Spatial attention shifts to colored items - an EEG-based brain-computer interface
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Abstract
This dataset has been released as a contribution to the Frontiers Research Topic "Datasets for Brain-Computer Interface Applications" https://www.frontiersin.org/research-topics/9784/datasets-for-brain-computer-interface-applications.
Here we provide EEG (electroencephalogram) data recorded during BCI (brain-computer interface) control. The BCI was intended for the decoding of binary decisions from a series of visual stimuli. The decoding task is to determine to which of the simultaneously presented items the participant shifted his/her attention. By determining the visual field in which the target was presented, the subjectively selected target color, which was associated with a “yes”/”no” response, can be determined. 18 Participants were presented with a sequence of ten visual stimuli in which a red "x"-cross and a green "+"-cross were presented simultaneously in the opposite visual hemifields. Participants associated the green cross with the word "yes" and the red cross with the word "no" while responding to questions, which were shown on the screen beforehand. They communicated their response only by directing their attention to the respective cross, while fixating their visual gaze on a cross in the center of the screen. The size and eccentricity of the symbols varied between trials. The online decoded response was presented as feedback on the screen, showing the word "yes" or "no". Before the experiment started, eye movements were recorded to determine the relationship between EOG signal strength and shift of gaze angle. For this purpose, participants were asked to track a cross jumping to peripheral positions and back to the center.
Description
In short, the EEG data are segmented in trials (each corresponding of a series of visual stimuli during which participants responded to a question by their spatial covert attention) and are stored in bciexp.data.
We also provide example scripts that show how the data were analysed as described in the linked publication:
https://gitlab.stimulate.ovgu.de/christoph.reichert/visual-spatial-attention-bci
Erhebungsmethoden und -instrumente:
Visual presentation: Matlab+Cogent Graphics toolbox, TFT Display, Photodiode; EEG recording: Brainamp DC amplifier (BrainProducts), 30 Ag/AgCl electrodes + EOG, reference: right mastoid, 250 Hz sampling rate, 0.1 Hz highpass filter