EEG Dataset for RSVP and P300 Speller Brain-Computer Interfaces

Won, Kyungho; Kwon, Moonyoung; Ahn, Minkyu; Jun, Sung Chan

doi:10.1038/s41597-022-01509-w

Cited by 68EEG and Brain-Computer InterfacesGaze Tracking and Assistive TechnologyEmotion and Mood Recognition

The DOI matches its Crossref record; 37 of the 38 references it cites were found in a registry or an index.

EEG Dataset for RSVP and P300 Speller Brain-Computer Interfaces

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Won, Kyungho, Kwon, Moonyoung, Ahn, Minkyu, Jun, Sung Chan·2022·Scientific Data

DOI 10.1038/s41597-022-01509-wPMC PMC9270361

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Abstract

As attention to deep learning techniques has grown, many researchers have attempted to develop ready-to-go brain-computer interfaces (BCIs) that include automatic processing pipelines. However, to do so, a large and clear dataset is essential to increase the model’s reliability and performance. Accordingly, our electroencephalogram (EEG) dataset for rapid serial visual representation (RSVP) and P300 speller may contribute to increasing such BCI research. We validated our dataset with respect to features and accuracy. For the RSVP, the participants (N = 50) achieved about 92% mean target detection accuracy. At the feature level, we observed notable ERPs (at 315 ms in the RSVP; at 262 ms in the P300 speller) during target events compared to non-target events. Regarding P300 speller performance, the participants (N = 55) achieved about 92% mean accuracy. In addition, P300 speller performance over trial repetitions up to 15 was explored. The presented dataset could potentially improve P300 speller applications. Further, it may be used to evaluate feature extraction and classification algorithm effectively, such as for cross-subjects/cross-datasets, and even for the cross-paradigm BCI model.

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The stimulus frequency differs between the two paradigms: RSVP usually displays with a refresh rate of 5-10 Hz, while the P300 speller flashes for 125 ms and turns off for 62.5 ms until the next sequence flashes.
“Secondly, the stimulus frequency is different for the two paradigms - the RSVP usually displays with a refresh rate of 5-10 Hz, while the P300 speller flashes for 125 ms and turns off for 62.5 ms until the next sequence flashes [18].”
From A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection (2024) · cites this paper as [18]
The Methods section on RSVP reports a 10 Hz refresh rate for this study, but does not establish that RSVP usually runs at 5–10 Hz. The Methods section on the P300 speller states that each stimulus sequence flashed for 125 ms and was off for 62.5 ms before the next sequence flashed. Page numbers are unavailable in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Notable ERPs emerge at 315 ms in the RSVP paradigm whereas occur at 262 ms in the row-column-based P300 speller paradigm during target events compared to nontarget events.
“As mentioned, notable ERPs emerge at 315 ms in the RSVP paradigm whereas occur at 262 ms in the row-column-based P300 speller paradigm during target events compared to nontarget events [18].”
From A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection (2024) · cites this paper as [18]

