Name: bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset
Published: May 19, 2025
License: https://creativecommons.org/licenses/by/4.0/legalcode

Database Open Access

Boyla Mainsah , Chance Fleeting , Thomas Balmat , Eric Sellers , Leslie Collins

Published: May 19, 2025. Version: 1.0.0

When using this resource, please cite: (show more options)
Mainsah, B., Fleeting, C., Balmat, T., Sellers, E., & Collins, L. (2025). bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (version 1.0.0). PhysioNet. RRID:SCR_007345. https://doi.org/10.13026/0byy-ry86

MLA	Mainsah, Boyla, et al. "bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset" (version 1.0.0). PhysioNet (2025). RRID:SCR_007345. https://doi.org/10.13026/0byy-ry86
APA	Mainsah, B., Fleeting, C., Balmat, T., Sellers, E., & Collins, L. (2025). bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (version 1.0.0). PhysioNet. RRID:SCR_007345. https://doi.org/10.13026/0byy-ry86
Chicago	Mainsah, Boyla, Fleeting, Chance, Balmat, Thomas, Sellers, Eric, and Leslie Collins. "bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset" (version 1.0.0). PhysioNet (2025). RRID:SCR_007345. https://doi.org/10.13026/0byy-ry86
Harvard	Mainsah, B., Fleeting, C., Balmat, T., Sellers, E., and Collins, L. (2025) 'bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset' (version 1.0.0), PhysioNet. RRID:SCR_007345. Available at: https://doi.org/10.13026/0byy-ry86
Vancouver	Mainsah B, Fleeting C, Balmat T, Sellers E, Collins L. bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (version 1.0.0). PhysioNet. 2025. RRID:SCR_007345. Available from: https://doi.org/10.13026/0byy-ry86

Please include the standard citation for PhysioNet: (show more options)
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220. RRID:SCR_007345.

APA	Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220. RRID:SCR_007345.
MLA	Goldberger, A., et al. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220." (2000). RRID:SCR_007345.
CHICAGO	Goldberger, A., L. Amaral, L. Glass, J. Hausdorff, P. C. Ivanov, R. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, and H. E. Stanley. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220." (2000). RRID:SCR_007345.
HARVARD	Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P.C., Mark, R., Mietus, J.E., Moody, G.B., Peng, C.K. and Stanley, H.E., 2000. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220. RRID:SCR_007345.
VANCOUVER	Goldberger A, Amaral L, Glass L, Hausdorff J, Ivanov PC, Mark R, Mietus JE, Moody GB, Peng CK, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220. RRID:SCR_007345.

Abstract

Brain–computer interfaces (BCIs) have wide-ranging applications as solutions for replacing or substituting neural output that has been lost because of severe neuromuscular injury or disease, such as individuals with late-stage amyotrophic lateral sclerosis (ALS). The P300-based BCI is one of the most commonly researched BCI for communication. This BCI dataset is curated from data originally generated from previous visual P300-based BCI speller studies, which include single- and multi-session experiments under a wide range of conditions. The BCI data are provided in an enriched and standardised format with BCI data elements that align with developing IEEE P2731 Working Group standards for BCI data to facilitate reusability. The data files, provided in open European Data Format ‘plus’, contain: i) electroencephalography (EEG) signals; ii) the BCI encoder, target characters and stimulus event markers for P300 event related potential analysis; iii) BCI spelling outcomes and feedback event markers for error related potential analysis; and if available, iv) self-reported demographics (age, sex, race, ethnicity); v) ALS diagnosis and a revised ALS Functional Rating Scale score obtained from medical records; and vi) eye tracker signals.

Background

Current BCIs have relatively low communication rates due to the inherent limitations associated with processing inherently noisy data and highly variable neural signal components of interest to extract the relevant information that is needed to control the BCI. Thus, improving BCI communication efficiency is an area of significant research interest. Part of the development process for any BCI algorithm involves performing simulations with EEG data collected from previous BCI studies to pre-assess various BCI algorithms or strategies under consideration and selecting promising candidates for real-world testing. Acquiring BCI data is time-consuming and expensive; thus, most BCI research groups rely on publicly available datasets to obtain the necessary data to perform simulations with EEG data, rather than conducting real-time BCI studies in-house. However, current publicly available BCI datasets have a limited number of participants, have an under-representation of target BCI end users, mostly use a proprietary file format or a non-standardised data dictionary. Also, there is a lack of serial data collected over several hours and days of BCI use that are needed for simulating long-term evaluation of BCI algorithms.

