**Overview:

The Bonn EEG Dataset is a widely recognized dataset in the field of biomedical signal processing and machine learning, specifically designed for research in epilepsy detection and EEG signal analysis. It contains electroencephalogram (EEG) recordings from both healthy individuals and patients with epilepsy, making it suitable for tasks such as seizure detection and classification of brain activity states. The dataset is structured into five distinct subsets (labeled A, B, C, D, and E), each comprising 100 single-channel EEG segments, resulting in a total of 500 segments. Each segment represents 23.6 seconds of EEG data, sampled at a frequency of 173.61 Hz, yielding 4,096 data points per segment, stored in ASCII format as text files.

****Structure and Label:

• Set A: EEG recordings from healthy individuals with eyes open, capturing normal brain activity under visual stimulation.
• Set B: EEG recordings from healthy individuals with eyes closed, reflecting brain activity in a resting state.
• Set C: EEG recordings from epilepsy patients, collected from the epileptogenic zone during an interictal (seizure-free) period.
• Set D: EEG recordings from epilepsy patients, collected from the hippocampal formation of the opposite brain hemisphere during an interictal period.
• Set E: EEG recordings from epilepsy patients during an ictal (seizure) period, capturing brain activity during an epileptic seizure. Each subset contains 100 EEG segments, ensuring a balanced distribution across the five classes, which supports both binary (e.g., healthy vs. epileptic) and multi-class (e.g., A-E classification) tasks.

**Key Characteristics

• Size: 500 EEG segments (100 segments per subset, across five subsets).
• Data Type: Single-channel EEG signals, stored in text files (ASCII format).
• Sampling Rate: 173.61 Hz, providing high temporal resolution.
• Segment Length: 23.6 seconds per segment, equivalent to 4,096 data points.
• Labels: Clearly defined for each subset (A: healthy, eyes open; B: healthy, eyes closed; C: interictal, epileptogenic zone; D: interictal, opposite hemisphere; E: ictal), enabling precise model evaluation.
• Preprocessing: The data is not pre-filtered, but a low-pass filter with a 40 Hz cutoff is recommended to remove high-frequency noise and artifacts, as suggested in the original documentation.

**Applications

The Bonn EEG Dataset is ideal for machine learning and signal processing tasks, including: - Developing algorithms for epileptic seizure detection and prediction. - Exploring feature extraction techniques, such as wavelet transforms, for EEG signal analysis. - Classifying brain states (healthy vs. epileptic, interictal vs. ictal). - Supporting research in neuroscience and medical diagnostics, particularly for epilepsy monitoring and treatment.

**Source

• The dataset is publicly available from the University of Bonn and can be downloaded from the following link: University of Bonn EEG Dataset
• The dataset is provided as five ZIP files, each containing 100 text files corresponding to the EEG segments for subsets A, B, C, D, and E.

**Citation

When using this dataset, researchers are required to cite the original publication: Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6), 061907. DOI: 10.1103/PhysRevE.64.061907.

**Additional Notes

1. The dataset is randomized, with no specific information provided about patients or electrode placements, ensuring simplicity and focus on signal characteristics.

2. The data is not hosted on Kaggle or Hugging Face but is accessible directly from the University of Bonn’s repository or mirrored sources.

3. Researchers may need to apply preprocessing steps, such as filtering or normalization, to optimize the data for machine learning tasks.

4. The dataset’s balanced structure and clear labels make it an excellent choice for a one-week machine learning project, particularly for tasks involving traditional algorithms like SVM, Random Forest, or Logistic Regression.

5. This dataset provides a robust foundation for learning signal processing, feature extraction, and machine learning techniques while addressing a real-world medical challenge in epilepsy detection.

The EEG Motor Movement/Imagery Dataset provides 64-channel electroencephalography recordings collected with the BCI2000 system during real and imagined motor tasks. Participants completed 14 experimental runs, including two one-minute baseline recordings with eyes open and eyes closed, followed by three repetitions of four two-minute task conditions. These conditions involved either executing or imagining unilateral fist movements in response to left/right visual targets, or executing or imagining bilateral fist or foot movements in response to top/bottom visual targets. EEG signals were recorded according to the international 10-10 electrode placement system at a sampling rate of 160 Hz and are provided in EDF+ format with accompanying annotation channels. Event labels identify rest periods and task onsets using three codes: T0 for rest, T1 for left-fist or both-fists movement/imagery depending on the run type, and T2 for right-fist or both-feet movement/imagery. The dataset supports research in brain-computer interfaces, motor imagery classification, movement-related EEG dynamics, and the development of signal-processing and machine-learning methods for neural decoding.

