**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.

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).

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

This project demonstrates a Brain-Computer Interface (BCI) simulation using real EEG signals to classify binary decisions (Yes/No). It is designed as an accessible prototype for researchers and students to understand and explore cognitive signal processing—without needing expensive hardware.

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.

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.

PCA

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 data set consists of over 240 two-minute EEG recordings obtained from 20 volunteers. Resting-state and auditory stimuli experiments are included in the data. The goal is to develop an EEG-based Biometric system.

The data includes resting-state EEG signals in both cases: eyes open and eyes closed. The auditory stimuli part consists of six experiments; Three with in-ear auditory stimuli and another three with bone-conducting auditory stimuli. The three stimuli for each case are a native song, a non-native song, and neutral music.

In this work, an experimental methodology for the acquisition of EEG signals from volunteer subjects was developed. The volunteers are colleagues and research fellows from ESPOL and patients of the Hospital Luis Vernaza for participating as test subjects. This dataset consists of over 8680 four-second EEG recordings obtained from 60 volunteers.

Equipment: We use the OpenBCI Cyton + Daisy (www.openbci.com) Biosensing Board for EEG signal recording. The OpenBCI equipment has an active bandpass filter in the 5 to 50Hz range, additionally, a notch filter at 60Hz. This non-invasive device operates within a sampling frequency of 125Hz and has 16 dry electrodes with two ground references, distributed in the international 10-10 system. All 16 EEG electrodes were recorded in monopolar configuration, in which the potential of each electrode is compared with a neutral electrode located in both lobes of the ears.

Data Description: Each recording was recorded in a CSV file format, the values of each electrode are in microvolts (uV). In total, each subject generates 124 CSV files in each experiment (run). Some subjects perform two experiments, one executing the motor tasks and the other imagining doing them. The tasks are described below: - Recording a Baseline with Eyes Open (BEO) without any task command: only once at the beginning of each run. - Closing Left Hand (CLH): five times per run. - Closing Right Hand (CRH): five times per run. - Dorsal flexion of Left Foot (DLF): five times per run. - Plantar flexion of Left Foot (PLF): five times per run. - Dorsal flexion of Right Foot (DRF): five times per run. - Plantar flexion of Right Foot (PRF): five times per run. - Resting in between tasks (Rest): after each task, in total 31 files.

CSV file encoding: - Subject ID: Assigned ID to each test subject in order to hide their identity. e.g. Sx, such that x can be any number from 1 to 60. - Repetition number: The participants may perform more than one repetition of the experiment. ExaOnly one subject volunteered to perform up to 4 repetitions. e.g. Rx, such that x can be any repetition number between 1 and 4. - Motor or Motor Imagery Activity: For each repetition, participants are asked to perform first the motor tasks (M) and then the motor imagery tasks (I). & Mx and Ix, where x is the Label of the task performed. - Label: Identifier of the performed task, where 1 is for BEO, 2 for CLH, 3 for CRH, 4 for DLF, 5 for PLF, 6 for DRF, 7 for PRF and finally 8 for Rest. e.g. M2 represents the CLH Motor task. - Task repetition number: Ordinal number of the task repetition. Tasks are presented randomly up to 5 times per run. e.g. S24R1I6_5 is from subject 24, repetition 1, DRF Imagery task. Finally, the number five at the end represents the fifth task repetition in the record.

Additionally, this dataset includes the file "Test_Subject_Annotations.csv", with the demographic information of each of the 60 volunteers, respecting the confidentiality of each individual.

PCA

The datset is comprised of 46(22 commercial adertisement and 24 kannada Music clips) different subjcets EEG data recorded uisng 2 channel EEG device

The dataset folder contaions two sub folder 1. comercial advertisement 1.1 Channel_1(Ch_1) and Channel_2 (Ch_2) :Prefontal Cortex 2. Kannada Musical clips 2.1 channel_1(Ch_1) and Channel_2 (Ch_2) :left Brain

Excel file information : Each file column represneted as number of subjects and row is represnted as features per subjects There are totaly 12 excel files from two channels ( 6 for commercial advertisemnt and 6 for kannda Musical clips).