This open dataset represents a collection of data acquired from BCI research at the Applied Machine Learning Laboratory at Duke University and the Brain-Computer Interface Laboratory at East Tennessee State University. From research supported by the National Institutes of Health (NIH), we have acquired a large amount of single- and multi-session data from P300-based BCI speller [1] studies with abled-bodied individuals and individuals with ALS tested under a wide range of experiment conditions. We have performed data curation, data cleaning, and data engineering to transform proprietary data files into an open and nonproprietary file format and packaged the transformed files into a machine-readable dataset.

Methods

Data Acquisition

Data were recorded using BCI2000 [2], an open-source BCI software platform supported by the NIH. EEG signals were collected non-invasively at 256 Hz using passive gel-based electrodes or active dry electrodes connected to biosignal amplifiers (g.tec medical engineering GmbH) [3]. An electrode impedance check was conducted to ensure low impedance prior to signal recording. Raw EEG signals were bandpass filtered and sometimes, notch filtered, at the biosignal amplifier stage prior to data storage [3].

For hybrid BCI use, eye gaze position, eye position, eye distance from the screen and pupil diameter were collected using a Tobii Pro X2-30 (Tobii AB) infrared eye tracker. The eye tracker was calibrated for each participant prior to BCI use. Eye tracker data were acquired via the EyeTrackerLogger filter in BCI2000 [4] and synchronised to EEG data collection during BCI use. Raw eye tracker position data are pre-processed based on the technical specifications of the Tobii EyeTrackerLogger filter in BCI2000 [4].

Experiment Setup

During an experiment session, participants performed copy-spelling of predefined tokens using the P300 speller application in BCI2000. A user is presented with a set of choices on a speller grid, one of which is assumed to be the user’s desired or target character. In copy-spelling mode, the user spells a pre-defined token (words or number sequence) and is provided a cue on which target character to spell one at a time. To select a new target character, the user focuses on that character as subsets of characters are illuminated on the screen; the illumination of a character subset represents a visual stimulus event. The BCI infers the user’s target character by: i) processing a time window of EEG data time-locked to each stimulus event; ii) using a classifier to detect P300 event-related potentials (ERPs) embedded in EEG data that are elicited in response to the presentation of the target character; and iii) estimating the user’s intended character by matching the character presentation patterns to the detected P300 ERPs with a character decoding function.

In general, an experiment session consists of a calibration phase and a test phase. During the calibration phase, participants perform copy-spelling with no BCI feedback to collect labelled EEG data to train a classifier. During the test phase, the trained BCI classifier is applied and participants performed copy-spelling with BCI feedback to evaluate a new BCI algorithm or strategy. Relevant studies in this BCI dataset and related publications detailing study-specific experiments protocol are listed in Table 1.

**Table 1.** Summary of BCI Studies in the Current bigP3BCI Dataset
Study ID	Related Publication	No. of Participants	Has Target End User?	No. of Sessions	Stimulus Paradigm(s)	Grid Size^§
A	[5]	13		1	RC, CB, RD	9 $\times$ 8
B	[6]	18	Yes	Var	CB	6 $\times$ 6
C	[7]	19		1	CB	9 $\times$ 8
D	[8]	17		1	RC	9 $\times$ 8
E	*	8		1	CB	9 $\times$ 8
F	[9]	10	Yes	3	CB	9 $\times$ 8
G	[10]	20		1	CB	9 $\times$ 8
H	[11]	16		1	CB	9 $\times$ 8
I	ꝉ	13		1	CB, PB	9 $\times$ 8
J	[12]	20		1	RC, PB	6 $\times$ 6
K	[13]	5		1 or 2	CB, AD	9 $\times$ 8
L	[14]	11	Yes	1	RC, CB, CB $_{col}$	6 $\times$ 6
M	[15]	21		1	CD, AD $_{diff}$	9 $\times$ 8
N	[16]	8	Yes	2	CB	6 $\times$ 6
O	[17]	18		2	CB, CB $_{sup}$	9 $\times$ 8
P	[18]	19		2	CB	9 $\times$ 8
Q	[19]	36		3	CB, CB $_{col}$	9 $\times$ 8
R	[20]	20		2	CB	9 $\times$ 8
S1	[21]	10		1	CB	9 $\times$ 8
S2	[21]	24		1	CB	9 $\times$ 8
Abbreviations: AD, Adaptive; AD $_{diff}$ , Adaptive Diffuse; ALS, Amyotrophic Lateral Sclerosis; CB, Checkerboard; CB $_{col}$ , Checkerboard Colour; CB $_{sup}$ , Checkerboard with suppressed characters; No., number; PB, Performance-Based; RD, Random; RC, Row-Column; Var, variable. ^§Grid size is specified as number of rows $\times$ number of columns in a matrix layout. *The experiment protocol of study E is similar to that of study D. ꝉThe experiment protocol of study I is similar to that of study J.

