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Building Brain Invaders: EEG data of an experimental validation

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arxiv 1905.05182 v1 pith:3VZW5Z2P submitted 2019-05-13 cs.HC q-bio.NC

Gijsbrecht Van Veen (GIPSA-VIBS) , Alexandre Barachant (CEA-LETI , GIPSA-VIBS) , Anton Andreev (GIPSA-Services) , Grégoire Cattan (GIPSA-Services) , Pedro Coelho Rodrigues (GIPSA-VIBS) , Marco Congedo (GIPSA-VIBS) This is my paper · ORCID
classification cs.HCq-bio.NC
keywords datadatasetinvadersvisualavailablebraincongedoexperimental
verification ladder T0 review T1 audit T2 compute T3 formal
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We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.2649006 in mat and csv formats. This dataset contains electroencephalographic (EEG) recordings of 25 subjects testing the Brain Invaders (Congedo, 2011), a visual P300 Brain-Computer Interface inspired by the famous vintage video game Space Invaders (Taito, Tokyo, Japan). The visual P300 is an event-related potential elicited by a visual stimulation, peaking 240-600 ms after stimulus onset. EEG data were recorded by 16 electrodes in an experiment that took place in the GIPSA-lab, Grenoble, France, in 2012 (Van Veen, 2013 and Congedo, 2013). Python code for manipulating the data is available at https://github.com/plcrodrigues/py.BI.EEG.2012-GIPSA. The ID of this dataset is BI.EEG.2012-GIPSA.

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Cited by 2 Pith papers

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  1. Removing Neural Signal Artifacts with Autoencoder-Targeted Adversarial Transformers (AT-AT)

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    An autoencoder-gated adversarial transformer denoises EEG-EMG mixtures with reconstruction accuracy comparable to larger published models at a fraction of the model size.

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    cs.LG 2025-01 conditional novelty 5.0 of 10

    A small adversarial denoising autoencoder with logistic covariance rescaling filters EMG noise from EEG competitively while using under 400,000 parameters.

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