Subjective self-rating scale

Name
age Gender Have you ever had any health issues? YES NO Have you watched this song/advertisement before? YES NO Please let us know if this advertisement brings up any specific memories for you. YES NO Please Rate the following query from 1 to 10. How funny was the advertisement you watched How sad was the advertisement you watched How Horror was the advertisement you watched How relaxed was the Music you viewed with How Sad was the Music you viewed with How enjoyable was the Music you viewed with Do you think what you just watched was entertaining enough? If you have any comment please write here

Here is the website address for each stimulus that we considered:

ad1: https://www.youtube.com/watch?v=ZzG7duipQ7U&ab_channel=perfettiindia ad2: https://www.youtube.com/watch?v=SfAxUpeVhCg&ab_channel=bo0fhead ad3: https://www.youtube.com/watch?v=HqGsT6VM8Vg&ab_channel=kiddlestix song1: https://www.youtube.com/hashtag/kgfchapter2 song 2: https://www.youtube.com/watch?v=x43w4lLS9E0&ab_channel=AnandAudio Song 3: https://youtube.com/watch?v=Ysf4QRrcLGM&si=EnSIkaIECMiOmarE

For a more comprehensive understanding of the dataset and its background, we kindly ask researchers to refer to our associated manuscript titled:

Entertainment Based Database for Emotion Recognition from EEG Signals, the research article accepted at 3rd International Conference on Applied Intelligence and informatics (AII2023) held in Fostering reproducibility of research results right 29 -31 OCT 2023, DUBAI, UAE. (When utilizing this dataset in your research, please consider citing the following reference)

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

THINGS-EEG

This dataset is a processed version of THINGS-EEG, derived from the paper Bridging the Vision-Brain Gap with an Uncertainty-Aware Blur Prior (CVPR 2025). In this version, the EEG data is stored in float16 format, reducing the storage size by half. The original official dataset can be accessed from the OSF repository. Original official dataset:

A large and rich EEG dataset for modeling human visual object recognition [THINGS-EEG]

  Citation… See the full description on the dataset page: https://huggingface.co/datasets/Haitao999/things-eeg.

Aim: This dataset aims to provide open access of raw EEG signal to the general public. We believe that such fusion of human moods (Relaxation & concentration) shall increase scientific transparency and efficiency, promote the validation of published methods, and foster the development of new algorithms. In addition, publishing research data is becoming more important as public funding agencies are moving towards open research data requirements.

Scenario: The proposed scenario adapted to acquire the brain EEG signals in two different mental status. First while subjects in a relaxed mood, and second in concentration mood. Both of these cognitive stimuli considers as self-induced motivation. The recording period continues till three minutes for each session, as follows: -In the first minute, the subject is asked to relax and sit on a handed chair with eye open looking at a black screen computer of about 40cm far. Until hearing beep sound. -In the second minute, a random picture appear on the screen contain a question or some different objects. The subject is asked to solve the problem or to find common relation links all these objects together. -In last minute, the subject is asked to close his/her eyes and relax again until the beep sound.

Sessions: Fore sessions were recorded for each subject. Such that, first two sessions are done on the same day with 1-2 hours interval, and remaining sessions are done after 2-3 days in the same way. The reason behind this separation is to avoid medium term influences that may subjects have. Each session continues for three minutes. The total recording time for each subject equal to 720 seconds. A small program designed to control the timing and recording procedure of the sessions.

Numbering system: The numbering system is formatted to include both subject enrollment number and trials. First four characters represent the subject number, where last three characters represent the session record number. For example (S001E03) indicate 1st subject and 3rd recording session.

Artifacts: In this experiment, we notice that some subjects accidentally generated internal artifacts. Therefore we intentionally continue recording their brain signals to provide more realistic condition to the experiment and also provide a role for the artifact removal techniques in the pre-processing phase.

Data recording: EEG raw data recorded using EMOTIV EPOC+ device with 14 channels (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, and AF42), plus References in the CMS/DRL noise cancellation configuration P3/P4 locations. The signals were sampled with 250 SPS.

Sample space: The sample space consists of 30 participants (56.6% male and 43.3% female) with ages of 18-40 years. The subjects do not suffer(ing/ed) from any brain problems (mentally or physiologically). 33% of the subjects were smokers and 3% of them were alcoholics. All the subjects are well educated and have at least B.S degree.

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

This dataset contains the EEG resting state-closed eyes recordings from 88 subjects in total.

Participants: 36 of them were diagnosed with Alzheimer's disease (AD group), 23 were diagnosed with Frontotemporal Dementia (FTD group) and 29 were healthy subjects (CN group). Cognitive and neuropsychological state was evaluated by the international Mini-Mental State Examination (MMSE). MMSE score ranges from 0 to 30, with lower MMSE indicating more severe cognitive decline. The duration of the disease was measured in months and the median value was 25 with IQR range (Q1-Q3) being 24 - 28.5 months. Concerning the AD groups, no dementia-related comorbidities have been reported. The average MMSE for the AD group was 17.75 (sd=4.5), for the FTD group was 22.17 (sd=8.22) and for the CN group was 30. The mean age of the AD group was 66.4 (sd=7.9), for the FTD group was 63.6 (sd=8.2), and for the CN group was 67.9 (sd=5.4).