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Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
Updating P300: An integrative theory of P3a and P3b
Polich, J. (2007) · Clinical Neurophysiology · doi:10.1016/j.clinph.2007.04.019 · Cited by 7,919
EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces
Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aace8c · Cited by 5,115 · cited 2×
“In addition, classification accuracy has been improved by applying deep learning techniques to BCI, and many cross-subject models that use other participants’ training data have been proposed 9–11 .”
Method
Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials.
Farwell, L. A., & Donchin, E. (1988) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(88)90149-6 · Cited by 3,228
BCI2000: A General-Purpose Brain-Computer Interface (BCI) System
Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N., & Wolpaw, J. R. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827072 · Cited by 3,129
The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments
Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.44 · Cited by 1,775
Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis
Delorme, A., Sejnowski, T., & Makeig, S. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.11.004 · Cited by 1,759
Cognitive and biological determinants of P300: an integrative review
Polich, J. M., & Kok, A. (1995) · Biological Psychology · doi:10.1016/0301-0511(95)05130-9 · Cited by 1,656
Review of the BCI Competition IV
Tangermann, M., Müller, K.-R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., Leeb, R., Mehring, C., Miller, K. J., Müller-Putz, G. R., et al. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00055 · Cited by 1,288
High-speed spelling with a noninvasive brain–computer interface
Chen, X., Wang, Y., Nakanishi, M., Gao, X., Jung, T.-P., & Gao, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508080112 · Cited by 969
Characterization of N200 and P300: selected studies of the Event-Related Potential
Patel, S. H., & Azzam, P. N. (2005) · International Journal of Medical Sciences · doi:10.7150/ijms.2.147 · Cited by 794
A comparison of classification techniques for the P300 Speller
Krusienski, D. J., Sellers, E. W., Cabestaing, F., Bayoudh, S., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2006) · Journal of Neural Engineering · doi:10.1088/1741-2560/3/4/007 · Cited by 762
Brain-computer interfaces based on the steady-state visual-evoked response
Middendorf, M. S., McMillan, G. R., Calhoun, G. L., & Jones, K. S. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.847819 · Cited by 685
The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials
Blankertz, B., Muller, K.-R., Curio, G., Vaughan, T. M., Schalk, G., Wolpaw, J. R., Schlogl, A., Neuper, C., Pfurtscheller, G., Hinterberger, T., et al. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.826692 · Cited by 670
Automatic removal of eye movement and blink artifacts from EEG data using blind component separation
Joyce, C. A., Gorodnitsky, I. F., & Marta Kutas, A. (2003) · Psychophysiology · doi:10.1111/j.1469-8986.2003.00141.x · Cited by 594
EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy
Lee, M.-H., Kwon, O., Kim, Y.-J., Kim, H.-K., Lee, Y.-E., Williamson, J., Fazli, S., & Lee, S. (2019) · GigaScience · doi:10.1093/gigascience/giz002 · Cited by 591
A Benchmark Dataset for SSVEP-Based Brain–Computer Interfaces
Wang, Y., Chen, X., Gao, X., & Gao, S. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2627556 · Cited by 507
EEG-BIDS, an extension to the brain imaging data structure for electroencephalography
Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C. L. M., Arnaud Delorme, A., & Oostenveld, R. (2019) · Scientific Data · doi:10.1038/s41597-019-0104-8 · Cited by 451
EEG datasets for motor imagery brain–computer interface
Cho, H., Ahn, M., Ahn, S., Kwon, M., & Jun, S. C. (2017) · GigaScience · doi:10.1093/gigascience/gix034 · Cited by 355
Self-citation
Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks
Kwon, O., Lee, M.-H., Guan, C., & Lee, S. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2946869 · Cited by 352
Robust EEG-based cross-site and cross-protocol classification of states of consciousness
Engemann, D. A., Raimondo, F., King, J.-R., Rohaut, B., Louppe, G., Faugeras, F., Annen, J., Cassol, H., Gosseries, O., Fernandez-Slezak, D., et al. (2018) · Brain · doi:10.1093/brain/awy251 · Cited by 329
Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network
Zhang, K., Robinson, N., Lee, S., & Guan, C. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.12.013 · Cited by 297
MOABB: trustworthy algorithm benchmarking for BCIs
Jayaram, V., & Barachant, A. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aadea0 · Cited by 272
Subject-independent mental state classification in single trials
Fazli, S., Popescu, F., Danóczy, M., Blankertz, B., Müller, K., & Grozea, C. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.06.003 · Cited by 265
Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis
Riccio, A., Simione, L., Schettini, F., Pizzimenti, A., Inghilleri, M., Belardinelli, M. O., Mattia, D., & Cincotti, F. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00732 · Cited by 162
Temporal dynamics of access to consciousness in the attentional blink
Kranczioch, C., Debener, S., Maye, A., & Engel, A. K. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.05.044 · Cited by 143
MOABB: Trustworthy algorithm benchmarking for BCIs
Jayaram, V., & Barachant, A. (2018) · MPG.PuRe (Max Planck Society) · Cited by 139
Cross-Dataset Variability Problem in EEG Decoding With Deep Learning
Xu, L., Xu, M., Ke, Y., An, X., Liu, S., & Ming, D. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00103 · Cited by 128
One-year test–retest reliability of auditory ERPs in young and old adults
Walhovd, K. B., & Fjell, A. M. (2002) · International Journal of Psychophysiology · doi:10.1016/s0167-8760(02)00039-9 · Cited by 115
Brain–Computer Interfaces for Multimodal Interaction: A Survey and Principles
Gürkök, H., & Nijholt, A. (2011) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2011.582022 · Cited by 101
Resting-state EEG activity predicts frontoparietal network reconfiguration and improved attentional performance
Rogala, J., Kublik, E., Krauz, R., & Wróbel, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-61866-7 · Cited by 90
Comparison of designs towards a subject-independent brain-computer interface based on motor imagery
Lotte, F., Guan, C., & Ang, K. K. (2009) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2009.5334126 · Cited by 78
Topographical and temporal stability of the P300
Karniski, W. M., & Blair, R. C. (1989) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(89)90043-6 · Cited by 63
CNN With Large Data Achieves True Zero-Training in Online P300 Brain-Computer Interface
Lee, J., Won, K., Kwon, M., Jun, S. C., & Ahn, M. (2020) · IEEE Access · doi:10.1109/access.2020.2988057 · Cited by 40
Self-citation
P300 Speller Performance Predictor Based on RSVP Multi-feature
Won, K., Kwon, M., Jang, S., Ahn, M., & Jun, S. C. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00261 · Cited by 34
Self-citation
Ensemble SWLDA Classifiers for the P300 Speller
Johnson, G. D., & Krusienski, D. J. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02577-8_60 · Cited by 24
EEG Dataset for RSVP and P300 Speller Brain-Computer Interfaces
Jun, S. C., Won, K., Kwon, M., & Ahn, M. (2022) · Figshare · doi:10.6084/m9.figshare.c.5769449 · Cited by 1
Self-citation