The Harvard EEG Database will encompass data gathered from four hospitals affiliated with Harvard University: Massachusetts General Hospital (MGH), Brigham and Women's Hospital (BWH), Beth Israel Deaconess Medical Center (BIDMC), and Boston Children's Hospital (BCH). The EEG data includes three types:

rEEG: "routine EEGs" recorded in the outpatient setting.
EMU: recordings obtained in the inpatient setting, within the Epilepsy Monitoring Unit (EMU).
ICU/LTM: recordings obtained from acutely and critically ill patients within the intensive care unit (ICU).

This dataset contains EEG recordings from 18 subjects listening to one of two competing speech audio streams. Continuous speech in trials of ~50 sec. was presented to normal hearing listeners in simulated rooms with different degrees of reverberation. Subjects were asked to attend one of two spatially separated speakers (one male, one female) and ignore the other. Repeated trials with presentation of a single talker were also recorded. The data were recorded in a double-walled soundproof booth at the Technical University of Denmark (DTU) using a 64-channel Biosemi system and digitized at a sampling rate of 512 Hz. Full details can be found in:

• Søren A. Fuglsang, Torsten Dau & Jens Hjortkjær (2017): Noise-robust cortical tracking of attended speech in real-life environments. NeuroImage, 156, 435-444

and

• Daniel D.E. Wong, Søren A. Fuglsang, Jens Hjortkjær, Enea Ceolini, Malcolm Slaney & Alain de Cheveigné: A Comparison of Temporal Response Function Estimation Methods for Auditory Attention Decoding. Frontiers in Neuroscience, https://doi.org/10.3389/fnins.2018.00531

The data is organized in format of the publicly available COCOHA Matlab Toolbox. The preproc_script.m demonstrates how to import and align the EEG and audio data. The script also demonstrates some EEG preprocessing steps as used the Wong et al. paper above. The AUDIO.zip contains wav-files with the speech audio used in the experiment. The EEG.zip contains MAT-files with the EEG/EOG data for each subject. The EEG/EOG data are found in data.eeg with the following channels:

• channels 1-64: scalp EEG electrodes
• channel 65: right mastoid electrode
• channel 66: left mastoid electrode
• channel 67: vertical EOG below right eye
• channel 68: horizontal EOG right eye
• channel 69: vertical EOG above right eye
• channel 70: vertical EOG below left eye
• channel 71: horizontal EOG left eye
• channel 72: vertical EOG above left eye

The expinfo table contains information about experimental conditions, including what what speaker the listener was attending to in different trials. The expinfo table contains the following information:

• attend_mf: attended speaker (1=male, 2=female)
• attend_lr: spatial position of the attended speaker (1=left, 2=right)
• acoustic_condition: type of acoustic room (1= anechoic, 2= mild reverberation, 3= high reverberation, see Fuglsang et al. for details)
• n_speakers: number of speakers presented (1 or 2)
• wavfile_male: name of presented audio wav-file for the male speaker
• wavfile_female: name of presented audio wav-file for the female speaker (if any)
• trigger: trigger event value for each trial also found in data.event.eeg.value

DATA_preproc.zip contains the preprocessed EEG and audio data as output from preproc_script.m.

The dataset was created within the COCOHA Project: Cognitive Control of a Hearing Aid

Description:
This dataset contains electroencephalography (EEG) signals recorded during an emotion classification experiment using two devices: a high-end professional EEG system (BrainVision) and a low-cost brain-computer interface (Emotiv EPOC+). Data were collected from 20 participants while they were exposed to visual stimuli from the International Affective Picture System (IAPS), designed to induce four emotional states based on Russell’s valence-arousal model:

• HVHA: High valence, high arousal
• HVLA: High valence, low arousal
• LVHA: Low valence, high arousal
• LVLA: Low valence, low arousal

The dataset includes raw EEG recordings, preprocessed signals, and extracted features for further analysis. Additionally, a README file provides detailed information on the data structure, device configurations, and emotional labels assigned to each signal segment.

