An autoencoder-gated adversarial transformer denoises EEG-EMG mixtures with reconstruction accuracy comparable to larger published models at a fraction of the model size.
Building Brain Invaders: EEG data of an experimental validation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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.
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Removing Neural Signal Artifacts with Autoencoder-Targeted Adversarial Transformers (AT-AT)
An autoencoder-gated adversarial transformer denoises EEG-EMG mixtures with reconstruction accuracy comparable to larger published models at a fraction of the model size.