Flagged first, then by citations

1 of the 38 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

Bougrain, L., Saavedra, C. & Ranta, R. Finally, what is the best filter for P300 detection? in TOBI Workshop lll- Tools for Brain-Computer Interaction - 2012 (2012).

Sorted by citations

Multi-Task Collaborative Network: Bridge the Supervised and Self-Supervised Learning for EEG Classification in RSVP Tasks
Li, H., Tang, J., Li, W., Dai, W., Liu, Y., & Zhou, Z. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3357863 · Cited by 26
Enhancing P300 Detection Using a Band-Selective Filter Bank for a Visual P300 Speller
Blanco-Díaz, C. F., Guerrero-Méndez, C. D., & Ruiz-Olaya, A. F. (2023) · IRBM · doi:10.1016/j.irbm.2022.100751 · Cited by 22
A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection
Wang, H., Wang, Z., Sun, Y., Yuan, Z., Xu, T., & Li, J. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3415474 · Cited by 20
“Secondly, the stimulus frequency is different for the two paradigms - the RSVP usually displays with a refresh rate of 5-10 Hz, while the P300 speller flashes for 125 ms and turns off for 62.5 ms until the next sequence flashes [18].”
BackgroundInfluential
EEG-Based Target Detection Using an RSVP Paradigm under Five Levels of Weak Hidden Conditions
Lian, J., Qiao, X., Zhao, Y., Li, S., Wang, C., & Zhou, J. (2023) · Brain Sciences · doi:10.3390/brainsci13111583 · Cited by 15
A Model-Agnostic Feature Attribution Approach to Magnetoencephalography Predictions Based on Shapley Value
Fan, Y., Mao, H., & Li, Q. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3248139 · Cited by 14
Fuzzy deep learning for modeling uncertainty in character recognition using EEG signals
Latifi, F., Hosseini, R., & Sharifi, A. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111575 · Cited by 16
Online continual decoding of streaming EEG signal with a balanced and informative memory buffer
Duan, T., Wang, Z., Li, F., Doretto, G., Adjeroh, D., Yin, Y., & Tao, C. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106338 · Cited by 14
Evaluation of consumer-grade wireless EEG systems for brain-computer interface applications
Lee, S., Kim, M., & Ahn, M. (2024) · Biomedical Engineering Letters · doi:10.1007/s13534-024-00416-w · Cited by 12
Self-citation
Ten quick tips for clinical electroencephalographic (EEG) data acquisition and signal processing
Cisotto, G., & Chicco, D. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2256 · Cited by 12
EEG-Based Emotion Recognition with Combined Fuzzy Inference via Integrating Weighted Fuzzy Rule Inference and Interpolation
Li, F., Yu, F., Shen, L., Li, H., Yang, X., & Shen, Q. (2025) · Mathematics · doi:10.3390/math13010166 · Cited by 13
A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces
Yeom, H. G., Kim, J. S., & Chung, C. K. (2023) · Scientific Data · doi:10.1038/s41597-023-02454-y · Cited by 12
Analyzing and computing humans by means of the brain using Brain-Computer Interfaces - understanding the user – previous evidence, self-relevance and the user’s self-concept as potential superordinate human factors of relevance
Herbert, C. (2024) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1286895 · Cited by 12
An Advanced Healthcare System Where Internet of Things meets Brain-Computer Interface using Event-Related Potential
Parui, S., Samanta, D., & Chakravorty, N. (2023) · International Conference on Distributed Computing and Networking (ICDCN) · doi:10.1145/3571306.3571449 · Cited by 10
A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain’s Electrical Activity Signals
Küçükakarsu, M., Kavsaoğlu, A. R., Alenezi, F. S., Alhudhaif, A., Alwadie, R., & Polat, K. (2023) · Diagnostics · doi:10.3390/diagnostics13030575 · Cited by 8
Efficient automated method to extract EOG artifact by combining Circular SSA with wavelet and unsupervised clustering from single channel EEG
Murali Krishna, Y., & Pamula, V. K. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105455 · Cited by 8
NeuroIDBench: An open-source benchmark framework for the standardization of methodology in brainwave-based authentication research
Chaurasia, A. K., Fallahi, M., Strufe, T., Terhörst, P., & Arias-Cabarcos, P. (2024) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2024.103832 · Cited by 7
