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).

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 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)

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.

📝 Description

The EEG-Motion Cognitive State Assessment Dataset (EEG-MCSAD) is a multimodal dataset developed for research in remote neurocognitive assessment of elderly and disabled patients. It integrates simulated Electroencephalogram (EEG) data with skeletal motion tracking features, enabling comprehensive cognitive state evaluation such as Alzheimer's disease, Parkinson's disease, vascular dementia, mild cognitive impairment (MCI), HEALTH CONTROL.

The dataset reflects realistic clinical scenarios where patients perform structured cognitive tasks. EEG signals are represented through Power Spectral Density (PSD) features across different brainwave frequency bands, while motion signals capture physical responses such as velocity, acceleration, and balance stability. Additionally, a fused feature score is included to represent the combined contribution of EEG and motion signals.

The dataset is labeled into three cognitive states:

0 = Normal

1 = Mild Impairment

2 = Severe Decline

It is suitable for building and testing machine learning and deep learning models for early detection of cognitive disorders, brain-inspired computing, and assistive healthcare technologies.

🔑 Key Features

Multimodal Representation – Combines EEG and motion data for enriched cognitive assessment.

EEG Features – Power Spectral Density (PSD) values across delta, theta, alpha, beta, and gamma bands.

Motion Features – Joint velocity, joint acceleration, and stability indicators for movement analysis.

Task Performance Scores – Simulated task outcomes representing cognitive performance.

Feature Fusion – Vector-level fused feature score combining EEG and motion signals.

Target Column – Neurocognitive_State with three labeled classes (Normal, Mild Impairment, Severe Decline).

Size & Format – 1345 rows × 11 columns in CSV format.

Applications – Suitable for classification tasks, cognitive decline prediction, multimodal learning, and rehabilitation system design.

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.

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 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.

The neural basis of object recognition and semantic knowledge have been the focus of a large body of research but given the high dimensionality of object space, it is challenging to develop an overarching theory on how brain organises object knowledge. To help understand how the brain allows us to recognise, categorise, and represent objects and object categories, there is a growing interest in using large-scale image databases for neuroimaging experiments. Traditional image databases are based on manually selected object concepts and often single images per concept. In contrast, ‘big data’ stimulus sets typically consist of images that can vary significantly in quality and may be biased in content. To address this issue, recent work developed THINGS: a large stimulus set of 1,854 object concepts and 26,107 associated images (https://things-initiative.org/). In the current paper, we present THINGS-EEG, a dataset containing human electroencephalography responses from 50 subjects to all concepts and 22,248 images in the THINGS stimulus set. The THINGS-EEG dataset provides neuroimaging recordings to a systematic collection of objects and concepts and can therefore support a wide array of research to understand visual object processing in the human brain.

This repository contains the code that was used to perform the analyses described in this paper:

Grootswagers, T., Zhou, I., Robinson, A.K. et al. Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams. Sci Data 9, 3 (2022). https://doi.org/10.1038/s41597-021-01102-7

• THINGS images and concept descriptions obtained from: https://osf.io/jum2f (see also: https://things-initiative.org/)

• The raw data, preprocessed data, and grand-average RDMs are publicly available on Openneuro: https://openneuro.org/datasets/ds003825

• RDMs for single subjects are publicly available on figshare: https://doi.org/10.6084/m9.figshare.14721282 (note: OSF sometimes incorrectly lists this as private)

see the README in the code folder for instructions on how to reproduce the figures in the paper.

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.

EEGEmotions-27 Dataset

EEGEmotions-27 is a dataset of raw EEG recordings collected during affective elicitation experiments. It contains data from 88 participants as they experienced 27 distinct emotional states while watching emotionally evocative video clips.

📁 Dataset Structure

EEG Raw Data

• Location: eeg_raw/
• File Format: {Participant_ID}_{Emotion_ID}.0.txt
  • Example: 12_5.0.txt → Participant 12, Emotion 5 (Anger)
• Data Format: Plain text EEG time series (14 channels from Emotiv X headset, sampled at 256Hz)

Emotion ID Mapping

👤 Participant Information

• File: participants_info.csv
• Fields:
  • Participant ID
  • Gender:
    • 1 = Male
    • 2 = Female
  • Age:
    • 2 = 20s
    • 3 = 30s
    • 4 = 40s
    • 5 = 50s
    • 6 = 60s
  • Nation:
    • 1 = Korean
    • 2 = Vietnamese

📌 Other Files

• emotivX_channels_location.ced → Electrode position file for Emotiv X EEG headset (14 channels).

