All datasets are open access. You can freely download and use the data.
If there is an associated publication, please make sure to cite it.
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MI
Big Dataset for 11 intuitive movement tasks from single upper Limb
Participants: 25
Signals: 60-channel EEG, 7-channel EMG, 4-channel EOG
Licensor: Korea University
Description: PDF
Citation: J.-H. Jeong, J.-H. Cho, K.-H. Shim, B.-H. Kwon, B.-H. Lee, D.-Y. Lee, D.-H. Lee, and S.-W. Lee, “Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions,” GigaScience, Vol. 9, No. 10, pp. giaa098, 2020.
Big Data of 2-classes MI
Participants: 54
Signals: 62-channel EEG, 4-channel EMG
Licensor: Korea University
Description: PDF
Citation: M.-H. Lee, O.-Y. Kwon, Y.-J. Kim, H.-K. Kim, Y.-E. Lee, J. Williamson, S. Fazli, and S.-W. Lee, “EEG Dataset and OpenBMI Toolbox for Three BCI Paradigms: An Investigation into BCI Illiteracy,” GigaScience, Vol. 8, No. 5, 2019, pp. 1-16.
fNIRS Dataset for Finger/Foot Motor Execution Task
Participants: 30
Signals: 20-channel fNIRS
Licensor: Korea University
Description: PDF
Citation: S. Bak, J. Park, J. Shin, and J. Jeong, “Open-Access fNIRS Dataset for Classification of Unilateral Finger-and Foot-Tapping,” Electronics, Vol. 8, No. 12, 2019, p. 1486.
EEG Dataset for 3-Class MI
Participants: 12
Signals: 30-channel EEG
Licensor: Korea University
Description: PDF
Citation: K.-T. Kim, H.-I. Suk, and S.-W. Lee, “Commanding a Brain-Controlled Wheelchair using Steady-State Somatosensory Evoked Potentials,” IEEE Trans. on Neural Systems & Rehabilitation Engineering, Vol. 26, No. 3, 2018, pp. 654-665.
EEG Dataset for MI-based BCI
Participants: 52
Signals: 64-channel EEG, 2-channel EMG
Licensor: Gwangju Institute of Science and Technology
Description: PDF
Citation: H. Cho, M. Ahn, S. Ahn, K. Kwon, and S. C. Jun, “EEG Datasets for Motor Imagery Brain-Computer Interface,” GigaScience, Vol. 6, No. 1, 2017, pp. 1-8.
EEG Dataset during Conventional MI
Participants: 52
Signals: 70-channel EEG, 6-channel EMG, 1-channel EOG
Licensor: Korea University
Description: PDF
Citation: M.-H. Lee, K.-T. Kim, Y.-J. Kee, J.-H. Jeong, S.-M. Kim, S. Fazli, and S.-W. Lee, “OpenBMI: A Real-Time Data Analysis Toolbox for Brain-Machine Interfaces,” Proc. IEEE International Conference on Systems, Man and Cybernetics, Budapest, Hungary, Oct. 9-12, 2016.
MEG/EEG Dataset for MI-BCI
Participants: 10
Signals: 19-channel EEG, 150-channel MEG
Licensor: Gwangju Institute of Science and Technology
Description: PDF
Citation: M. Ahn, S. Ahn, J. H. Hong, H. Cho, K. Kim, B. S. Kim, J. W. Chang, and S. C. Jun, “Gamma Band Activity Associated with BCI Performance: Simultaneous MEG/EEG Study,” Frontiers in Human Neuroscience, Vol. 7, 2013, article 848.
SSVEP
Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running
Participants: 24
Signals: 32-channel scalp-EEG, 14-channel ear-EEG, 4-channel EOG, and 27-channel IMU
Licensor: Korea University
Description: PDF
Citation: Y.-E. Lee, G.-H. Shin, M. Lee, and S.-W. Lee, “Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running,” Scientific Data, Vol. 8, 2021, pp. 1-12.