AMBER: advancing multimodal brain-computer interfaces for enhanced robustness—A dataset for naturalistic settings
Awais, M. A., Redmond, P. J., Ward, T. E., & Healy, G. F. (2023) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2023.1216440 · Cited by 7
Correlation between Neurophysiological Measures of Consciousness and BCI Performance in a Locked-in Patient
Settgast, T., Zilio, F., Kübler, A., & Northoff, G. (2023) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/bci57258.2023.10078703 · Cited by 6
Signal alignment for cross-datasets in P300 brain-computer interfaces
Song, M., Gwon, D., Jun, S. C., & Ahn, M. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad430d · Cited by 7
Self-citation
EEG channel reconstruction using convolutional neural networks in limited BCIs: A proposed method for neuromarketing applications
Pérez, M. Q., Bernal, S. L., Prat, E. H., Montesano, L., Maimó, L. F., & Celdrán, A. H. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113455 · Cited by 5
The ensemble multi-scale convolution neural network for visual target detection EEG-based brain-computer interfaces
Wang, X., Dang, M., Yang, K., Cui, X., Zhang, D., & Chen, C. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106583 · Cited by 5
A feature enhanced EEG compression model using asymmetric encoding–decoding network *
Wang, X., Zhang, J., & Wu, X. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad48ba · Cited by 4
Semantic image sorting method for RSVP presentation
Wu, H., Li, F., Chu, W., Li, Y., Niu, Y., Shi, G., Zhang, L., & Chen, Y. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad4593 · Cited by 4
A visually-induced optogenetically-engineered system enables autonomous glucose homeostasis in mice
Li, S., Zhou, Y., Kong, D., Miao, Y., Guan, N., Gao, G., Jin, J., & Ye, H. (2024) · Journal of Controlled Release · doi:10.1016/j.jconrel.2024.12.006 · Cited by 4
GSC-ABTA: A group-level brain sources connectivity framework based on adaptive block tensor analysis
Najafabadian, B., Nasrabadi, A. M., & Rashidi, S. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108336 · Cited by 4
An Enhanced Bio-inspired GWO–DE Technique for Efficient Feature Selection in the EEG-RSVP Paradigm
Abinayaa, S., & Sridhar, S. S. (2025) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-00944-y · Cited by 4
Spatial–temporal attention enhanced high-ratio EEG compression using deep variational autoencoder and arithmetic coding
Wang, X., Liao, C., Wu, X., & Zhang, J. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108574 · Cited by 3
Near-Brain Computation: Embedding P300-based BCIs at EEG headset level
Mezzina, G., Walchshofer, M., Guger, C., & Venuto, D. D. (2023) · International Workshop on Advances in Sensors and Interfaces (IWASI) · doi:10.1109/iwasi58316.2023.10164428 · Cited by 3
NeuronLab: BCI framework for the study of biosignals
Bernal, S. L., Lopez, J. L. G. D. C., Beltrán, E. T. M., Pérez, M. Q., Pérez, G. M., & Celdrán, A. H. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128027 · Cited by 3
Task-relevant stimulus design improves P300-based brain–computer interfaces
Kim, J., Cho, Y. S., & Kim, S.-P. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ada0e3 · Cited by 2
TFANet: A Time-Frequency Aware Network With Joint Entropy Coding for High-Ratio EEG Compression
Wang, X., Wu, X., Li, Y., Wu, X., & Zhang, J. (2025) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2025.3590270 · Cited by 2
A Plug-and-Play P300-Based BCI With Zero-Training Application
Kim, J., & Kim, S.-P. (2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2025.3603979 · Cited by 4
Multi-head noise regression for single-channel EEG: estimating ocular and muscle contamination to guide artifact removal
Shaikh, U. Q., Kalra, A. M., Lowe, A., & Niazi, I. K. (2026) · Journal of Neural Engineering · doi:10.1088/1741-2552/ae541d · Cited by 3
Effect of 3D Paradigm on Event-Related Potentials
Korkmaz, O. E. (2022) · Traitement du signal · doi:10.18280/ts.390544 · Cited by 1
Improving subject transfer in EEG classification with divergence estimation
Smedemark-Margulies, N., Wang, Y., Koike–Akino, T., Liu, J., Parsons, K., Bicer, Y., & Deniz Erdogmus, B. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad9777 · Cited by 2