• video_clips/ → Contains video stimuli used for emotion elicitation.

📜 Citation

If you use this dataset in your research, please cite the following papers:

1. Survey Paper:

Phuong, H.T., Im, E.T., Oh, M.S., & Gim, G.Y. (2025). EEG-Based Emotion Recognition: A Review and Emerging Paths. IEEE Access, 13, 165037-165060. DOI: https://doi.org/10.1109/ACCESS.2025.3610918

@ARTICLE{11168214,
 author={Huy-Tung, Phuong and Eun-Tack, Im and Myeong-Seok, Oh and Gwang-Yong, Gim},
 journal={IEEE Access}, 
 title={EEG-Based Emotion Recognition: A Review and Emerging Paths}, 
 year={2025},
 volume={13},
 number={},
 pages={165037-165060}
}

2. Dataset Paper:

Phuong, H.T., Im, E.T., Oh, M.S., & Gim, G.Y. (2025). EEGEmotions-27: A Large-Scale EEG Dataset Annotated With 27 Fine-Grained Emotion Labels. IEEE Access, 13, 176915-176932. DOI: https://doi.org/10.1109/ACCESS.2025.3620677

@ARTICLE{11202184,
 author={Huy-Tung, Phuong and Eun-Tack, Im and Myeong-Seok, Oh and Gwang-Yong, Gim},
 journal={IEEE Access}, 
 title={EEGEmotions-27: A Large-Scale EEG Dataset Annotated With 27 Fine-Grained Emotion Labels}, 
 year={2025},
 volume={13},
 number={},
 pages={176915-176932}
}

⚖️ License

This dataset is licensed under CC BY-NC 4.0 You are free to use, share, and adapt the data for non-commercial purposes, with proper attribution.

📞 Contact

For questions or collaboration, please contact huytungst@gmail.com.

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

This dataset provides a structured collection of synthetically generated EEG signals designed to support research in automatic stroke detection, classification, and neurodiagnostic modeling. It includes four distinct EEG categories—Ischemic Stroke, Hemorrhagic Stroke, Transient Ischemic Attack (TIA), and Normal Control—represented across 2000 EEG trials.

Each EEG segment is captured as 16 channels, sampled at 256 Hz, with a duration of 4 seconds (1024 samples). The synthetic signals incorporate realistic neurophysiological characteristics such as delta–theta slowing (ischemic), burst-suppression patterns (hemorrhagic), intermittent desynchronization (TIA), and alpha rhythm preservation (normal), enabling the dataset to closely resemble real clinical EEG conditions.

The data is formatted into four separate CSV files, one for each category, making it directly usable for:

EEG classification

Deep learning (EEGNet, CNN, LSTM, Transformer)

Feature extraction studies (PSD, CWT, STFT)

Signal processing and biomedical research

Benchmarking machine learning models

Research papers, thesis work, and simulation studies

This dataset serves as a valuable resource for researchers developing early-stage stroke prediction systems, healthcare analytics models, and EEG-based diagnostic tools.

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Welcome to the FEIS (Fourteen-channel EEG with Imagined Speech) dataset.

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The FEIS dataset comprises Emotiv EPOC+ [1] EEG recordings of:

* 21 participants listening to, imagining speaking, and then actually speaking
 16 English phonemes (see supplementary, below)

* 2 participants listening to, imagining speaking, and then actually speaking
 16 Chinese syllables (see supplementary, below)

For replicability and for the benefit of further research, this dataset
includes the complete experiment set-up, including participants' recorded
audio and 'flashcard' screens for audio-visual prompts, Lua script and .mxs
scenario for the OpenVibe [2] environment, as well as all Python scripts
for the preparation and processing of data as used in the supporting
studies (submitted in support of completion of the MSc Speech and Language
Processing with the University of Edinburgh):

* J. Clayton, "Towards phone classification from imagined speech using
 a lightweight EEG brain-computer interface," M.Sc. dissertation,
 University of Edinburgh, Edinburgh, UK, 2019.