Recordings: Recordings were aquired from the 2nd Department of Neurology of AHEPA General Hospital of Thessaloniki by an experienced team of neurologists. For recording, a Nihon Kohden EEG 2100 clinical device was used, with 19 scalp electrodes (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2) according to the 10-20 international system and 2 reference electrodes (A1 and A2) placed on the mastoids for impendance check, according to the manual of the device. Each recording was performed according to the clinical protocol with participants being in a sitting position having their eyes closed. Before the initialization of each recording, the skin impedance value was ensured to be below 5k?. The sampling rate was 500 Hz with 10uV/mm resolution. The recording montages were anterior-posterior bipolar and referential montage using Cz as the common reference. The referential montage was included in this dataset. The recordings were received under the range of the following parameters of the amplifier: Sensitivity: 10uV/mm, time constant: 0.3s, and high frequency filter at 70 Hz. Each recording lasted approximately 13.5 minutes for AD group (min=5.1, max=21.3), 12 minutes for FTD group (min=7.9, max=16.9) and 13.8 for CN group (min=12.5, max=16.5). In total, 485.5 minutes of AD, 276.5 minutes of FTD and 402 minutes of CN recordings were collected and are included in the dataset.

Preprocessing: The EEG recordings were exported in .eeg format and are transformed to BIDS accepted .set format for the inclusion in the dataset. Automatic annotations of the Nihon Kohden EEG device marking artifacts (muscle activity, blinking, swallowing) have not been included for language compatibility purposes (If this is an issue, please use the preprocessed dataset in Folder: derivatives). The unprocessed EEG recordings are included in folders named: sub-0XX. Folders named sub-0XX in the subfolder derivatives contain the preprocessed and denoised EEG recordings. The preprocessing pipeline of the EEG signals is as follows. First, a Butterworth band-pass filter 0.5-45 Hz was applied and the signals were re-referenced to A1-A2. Then, the Artifact Subspace Reconstruction routine (ASR) which is an EEG artifact correction method included in the EEGLab Matlab software was applied to the signals, removing bad data periods which exceeded the max acceptable 0.5 second window standard deviation of 17, which is considered a conservative window. Next, the Independent Component Analysis (ICA) method (RunICA algorithm) was performed, transforming the 19 EEG signals to 19 ICA components. ICA components that were classified as “eye artifacts” or “jaw artifacts” by the automatic classification routine “ICLabel” in the EEGLAB platform were automatically rejected. It should be noted that, even though the recording was performed in a resting state, eyes-closed condition, eye artifacts of eye movement were still found at some EEG recordings.

A complete analysis of this dataset can be found in the published Data Descriptor paper "A Dataset of Scalp EEG Recordings of Alzheimer’s Disease, Frontotemporal Dementia and Healthy Subjects from Routine EEG", https://doi.org/10.3390/data8060095

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.

Description

We collected EEG signal data from 4 drivers while they were awake and asleep using NeuroSky MindWave sensor. For safety precautions they weren't actually driving while acquiring the signals. Each driver wore the helmet for 5-8 minutes for each label (sleepy, not sleepy) and the signals are acquired approximately every second. The signals are measured in units of microvolts squared per hertz (μV²/Hz). This is a measure of the power of the EEG signal at a particular frequency.

The high values that you are seeing are likely due to the fact that the MindWave sensor is only measuring EEG data from a single location on the forehead. This is in contrast to medical-grade EEG devices, which typically use multiple electrodes placed on different parts of the scalp.

Methodology

The driver would wear the NeuroSky MindWave headset connected by a USB stick to the laptop and we would collect EEG signals from their brain. The NeuroSky mindwave headset is a single channel headset that measures the voltage between an electrode resting on the frontal lobe (forehead) and two electrodes (one ground and one reference) each in contact with one earlobe. The drivers were instructed to be awake or asleep and their EEG signals were recorded accordingly.

Content

• Attention and meditation are calculated from the headset itself, we didn't consider it a reliable feature.
• All EEG signals are divided and specified by the headset. There was no signal preprocessing done. # Inspiration
• This dataset was made for our graduation project. We got an A- (90%).
• Our highest accuracy was 82%, hopefully you can do even better.
• I thought about uploading this dataset since we worked hard on it and it's a waste seeing it idle after we got our grade so hopefully other people might find it useful too! # More references
• here is our proposal document. The similar systems section would be very useful
• you will find some results at the end of the document they were used using this dataset as it used the same helmet as ours although not the same classification and that's why we got poor results but it was for a start

This database contains non-EEG physiological signals collected at Quality of Life Laboratory at University of Texas at Dallas, used to infer the neurological status (including physical stress, cognitive stress, emotional stress and relaxation) of 20 healthy subjects. The data was collected using non-invasive wrist worn biosensors and consists of electrodermal activity (EDA), temperature, acceleration, heart rate (HR), and arterial oxygen level (SpO2).