Data Description

Data were extracted from native BCI2000 source files in .dat file format and stored in the open European Data Format plus (EDF+) [22]. The EDF+ data files contain, where available:

EEG signals;
The BCI encoder, target characters and stimulus event markers;
BCI spelling outcomes and feedback event markers;
Self-reported demographics (race, ethnicity, age, sex);
ALS diagnosis and a revised ALS Functional Rating Scale score obtained from medical records, if available; and
Eye tracker signals.

File Hierarchy

Files are organised by study (see Table 1). The base hierarchy levels are Study#\#_$$\SE@@@, where #, $$ and @@@ are the study, subject and session identifier numbers, respectively. The next hierarchy level specifies either the ~\Train or ~\Test phase of the experiment. The subsequent hierarchy levels until the data files depend on the conditions tested during the experiment. File names end with either *Train%%.edf (train phase) or *Test%%.edf (test phase), where %% is the file number.

./
├── StudyA
│   ├── A_01
│   │   └── SE001
│   │       ├── Train
│   │       │   ├── Condition1
│   │       │   │   ├── A_01_SE001CND1_Train01.edf
│   │       │   │   └── A_01_SE001CND1_Train02.edf
│   │       │   └── Condition2
│   │       │       ├── A_01_SE001CND2_Train01.edf
│   │       │       └── A_01_SE001CND2_Train02.edf
│   │       └── Test
│   │           ├── Condition1
│   │           │   ├── A_01_SE001CND1_Test01.edf
│   │           │   └── A_01_SE001CND1_Test02.edf
│   │           └── Condition2
│   │               ├── A_01_SE001CND2_Test01.edf
│   │               └── A_01_SE001CND2_Test02.edf
:   :

EDF+ File Header

Conventionally, the EDF+ file header contains information about the patient identity and the technical specifications of the recorded signals [22, 23]. While our BCI dataset is anonymised, we adapted the patient identification subfields in the EDF+ header to include participant demographics. If available, participant demographics (BCI data level 0 [24]) are contained in the EDF+ file header. We also adapted the patient record identification subfields to include a study identifier, session number and equipment model. Our BCI data dictionary for the EDF+ file header is provided in Table 2.

**Table 2.** Data Dictionary of EDF+ File Header
File Header Field	File Header Subfield	Custom File Header Subfield Label	Values
Patient identification	Patient code	Subject number	A_01, A_02, etc.
	Sex^a		- Male - Female
	Date of birth		- 01-JAN-YYYY, date-shifted in years based on assumed recording start date of 01-JAN-2020. - If age is not available, set to 01-JAN-YYYY. Note: All study participants are adults (at least 18 years).
	Patient name	<Race>_<Ethnicity>_<ALS Status>	Race^a - White - Black/African American - Asian - American Indian or Alaska Native - Native Hawaiian or Other Pacific Islander - X (if not available)
			Ethnicity^a - Hispanic or Latino - Not Hispanic or Latino - X (if not available)
			ALS Severity^b - NonALS - ALS_#, where # is the revised ALS Functional Rating Scale (ALSFRS-R) score ranging from 0 to 48 - ALS_X (if the ALRFRS-R score is not available)
Recording identification	Start date		01-JAN-2020 Note: All recording start dates are set to the same value
	Hospital administration code	<Dataset name>_<Dataset version>_<Study name>	Dataset name: bigP3BCI Dataset version: v#.#.# (following convention for semantic versioning) Study name: StudyA, StudyB, etc. Example: bigP3BCI_v1.0.0_StudyA
	Technician	Session number	SE001, SE002, etc.
	Equipment code	Equipment model
^aSex, race and ethnicity categories are primarily based on definitions outlined by the NIH [25]. ^bThe revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R) is an instrument for evaluating degree of functional impairment in individuals with ALS; the total ALSFRS-R score ranges from 0 (worst) to 48 (best) [26].