Data Format:

• Raw EEG data: Original signal recordings (.edf, .csv, .mat).
• Preprocessed EEG: Filtered and normalized data (.csv, .mat).
• Extracted features: Set of statistical, spectral, and entropy-based features used in the study (.csv, .xlsx).
• Metadata: Participant information (anonymized), experimental details, and emotional condition labels (.txt, .json).

Usage and Applications:
This dataset can be used for research in neuroscience, emotion classification, artificial intelligence, machine learning, and EEG signal processing. It is particularly suitable for developing and validating machine learning and deep learning models applied to emotion recognition from brain signals.

License & Accessibility:
The dataset is publicly available under the Creative Commons Attribution (CC BY 4.0) license, allowing free use, distribution, and modification with proper attribution.

Recommended Citation:
If you use this dataset in your research, please cite the associated publication:

Sánchez-Reolid, R., Martínez-Sáez, M. C., García-Martínez, B., Fernández-Aguilar, L., Ros, L., Latorre, J. M., & Fernández-Caballero, A. (2022). Emotion classification from EEG with a low-cost BCI versus a high-end equipment. International Journal of Neural Systems, 32(10), 2250041. World Scientific. https://doi.org/10.1142/S0129065722500411

The Neurotech EEG Dataset is a large clinical scalp EEG corpus comprising 23,607 EEG recordings from 4,914 patients acquired by a single EEG monitoring service provider between 2021 and 2025, totaling 212,186 hours of signal data (10.2 TB). A distinguishing feature is the large proportion of ambulatory recordings acquired in patients' homes, including multi-day studies — a real-world, out-of-hospital recording context largely absent from existing large clinical EEG corpora, which are predominantly hospital-based. Recordings span routine outpatient EEGs, ambulatory monitoring, and continuous inpatient/ICU EEG, all acquired with Natus/Xltek NeuroWorks hardware at 256 Hz using the standard International 10-20 montage. The dataset includes 226,486 technician-placed annotations — including 50,482 spike markers, 6,892 seizure markers, 21,330 sharp-wave annotations, and free-text clinical observations. De-identified patient-level clinical metadata (demographics, ICD-10 referral diagnoses, comorbidities, medications, EEG findings, and monitoring summaries) is included for the 4,812 patients with available clinical records. Data are released in BIDS-EEG format with HIPAA-compliant de-identification including per-patient date shifting and automated name scrubbing.

This Siena Sleep EEG dataset contains multi-channel EEG recordings collected during sleep, specifically curated for epilepsy detection and sleep stage analysis. Electroencephalography (EEG) is one of the most reliable methods for studying brain activity during sleep, and it plays a crucial role in diagnosing neurological disorders such as epilepsy.

The dataset is formatted as a large-scale time-series table where each row represents a sampled time point, and each column corresponds to an EEG electrode channel. An additional diagnosis label column indicates whether the signal segment belongs to a healthy control or an epilepsy patient.

Dataset Structure

Number of Records: 944,640 samples

Number of Features: 20 EEG channels + 1 diagnosis label

File Format: CSV

Memory Size: ~150 MB

Columns

EEG Channels (20):

Fp1, F3, C3, P3, O1, F7, T3, T5, Fc1, Fc5, Cp1, Cp5, F9, Fz, Cz, Pz, Pf2, F4, C4, P4

These correspond to standard 10–20 EEG electrode placements, covering frontal, central, parietal, occipital, and temporal lobes.

diagnosis: 0 → Non-epileptic (Healthy subject)

1 → Sleep Stage Epileptic case

Abstract: This dataset includes the EEG of 6 epileptic patients recorded at the Epilepsy monitoring unit of the American university of Beirut Medical Center between January 2014 and July 2015. The data represents measurements from 21 scalp electrodes, following the 10-20 electrode system, sampled at 500 Hz . All channels have been bandpass filtered between 1/1.6 Hz and 70Hz while filtering out the 50Hz (electrical utility frequency). Some channels have been omitted from specific recordings due to artifact constraints.