EEG Dataset for 9-class SSVEP Based BCI Speller
Participants: 23
Signals: 19-channel EEG
Licensor: Gwangju Institute of Science and Technology
Description: PDF
Citation: It will be updated soon. Please contact admin.
Big Data of 4-classes SSVEP
Participants: 54
Signals: 62-channel EEG, 4-ch channel
Licensor: Korea University
Description: PDF
Citation: M.-H. Lee, O.-Y. Kwon, Y.-J. Kim, H.-K. Kim, Y.-E. Lee, J. Williamson, S. Fazli, and S.-W. Lee, “EEG Dataset and OpenBMI Toolbox for Three BCI Paradigms: An Investigation into BCI Illiteracy,” GigaScience, Vol. 8, No. 5, 2019, pp. 1-16.
A Multi-Day and Multi-Band EEG Dataset for Steady-State Visual Evoked Potential
Participants: 30
Signals: 33-channel EEG
Licensor: Kumoh National Institute of Technology
Description: PDF
Citation: G.-Y. Choi, C.-H. Han, Y.-J. Jung, H.-J. Hwang, “A Multi-Day and Multi-Band Dataset for Steady-State Visual Evoked Potential-based Brain-Computer Interface”, Gigascience, Vol. 8, No. 11, 2019, p. giz133.
SSVEP BCI Game in Real – Exhibition Environment
Participants: 71
Signals: 19-channel ear-EEG
Licensor: Gwangju Institute of Science and Technology
Description: PDF
Citation: It will be updated soon. Please contact the admin.
EEG Dataset for SSVEP using Ear-EEG and Scalp-EEG
Participants: 11
Signals: 18-channel ear-EEG, 8-channel scalp-EEG
Licensor: Korea University
Description: PDF
Citation: N.-S. Kwak and S.-W. Lee, “Error Correction Regression Framework for Enhancing the Decoding Accuracies of Ear-EEG Brain-Computer Interfaces,” IEEE Trans. on Cybernetics, Vol. 50, No. 8, 2020, pp. 3654-3667.
EEG Dataset for SSVEP under Ambulatory Environment
Participants: 7
Signals: 8-channel EEG
Licensor: Korea University
Description: PDF
Citation: N.-S. Kwak, K. Muller, and S.-W. Lee, “A Convolutional Neural Network for Steady State Visual Evoked Potential Classification under Ambulatory Environment,” PLOS ONE, Vol. 12, No. 2, 2017, article 0172578.
EEG Dataset for High-Frequency SSVEP based Speller
Participants: 26
Signals: 32-channel EEG
Licensor: Korea University
Description: PDF
Citation: D.-O. Won, H.-J. Hwang, S. Daehne, K.-R. Muller, and S.-W. Lee, “Effect of Higher Frequency on the Classification of Steady State Visual Evoked Potentials,” Journal of Neural Engineering, Vol. 13, No. 1, 2015, pp. 1-11.
ERP
Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running
Participants: 24
Signals: 32-channel scalp-EEG, 14-channel ear-EEG, 4-channel EOG, and 27-channel IMU
Licensor: Korea University
Description: PDF
Citation: Y.-E. Lee, G.-H. Shin, M. Lee, and S.-W. Lee, “Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running,” Scientific Data, Vol. 8, 2021, pp. 1-12.
EEG(+Ear-EEG)-based ERP detection during walking
Participants: 15
Signals: 32-channel scalp-EEG, 14-channel ear-EEG, 4-channel EOG, 6-channel IMU sensors
Licensor: Korea University
Description: PDF
Citation: It will be updated soon. Please contact the admin.
Big Data of ERP speller
Participants: 54
Signals: 62-channel EEG, 4-channel EMG
Licensor: Korea University
Description: PDF
Citation: M.-H. Lee, O.-Y. Kwon, Y.-J. Kim, H.-K. Kim, Y.-E. Lee, J. Williamson, S. Fazli, and S.-W. Lee, “EEG Dataset and OpenBMI Toolbox for Three BCI Paradigms: An Investigation into BCI Illiteracy,” GigaScience, Vol. 8, No. 5, 2019, pp. 1-16.