EDF+ Data Records

We used the IEEE P2731 Working Group (WG) [24, 27] Standard for a Unified Terminology for Brain-Computer Interfaces for data storage and sharing that are currently under development, which recommends providing BCI data at three levels. BCI data level 0 includes information about data acquisition, physiological signals and individual attributes. BCI data level 1 includes information about the BCI paradigm that is needed to train the BCI machine learning algorithm. BCI data level 2 includes information related to the BCI feedback. The data dictionary for the EDF+ data records organised by the proposed IEEE P2731 BCI data levels are presented in Table 3. Most labels for the EDF+ data records are derived from parameter definitions in the P3SpellerTask and EyeTrackerLogger modules in BCI2000 [2].

**Table 3.** Dictionary for EDF+ Data Records Organised by Developing IEEE P2731 BCI Data Levels [24]
BCI Data Level	Data	Data Label (see table notes)	Values
Level 1: Biosignals	EEG signals	EEG_<electrode> e.g., EEG_Cz	$\mu V$
	Eye tracker data validity	ET<Left/Right>EyeValid	0: Eye tracker data invalid 1: Eye tracker data valid
	Eye gaze position	ET<Left/Right>EyeGaze<X/Y>	- Ratio between 0 and 1, location on the screen the participant is looking - $[X = 0, Y = 0]$ corresponds to the top left of the screen
	Eye position	ET<Left/Right>EyePos<X/Y>	- Ratio between 0 and 1 (eye position relative to the camera in 2D space) - $[X = 0, Y = 0]$ corresponds to the top left of the camera's view
	Eye distance	ET<Left/Right>EyeDist	mm (distance between the screen and the eyes)
	Pupil size	ET<Left/Right>PupilSize	mm
Level 1: BCI Training	Target character trial events	PhaseInSequence	0: Pre-run 1: Pre-trial 2: During trial 3: Post-trial
	Stimulus events (Phase 2)	StimulusBegin	0: stimulus off 1: stimulus on
		StimulusType	0: non-target stimulus event when StimulusBegin = 1 1: target stimulus event
	Character events (Phase 2)	<Character>_<rowIndex>_<column Index> (e.g., K_2_3)	0: character not presented 1: character presented
	Target character (Phase 2)	CurrentTarget	- Index of target current character - Character index = $(r-1)*R + C$ , where $r$ is row index; $R$ is number of rows; $C$ , is number of columns; and $R \times C$ is the grid size. - E.g., index of K_2_3 in a $9 \times 8$ grid is 11.
Level 2: BCI Feedback	Predicted target character (Phase 3)	Selected<Target/Row/Column>	Index of predicted target character/row/column).
	Presented feedback (Phase 3)	DisplayResult	0: Feedback not presented 1: Feedback presented
		FakeFeedback	Index of presented character overriding predicted target character during a character trial.
<⋯> indicates a substring within the angle brackets. <option> in italics indicates a variable substring within the angle brackets. <option1/option2/…/optionN> in solid indicates a variable substring from the fixed set {option1, option2,…, option}. E.g., ET<Left/Right>PupilSize indicates two options, ETLeftPupilSize and ETRightPupilSize, for the pupil size label of the eye tracker (ET).

Usage Notes

This dataset can be used for P300 ERP analysis and error-related potential analysis based on information provided in BCI data levels 1 and 2, respectively (Table 3). EDF+ is an open format and can be read using various proprietary and open platforms, such MATLAB, R, Python and C++ [23].

The BCI studies that generated the data are listed in Table 1 above. It is important to note that there is missing demographic data within files and missing files for some participants.

Release Notes

Version 1.0.0: Initial release of the dataset. The dataset version number is included in the EDF+ file header, see Table 2.

Ethics

All data were recorded during online P300-based BCI studies approved by Institutional Review Boards at Duke University, Duke University Health System and East Tennessee State University. All participants gave informed consent, either by themselves or via a legally authorized representative (for some participants with ALS), prior to data collection and were compensated for their time. The dataset is anonymised to protect participant privacy: all personal identifiable information has been removed and all dates and times (e.g., data collection time, birth date) have been time-shifted.

Acknowledgements

The development of this dataset was funded by the National Institutes of Health under a grant supplement administered by the National Institute on Deafness and Other Communication Disorders at the National Institutes of Health (Grant R21DC018347-02S1). The authors would like to thank Mr. Thomas Napoles, Ms. Katie Kilroy and Dr. John Board at the Duke University Office of Information Technology for their technical support in preparing this BCI dataset.

Conflicts of Interest

The authors have no conflicts of interest to declare.

References

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Topics:
brain-computer interface electroencephalography ieee p2731 working group standard amyotrophic lateral sclerosis p300 speller p300 event related potential oddball paradigm error-related potential

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bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset

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