* S. Wellington, "An investigation into the possibilities and limitations
 of decoding heard, imagined and spoken phonemes using a low-density,
 mobile EEG headset," M.Sc. dissertation, University of Edinburgh,
 Edinburgh, UK, 2019.

Each participant's data comprise 5 .csv files -- these are the 'raw'
(unprocessed) EEG recordings for the 'stimuli', 'articulators' (see
supplementary, below) 'thinking', 'speaking' and 'resting' phases per epoch
for each trial -- alongside a 'full' .csv file with the end-to-end
experiment recording (for the benefit of calculating deltas).

To guard against software deprecation or inaccessability, the full repository
of open-source software used in the above studies is also included.

We hope for the FEIS dataset to be of some utility for future researchers,
due to the sparsity of similar open-access databases. As such, this dataset
is made freely available for all academic and research purposes (non-profit).

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REFERENCING

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If you use the FEIS dataset, please reference:

* S. Wellington, J. Clayton, "Fourteen-channel EEG with Imagined Speech
 (FEIS) dataset," v1.0, University of Edinburgh, Edinburgh, UK, 2019.
 doi:10.5281/zenodo.3369178

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LEGAL

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The research supporting the distribution of this dataset has been approved by
the PPLS Research Ethics Committee, School of Philosophy, Psychology and
Language Sciences, University of Edinburgh (reference number: 435-1819/2).

This dataset is made available under the Open Data Commons Attribution License
(ODC-BY): http://opendatacommons.org/licenses/by/1.0

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ACKNOWLEDGEMENTS

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The FEIS database was compiled by:

Scott Wellington (MSc Speech and Language Processing, University of Edinburgh)
Jonathan Clayton (MSc Speech and Language Processing, University of Edinburgh)

Principal Investigators:

Oliver Watts (Senior Researcher, CSTR, University of Edinburgh)
Cassia Valentini-Botinhao (Senior Researcher, CSTR, University of Edinburgh)

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METADATA

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For participants, dataset refs 01 to 21:

01 - NNS
02 - NNS
03 - NNS, Left-handed
04 - E
05 - E, Voice heard as part of 'stimuli' portions of trials belongs to
   particpant 04, due to microphone becoming damaged and unusable prior to
   recording
06 - E
07 - E
08 - E, Ambidextrous
09 - NNS, Left-handed
10 - E
11 - NNS
12 - NNS, Only sessions one and two recorded (out of three total), as
   particpant had to leave the recording session early
13 - E
14 - NNS
15 - NNS
16 - NNS
17 - E
18 - NNS
19 - E
20 - E
21 - E

E = native speaker of English
NNS = non-native speaker of English (>= C1 level)

For participants, dataset refs chinese-1 and chinese-2:

chinese-1 - C
chinese-2 - C, Voice heard as part of 'stimuli' portions of trials belongs to
      participant chinese-1

C = native speaker of Chinese

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SUPPLEMENTARY

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Under the international 10-20 system, the Emotiv EPOC+ headset 14 channels:

F3 FC5 AF3 F7 T7 P7 O1 O2 P8 T8 F8 AF4 FC6 F4

The 16 English phonemes investigated in dataset refs 01 to 21:

/i/ /u:/ /æ/ /ɔ:/ /m/ /n/ /ŋ/ /f/ /s/ /ʃ/ /v/ /z/ /ʒ/ /p /t/ /k/

The 16 Chinese syllables investigated in dataset refs chinese-1 and chinese-2:

mā má mǎ mà mēng méng měng mèng duō duó duǒ duò tuī tuí tuǐ tuì

All references to 'articulators' (e.g. as part of filenames) refer to the
1-second 'fixation point' portion of trials. The name is a layover from
preliminary trials which were modelled on the KARA ONE database
(http://www.cs.toronto.edu/~complingweb/data/karaOne/karaOne.html) [3].

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[1] Emotiv EPOC+. https://emotiv.com/epoc. Accessed online 14/08/2019.

[2] Y. Renard, F. Lotte, G. Gibert, M. Congedo, E. Maby, V. Delannoy,
  O. Bertrand, A. Lécuyer. “OpenViBE: An Open-Source Software Platform
  to Design, Test and Use Brain-Computer Interfaces in Real and Virtual
  Environments”, Presence: teleoperators and virtual environments,
  vol. 19, no 1, 2010.