This work was made possible by NPRP grant # NPRP12S-0305-190231 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors.

Electroencephalography (EEG) is a technique for measuring the electrical activity of the brain in the form of action potentials using electrodes placed on the scalp. The technique is gaining popularity for research investigations due to its non-invasive nature and ease of application. EEG exposes a wide range of human brain potentials, including event-related, sensory, and visually evoked potentials (VEPs), and helps to build complex applications. The current dataset consists of thirty-two subjects' EEG recordings in response to visual stimuli (VEPs). The purpose of collecting such data is because of its contribution in the advancement of visual decoding and supporting EEG-based image classification and reconstruction. The primary goal is to investigate the cognitive mechanisms behind known and unknown perceptions. The dataset was collected using a standardised experimental setup that included several experimental phases to capture the essence of the experiment. Thirty-five adult participants participated in the data collection process. They had no visual impairment and took the Vividness of Visual Imagery Questionnaire (VVIQ) test to answer sixteen questions based on their memory and imagination. Out of the thirty-five participants, thirty-two cleared the test and their EEG were recorded. The data was collected using a 14-channel EPOC X – 14 EEG device. The recordings were sampled at 128 Hz, and the 10 – 20 system was followed for electrode placement. EMOTIVPro software was used for collection and annotation. The brain activity signals were collected while the participants were viewing an image displayed on a white screen. The image consists of natural objects like apple (class A), flower (class F), car (class C) and human face (class P). The file “VVIQuestionnaire.pdf” is the questionnaire used to ascertain the visual imagination of the participants. The other file “Participant_info.csv” contains the details of the participants (age, gender, image class viewed, and Participant ID) and their VVIQ score. The names of the participants have been purposely removed for reasons of anonymity and a unique participant ID has been assigned to each participant. These IDs are further used to represent the EEG of the participants. Each class folder further contains two subfolders: A1, A2 (for class A); C1, C2 (for class C); P1, P2 (for class P); and F1, F2 (for class F). All these folders contain the data acquired from the different participants who were shown these images as a csv and edf file. This file structure makes data easier to access and analyse based on the class of visual stimuli images and experimental design employed.

This database, collected at the Children’s Hospital Boston, consists of EEG recordings from pediatric subjects with intractable seizures. Subjects were monitored for up to several days following withdrawal of anti-seizure medication in order to characterize their seizures and assess their candidacy for surgical intervention. The recordings are grouped into 23 cases and were collected from 22 subjects (5 males, ages 3–22; and 17 females, ages 1.5–19).

The dataset contains Electroencephalography (EEG) responses from 20 Indian participants, on 12 songs of different genres (from Indian Classical to Goth Rock). Each session indicates a song by its number.

For the experiment, the participants were indicated to close their eyes indicated by a single beep, and the song was presented to them on speakers. After listening to each song, a double beep was presented, asking them to open their eyes and rate their familiarity and enjoyment to the song. The responses were taken on a scale of 1 to 5, where 1 meant most familiar or most enjoyable, and 5 meant least familiar or least enjoyable.

The events timeline in segmented data must be ignored as these are inherited from rawdata

📘 Overview

This dataset consists of EEG recordings used in the landmark study "Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity" by R.G. Andrzejak et al., published in Physical Review E, 64, 061907 (2001).

It is one of the most widely used benchmark datasets for epileptic seizure detection and EEG signal analysis.

🩺 Motivation

Epilepsy is a chronic neurological disorder that affects millions of people worldwide, particularly in areas with limited medical infrastructure. Electroencephalography (EEG) is the gold standard for diagnosing and monitoring epilepsy. Automatic seizure detection using Brain-Computer Interfaces (BCI) and machine learning can help provide timely diagnosis and improve patient care.

This dataset provides EEG time series signals collected from healthy volunteers and epileptic patients in different conditions, enabling researchers to study and develop algorithms for seizure detection, classification, and nonlinear brain dynamics.