EEG Dataset for ERP-based Random Speller
Participants: 20
Signals: 24-channel EEG
Licensor: Korea University
Description: PDF
Citation: M.-H. Lee, K.-T. Kim, Y.-J. Kee, J.-H. Jeong, S.-M. Kim, S. Fazli, and S.-W. Lee, “OpenBMI: A Real-Time Data Analysis Toolbox for Brain-Machine Interface,” Proc. IEEE International Conference on Systems, Man and Cybernetics, Budapest, Hungary, Oct. 9-12, 2016.
EEG Dataset for ERP during Simulated Driving
Participants: 15
Signals: 64-channel EEG, 1-channel EMG
Licensor: Korea University
Description: PDF
Citation: I.-H. Kim, J.-W. Kim, S. Haufe, and S.-W. Lee, “Detection of Braking Intention in Diverse Situations during Simulated Driving based on EEG Feature Combination,” Journal of Neural Engineering, Vol. 12, No. 1, 2015, pp. 1-12.
Cognitive Task
Tourists’ impulse buying behavior measurement at duty-free shops using fNIRS
Participants: 30
Signals: 15-channel fNIRS
Licensor: Korea University
Description: PDF
Citation: It will be updated soon. Please contact the admin.
EEG Data of Human Face Video Observation in Ambiguous Lie/Truth Intent Execution
Participants: 24
Signals: 32-channel EEG
Licensor: Korea University
Description: PDF
Citation: It will be updated soon. Please contact the admin.
Biomarker of Lateralization Index for Stress Calculated from Hemodynamic Responses of fNIRS for Subdividing between Eustress and Distress
Participants: 44
Signals: 15-channel fNIRS
Licensor: Korea University
Description: PDF
Citation: S. Bak, J. Shin, and J. Jeong, “Subdividing Stress Groups into Eustress and Distress Groups Using Laterality Index Calculated from Brain Hemodynamic Response,” Biosensors, Vol. 12, No. 1, 2022, pp. 1-18.
Multi-class imagined speech classification
Participants: 15
Signals: 64-channel EEG
Licensor: Korea University
Description: PDF
Citation: It will be updated soon. Please contact the admin.
Ear-EEG dataset for cognitive states
Participants: 14
Signals: 8-channel EEG
Licensor: Korea University
Description: PDF
Citation: It will be updated soon. Please contact admin.
EEG RSVP Color Identification Task Dataset
Participants: 21
Signals: 31-channel EEG
Licensor: Korea University
Description: PDF
Citation: It will be updated soon. Please contact admin.
EEG Dataset Induced by Watching Emotional Clips
Participants: 18
Signals: 14-channel EEG
Licensor: Yonsei University
Description: PDF
Citation: It will be updated soon. Please contact admin.
EEG Data Acquired from Lie Detection Experimental Paradigm
Participants: 24
Signals: 28-channel EEG
Licensor: Korea University
Description: PDF
Citation: It will be updated soon. Please contact admin.
Ear-EEG Dataset During Mental Arithmetic
Participants: 18
Signals: 25-channel scalp-EEG, 9-channel Ear-EEG
Licensor: Kumoh National Institute of Technology
Description: PDF
Citation: S.-I. Choi, C.-H. Han, G.-Y. Choi, J. Y. Shin, K. S. Song, C.-H. Im, and H.-J. Hwang, ” On the Feasibility of Using Ear-EEG to Develop an Endogenous Brain-Computer Interface”, Sensors, Vol. 18, No. 9, 2018, article 2856.
EEG Data Acquired from Risk Taking Balloon Task (BART)
Participants: 55
Signals: 32-channel EEG
Licensor: Korea University
Description: PDF
Citation: It will be updated soon. Please contact the admin.
EEG Resting State in Real World – Exhibition Environment
Participants: 44
Signals: 19-channel EEG
Licensor: Gwangju Institute of Science and Technology
Description: PDF
Citation: It will be updated soon. Please contact the admin.
