multi-stage format allows
researchers to interact with EEG signals at various stages of processing, ranging from raw signal analysis
to model-ready inputs. The use of a commercially available EEG device provides accessibility and
reproducibility, making this dataset an important resource for research into auditory neuroscience,
emotional computing, and brain-computer interface development.