📊 Dataset Structure

The dataset contains five subsets (A–E). Each subset contains 100 EEG segments, each 4096 samples long, recorded at a sampling frequency of 173.61 Hz. All signals are single-channel EEG segments stored as plain text files.

Set Description Subject Type Condition Region No. of Segments A Eyes open Healthy volunteer Non-epileptic Scalp EEG 100 B Eyes closed Healthy volunteer Non-epileptic Scalp EEG 100 C Seizure-free interval Epileptic patient Non-seizure Opposite hemisphere (hippocampal formation) 100 D Seizure-free interval Epileptic patient Non-seizure Epileptogenic zone 100 E During seizure Epileptic patient Seizure activity Epileptogenic zone 100

Each EEG segment represents approximately 23.6 seconds of recording (4096 samples / 173.61 Hz ≈ 23.6 s).

⚙️ File Format

Each EEG segment is stored as a .txt file containing a single column of 4096 numerical values (voltage measurements). For example:

0.123 0.127 0.118 ...

Total files: 500 File naming convention: Z001.txt, Z002.txt, ..., corresponding to sets A–E.

🧩 Applications

This dataset is widely used for:

Epileptic seizure detection and classification

EEG signal denoising and artifact removal

Time–frequency and nonlinear dynamics analysis

Brain-computer interface (BCI) algorithm evaluation

Deep learning and CNN/LSTM-based time series modeling

📈 Sampling Information

Sampling frequency: 173.61 Hz

Duration per segment: 23.6 seconds

Number of samples per segment: 4096

Channels: 1

Signal type: EEG voltage (µV, unspecified units)

🧑‍🔬 Source and Citation

If you use this dataset, please cite the original publication:

Andrzejak, R. G., Lehnertz, K., Rieke, C., Mormann, F., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity. Physical Review E, 64(6), 061907. DOI: 10.1103/PhysRevE.64.061907

⚠️ Usage License

The dataset is publicly available for research and educational purposes only. Commercial or military use is not permitted.

We used a high-density electroencephalography (HD-EEG) system, with 128 customized electrode locations, to record from 17 individuals with migraine (12 female) in the interictal period, and 18 age- and gender-matched healthy control subjects, during visual (vertical grating pattern) and auditory (modulated tone) stimulation which varied in temporal frequency (4 and 6Hz), and during rest. This dataset includes the EEG raw data related to the paper entitled Chamanzar, Haigh, Grover, and Behrmann (2020), Abnormalities in cortical pattern of coherence in migraine detected using ultra high-density EEG. The link to our paper will be made available as soon as it is published online.

This multimodal neuroimaging repository comprises simultaneously and independently acquired Electroencephalographic (EEG) and Magnetic Resonance Imaging (MRI) data, originally presented in our research article: “Preservation of EEG spectral power features during simultaneous EEG-fMRI”. Eyes-closed and eyes-open resting-state EEG data were recorded outside the Magnetic Resonance (MR) environment, inside the MR scanner without image acquisition and during the simultaneous acquisition of functional MRI (fMRI). Additionally, participants performed an eyes opening-eyes closure block-design task during the outside scanner and simultaneous EEG-fMRI conditions. The EEG data acquired outside the scanner provides a reference signal free of MR-related artifacts, the data acquired inside the scanner records the contribution of the ballistocardiographic (BCG) but not the gradient artifact, and the EEG recorded during fMRI acquisition captures both the gradient and the BCG artifacts. Functional MRI data were obtained with and without simultaneous EEG recording, and structural images were collected with and without the subjects wearing the EEG cap. This represents one of the largest open-access EEG-fMRI datasets available and will enable other researchers to: 1) Characterize the impact of gradient and BCG artifacts on EEG data, 2) Assess the effectiveness of novel artifact removal approaches, 3) Perform hardware-setup comparison studies, 4) Address the quality of structural and functional MRI data collected with this EEG cap, and 5) Validate multimodal integrative analysis on simultaneously acquired resting-state and task EEG-fMRI.

