This is the "Matching Pennies" dataset. It was collected as part of a small scale replication project targeting the following reference:

Matthias Schultze-Kraft et al. "Predicting Motor Intentions with Closed-Loop Brain-Computer Interfaces". In: Springer Briefs in Electrical and Computer Engineering. Springer International Publishing, 2017, pp. 79–90.

In brief, it contains EEG data for 7 subjects raising either their left or right hand, thus giving rise to a lateralized readiness potential as measured with the EEG. For details, see the Details about the experiment section.

Please cite as follows:

Appelhoff, S., Sauer, D. & Gill, S. S. Matching Pennies: A Brain Computer Interface Implementation Dataset. Open Science Framework, https://doi.org/10.17605/OSF.IO/CJ2DR (2018).

For more information, see the dataset_description.json file.

The eeg_matchingpennies dataset is made available under the Public Domain Dedication and License (PDDL).

Copyright (c) 2018-2023, Stefan Appelhoff, Daryl Sauer, and Suleman Samuel Gill

See the LICENSE file. A human readable information can be found at:

https://opendatacommons.org/licenses/pddl/1-0/

The dataset is formatted according to the Brain Imaging Data Structure (BIDS). See the dataset_description.json file for the specific version used.

Generally, you can find metadata in the .tsv files and documentation thereof in the accompanying .json files. For example participants.tsv contains metadata about the participants, and participants.json contains documentation about the columns in participants.tsv

An important BIDS definition to consider is the "Inheritance Principle", which is described in the BIDS specification under the following link:

https://bids-specification.readthedocs.io/en/latest/common-principles.html#the-inheritance-principle

In brief, the Inheritance Pinciple states that any metadata file (such as .json, .tsv) may be defined at any directory level, but no more than one applicable file may be defined at a given level [...], and the values from the top level are inherited by all lower levels -- unless they are overridden by a file at the lower level.

For a detailed description of the task, see Schultze-Kraft et al. (2017) and the supplied task-matchingpennies_eeg.json file. What follows is a brief summary.

Participants were seated in front of a computer screen placed on a desk. In front of the participants, two computer mice were glued to the desk: One to the right of the current participant, the other one to their left. On each trial in the experiment, participants were instructed to rest their hands on the desk in front of them, using their left and right ring-fingers to press a button on the computer mouse on their left and right side respectively. Pressing both mice buttons triggered a 3 second countdown that was visually represented as a shrinking horizontal bar on the computer screen. Participants were instructed to lift one (and only one!) hand, thereby releasing a button of a computer mouse, at exactly the time when the countdown reached 0 seconds. After a short delay (see below), a feedback stimulus in the form of a left or right hand was shown on the respective side of the computer screen. When the hand shown on the computer screen and the hand raised by the participant matched, this was counted as a "win" for the computer, else it was counted as a "win" for the participant. Participants were instructed to win as many trials against the computer as possible.

Throughout the experiment, EEG data was recorded and analyzed in a closed-loop BCI online setup. The computer made use of this data to predict which hand a given study participant was about to raise, in order to win more trials.

For more information, you can also consult the events.tsv and events.json files.

Due to an inherent latency of the system, the data used in the online analysis does not necessarily coincide with the offline data marked through EEG triggers. Therefore, after each trial-wise online analysis, the used data was saved separately as an "online chunk". Based on the online chunks of data, it was possible to calculate a latency of each trial with respect to the time difference between when an event happened at the electrode level and when it arrived in digitized format at the classification function. These latencies were calculated by sliding the online chunk of data across the data recording until a perfect match was found. The index of this perfect match is then compared to the timepoint of the event trigger and that difference constitutes the latency. The values are included in the events.tsv files for each subject.

The original data was recorded in .xdf format using labstreaminglayer (https://github.com/sccn/labstreaminglayer). It is stored in the /sourcedata directory. To comply with the BIDS format, the .xdf format was converted to BrainVision format (see the .eeg file for binary eeg data, the .vhdr as a text header filer containing meta data, and the .vmrk as a text file storing the eeg markers).

Note that in the experiment, the only EEG TTL triggers that were recorded are related to the participant raising their left (=value 1) or right (=value 2) hand, and the button release associated with this movement. The onset of the feedback stimulus (drawing of left or right hand shown on the screen) was not recorded, however it can be crudely estimated to be around 50ms + ~10ms + ~8ms, where 50ms were a hardcoded break before getting the online data from LSL, ~10ms was the time to get a prediction from the BCI classifier, but this varied around 10ms, and finally the ~8ms at the end come from the uncertainty of the refresh rate of the screen (60fps = 1 screen every 16.66666ms)

Subjects 1 to 4 participated in the pilot testing only. Their data are not included in this dataset.