[3] S. Zhao, F. Rudzicz. "Classifying phonological categories in imagined
  and articulated speech." In Proceedings of ICASSP 2015, Brisbane
  Australia, 2015.

"N" for non-match).

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.

Artificial intelligence (AI) based automated epilepsy diagnosis has aimed to ease the burden of manual detection, prediction, and management of seizure and epilepsy-specific EEG signals for medical specialists. With increasing open-source, raw, and large EEG datasets, there is a need for data standardization of patient and seizure-sensitive AI analysis with reduced redundant information. This work releases a balanced, annotated, fixed time and length meta-data of CHB-MIT Scalp EEG database v1.0.0.0.

The work releases patient-specific (inter and intra) and patient non-specific EEG data extracted using specific time stamps of ictal, pre-ictal, post-ictal, peri-ictal, and non-seizure EEG provided in the original dataset (annotations). Further details of this metadata can be found in the provided csv file (CHB-MIT DB timestamp.csv). The released EEG data is available in csv format and class labels are provided in the last row of the csv files. Data of ch06, ch12, ch23, and ch24 in patient-specific and chb24_11 in patient non-specific have not been included. The importance of peri-ictal EEGs has been elucidated in Handa, P., & Goel, N. (2021). Peri‐ictal and non‐seizure EEG event detection using generated metadata. Expert Systems, e12929.

Latest update: The dataset has been moved to: Data of Meta-EEGs

Please only download and cite the latest version!

This dataset consists of electroencephalography (EEG) signals acquired with a 124 EEG ANT-Neuro device.

Participants and Sessions

There are 13 participants included, ten performed one session and three performed two sessions. All participants had normal or corrected vision and hearing, apart from sub-16.

Task

The task consisted of imagining and perceiving stimuli from three modalities; visual pictorial, visual orthographic (writing) or auditory. Each of the stimuli belonged to one of three categories: guitar, flower and penguin. These categories were selected based on being semantically dissimilar to one another, and because there were all of 2 syllables.

Dataset Versions

The dataset provided consists of the raw EEG data, a pre-processed version, and an epoched version.

Attention deficit hyperactivity disorder (ADHD) is a condition that impacts a large number of people around the world and is characterized as a neurodevelopmental disorder. It can affect individuals of all ages, but it typically begins during childhood and can persist into adulthood. One of the diagnostic criteria of ADHD is abnormal electrical activity in the brain, as measured by Electroencephalography (EEG), particularly in frontal and central regions. We present a dataset that we collected from 79 participants, including 42 healthy adults and 37 adults with ADHD (age 20-68 years; male/female: 56/23). The EEG signals were recorded in four different states, including resting state with eyes open, eyes closed, cognitive challenge, and listening to omni harmonic. The dataset contains EEG signals recorded from five channels, including O1, F3, F4, Cz, and Fz. The sampling rate of data is 256 Hz. The participants were seated comfortably in a chair and asked to remain as calm as possible during the recordings. All participants provided informed consent, and the study protocol was approved by the ethics committee. This dataset can serve as a valuable resource for researchers working on ADHD and EEG.

This dataset comprises 128-channel EEG recordings from 14 right-handed participants, including 7 healthy controls and 7 patients with clinically confirmed left-hemisphere intracerebral hemorrhagic stroke. The recordings were obtained during three sensorimotor task conditions designed to examine neural responses to multimodal tactile-motor stimulation: vibration stimulation, active finger bending, and their combination.The EEG signals were recorded at a sampling rate of 1024 Hz using a 128-channel cap arranged according to the international 10-05 system. Mechanical vibration was delivered to the right hand using a custom-designed vibrating glove. In healthy participants, five stimulation frequencies (15, 40, 80, 120, and 200 Hz) were tested for alpha-band ERD/ERS-based frequency screening, and 40 Hz was used as the standardized vibration frequency in the subsequent comparative paradigm.This dataset supports analyses of cortical activation and brain network dynamics under unimodal and multimodal stimulation, including ERD/ERS analysis, phase lag index (PLI)-based functional connectivity analysis, and EEG microstate analysis. It may facilitate studies of sensorimotor integration, stroke-related network reorganization, and the development of EEG-based biomarkers for neurorehabilitation research.All data were collected with informed consent and have been anonymized for sharing and reuse.