VME-EFD : A novel framework to eliminate the Electrooculogram artifact from single-channel EEGs
Noorbasha, S. K., & kumar, A. (2024) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ad9bb6 · Cited by 1
An explainable hybrid bio-inspired feature selection framework using RSVP-evoked P300 EEG signals for identity authentication
Abinayaa, S., & Sridhar, S. S. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129674 · Cited by 1
EOG-free EEG using successive variational mode decomposition and fuzzy c-means clustering
Sravanthi, G. S., & Sharma, L. D. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109434 · Cited by 1
A Few-Layer Multilayer Perceptron is Worth Attention for EEG Classification in Rapid Serial Visual Presentation Task
Zhang, Z., Zheng, Y., Guo, K., Liang, J., & Dong, M. (2026) · International Journal of Neural Systems · doi:10.1142/s0129065726500309 · Cited by 1
Prior knowledge-driven robust electrode placement method for P300-based brain-computer interfaces
Sadeghi, M., Babaie‐Zadeh, M., & Rivet, B. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110123 · Cited by 1
PyraConvTNet: A pyramid-enhanced DeepConvNet with transformer for novel P300-smart home system
Xu, Q., Li, S., Chen, Z., Jia, X., Thakor, N. V., & Wang, H. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110221 · Cited by 1
[Research progress of brain-computer interface application paradigms based on rapid serial visual presentation].
Sun, J., Meng, J., You, J., Yang, M., Jiang, J., Xu, M., & Ming, D. (2023) · PubMed Central · doi:10.7507/1001-5515.202305061 · Cited by 1
Single-Channel P300 Decomposition Using Detector-Kernel Networks
Kotani, S., Higashi, H., & Tanaka, Y. (2024) · International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) · doi:10.1109/itc-cscc62988.2024.10628346 · Cited by 1
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Zheng, W., Chen, C., Wu, D., & Hao, B. (2024) · IEEE China Conference on System Simulation Technology and its Applications (CCSSTA) · doi:10.1109/ccssta62096.2024.10691814 · Cited by 1
Human Identification Based on Electroencephalogram Analysis When Entering a Password Phrase on a Keyboard
Sulavko, A. Е., & Samotuga, A. (2024) · Applied System Innovation · doi:10.3390/asi7060119 · Cited by 1
Toward the TCN-based Real-Time BCI System for Target Detection
Won, E., Lim, S., Kim, Y., & Dong, S.-Y. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782928 · Cited by 1
Enhancing Brain Signal Generation Through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models
An, Y., Tong, Y., Wang, W., & Su, S. W. (2026) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2026.3696676 · Cited by 1
A multi-subject and multi-session EEG dataset for modelling human visual object recognition
Xue, S., Jin, B., Jiang, J., Guo, L., Jin, Z., Wang, C., & Liu, J. (2025) · Scientific Data · doi:10.1038/s41597-025-04843-x · Cited by 2
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Authors: Won, K., Kwon, M., Ahn, M., & Jun, S. C.
Affiliations: Gwangju Institute of Science and Technology (KR)
Korea Testing Laboratory (KR)
Handong Global University (KR)
Corresponding author: Jun, Sung Chan
ORCID: Won, Kyungho
Kwon, Moonyoung
Ahn, Minkyu
Jun, Sung Chan
Venue: Scientific Data
Publisher: Springer Science and Business Media LLC
Published: 2022-07-08
Volume: 9
Issue: 1
Pages: 388-388
ISSN: 2052-4463 (ISSN-L)
DOI: 10.1038/s41597-022-01509-w
PMC: PMC9270361
OpenAlex: W4284888462
Cited by: 68
References: 38
Citations by year: 2026: 13 · 2025: 15 · 2024: 24 · 2023: 13 · 2022: 3
Type: data paper
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Scientific Data (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Keywords: brain-computer interface, P300 speller, rapid serial visual presentation, EEG dataset, event-related potentials, target detection, feature extraction, classification
MeSH terms: Algorithms, Electroencephalography, Evoked Potentials, Humans, Reproducibility of Results, Event-Related Potentials, P300, Brain-Computer Interfaces

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EEG Dataset for RSVP and P300 Speller Brain-Computer Interfaces

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