This article presents an EEG dataset collected using the EMOTIV EEG 5-Channel Sensor kit during four different types of stimulation: Complex mathematical problem solving, Trier mental challenge test, Stroop colour word test, and Horror video stimulation, Listening to relaxing music. The dataset consists of EEG recordings from 22 subjects for Complex mathematical problem solving, 24 for Trier mental challenge test, 24 for Stroop colour word test, 22 for horror video stimulation, and 20 for relaxed state recordings. The data was collected in order to investigate the neural correlates of stress and to develop models for stress detection based on EEG data. The dataset presented in this article can be used for various applications, including stress management, healthcare, and workplace safety. The dataset provides a valuable resource for researchers and developers working on stress detection using EEG data, while the stress detection method provides a useful tool for evaluating the effectiveness of different stress detection models. Overall, this article contributes to the growing body of research on stress detection and management using EEG data and provides a useful resource for researchers and practitioners working in this field.

This data presents a collection of EEG recordings of seven participants with Intellectual and Developmental Disorder (IDD) and seven Typically Developing Controls (TDC). The data is recorded while the participants observe a resting state and a soothing music stimuli. The data was collected using a high-resolution multi-channel dry-electrode system from EMOTIV called EPOC+. This is a 14-channel device with two reference channels and a sampling frequency of 128 Hz. The data was collected in a noise-isolated room. The participants were informed of the experimental procedure, related risks and were asked to keep their eyes closed throughout the experiment. The data is provided in two formats, (1) Raw EEG data and (2) Pre-processed and clean EEG data for both the group of participants. This data can be used to explore the functional brain connectivity of the IDD group. In addition, behavioral information like IQ, SQ, music apprehension and facial expressions (emotion) for IDD participants is provided in file “QualitativeData.xlsx".

Data Usage: The data is arranged as follows: 1. Raw Data: Data/RawData/RawData_TDC/Music and Rest Data/RawData/RawData_IDD/Music and Rest 2. Clean Data Data/CleanData/CleanData_TDC/Music and Rest Data/CleanData/CleanData_IDD/Music and Rest

The dataset comes along with a fully automated EEG pre-processing pipeline. This pipeline can be used to do batch-processing of raw EEG files to obtain clean and pre-processed EEG files. Key features of this pipeline are : (1) Bandpass filtering (2) Linenoise removal (3) Channel selection (4) Independent Component Analysis (ICA) (5) Automatic artifact rejection All the required files are present in the Pipeline folder.

If you use this dataset and/or the fully automated pre-processing pipeline for your research work, kindly cite these two articles linked to this dataset.

(1) Sareen, E., Singh, L., Varkey, B., Achary, K., Gupta, A. (2020). EEG dataset of individuals with intellectual and developmental disorder and healthy controls under rest and music stimuli. Data in Brief, 105488, ISSN 2352-3409, DOI:https://doi.org/10.1016/j.dib.2020.105488. (2) Sareen, E., Gupta, A., Verma, R., Achary, G. K., Varkey, B (2019). Studying functional brain networks from dry electrode EEG set during music and resting states in neurodevelopment disorder, bioRxiv 759738 [Preprint]. Available from: https://www.biorxiv.org/content/10.1101/759738v1

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.

This dataset has collected for the study of "Robust Detection of Event-Related Potentials in a User-Voluntary Short-Term Imagery Task.

EEG (electroencephalogram) signals, specifically containing extracted features in the time domain, frequency domain, and time-frequency domain. EEG data is often used to study brain activity and can be analyzed using various signal processing techniques.

Let me break down these domains:

1. Time Domain:

   • Features in the time domain are related to the amplitude and time characteristics of the EEG signal.
   • Common time domain features include mean, variance, skewness, kurtosis, and other statistical measures.

2. Frequency Domain:

   • Features in the frequency domain involve analyzing the signal in terms of its frequency components.
   • Common frequency domain features include power spectral density, dominant frequency, and spectral entropy.

3. Time-Frequency Domain:

   • Time-frequency analysis involves studying how the frequency content of the signal changes over time.
   • Common techniques include Short-Time Fourier Transform (STFT), Wavelet Transform, and spectrogram analysis.
   • Features in this domain might include changes in power across different frequency bands over time.

The combination of features from these domains provides a comprehensive understanding of the EEG signals and can be crucial for tasks such as brain-computer interface development, seizure detection, and cognitive state analysis.

"N" for non-match).