Emotional EEG/ECG/Face Dataset using Movie Clip Stimuli
Participants: 10
Signals: 14-channel EEG
Licensor: Yonsei University
Description: PDF
Citation: It will be updated soon. Please contact the admin.
EEG Dataset for Brainwave Entrainment using Auditory Stimulation
Participants: 10
Signals: 19-channel EEG
Licensor: Korea University
Description: It will be updated soon.
Citation: M. Lee, C.-B. Song, G.-H. Shin, and S.-W. Lee, “Possible Effect of Binaural Beat Combined with Autonomous Sensory Meridian Response for Inducing Sleep,” Frontiers in Human Neuroscience, Vol. 13, No. 425, 2019, pp. 1-16.
EEG Dataset during German Vocabulary Learning Task
Participants: 14
Signals: 63-channel EEG
Licensor: Korea University
Description: PDF
Citation: It will be updated soon. Please contact the admin.
EEG/ECG Dataset for Emotion Task
Participants: 80 (10 groups – 8 participants per group)
Signals: 8-channel EEG, 2-channel ECG
Licensor: Gwangju Institute of Science and Technology
Description: PDF
Citation: S. Lee, H. Cho, and S. C. Jun, “Simultaneous Bio-Signal Measurement System for Multiple Users – Development and Validation,” 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala Lumpur, Malaysian, Dec. 12-15, 2017.
EEG/NIRS Dataset during Mental Arithmetic
Participants: 12
Signals: 22-channel EEG, 9-channel NIRS
Licensor: Kumoh National Institute of Technology and Technical University of Berlin
Description: PDF
Citation: J. Y. Shin, K.-R. Muller, and H.-J. Hwang, “Eyes-closed Hybrid Brain-Computer Interface Employing Frontal Brain Activation,” PLOS ONE, Vol. 13, No. 5, 2018, article 0196359.
EEG/NIRS Dataset during Cognitive Tasks
Participants: 26
Signals: 30-channel EEG, 36-channel NIRS
Licensor: Technical University of Berlin and Kumoh National Institute of Technology
Description: PDF
Citation: J. Y. Shin, V. L. Alexander, D.-W. Kim, M. Jan, H.-J. Hwang, and K.-R. Muller, “Simultaneous Acquisition of EEG and NIRS during Cognitive Tasks for an Open Access Dataset,” Scientific Data, Vol. 5, 2018, article 180003.
MEG/EEG Dataset for Verbal-Interaction Hyperscanning Task
Participants: 10 (5 pairs)
Signals: 19-channel EEG, 152-channel MEG
Licensor: Gwangju Institute of Science and Technology
Description: PDF
Citation: S. Ahn, H. Cho, M. Kwon, K. Kim, H. Kwon, B. S. Kim, W. S. Chang, J. W. Chang, and S. C. Jun, “Interbrain Phase Synchronization during Tum-Taking Verbal Interaction?A Hyperscanning Study using Simultaneous EEG/MEG,” Human Brain Mapping, Vol. 39, No. 1, 2017, pp. 171-188.
EEG/ECG/EOG/fNIRS Dataset for Drowsy Driving Task
Participants: 11
Signals: 64-channel EEG, 2-channel ECG, 2-Channel EOG, fNIRS (2-LED, 8-Detector)
Licensor: Gwangju Institute of Science and Technology
Description: PDF
Citation: S. Ahn, T. Nguyen, H. Jang, J. G. Kim, and S. C. Jun, “Exploring Neurophysiological Correlates of Drivers’ Mental Fatigue caused by Sleep Deprivation using Simultaneous EEG, ECG, and fNIRS Data,” Frontiers in Human Neuroscience, Vol. 10, 2016, article 219.
EEG Dataset for Two-Stage Markov Decision Task
Participants: 18
Signals: 64-channel EEG
Licensor: Korea Advanced Institute of Science and Technology
Description: PDF
Citation: S. W. Lee, S. Shimojo, and J.P. O’Doherty, “Neural Computations Underlying Arbitration between Model-Based and Model-free Learning,” Neuron, Vol. 81, No. 3, 2014, pp. 687-699.