Superchords: decoding EEG signals in the millisecond range
- Published
- Accepted
- Subject Areas
- Bioengineering, Bioinformatics, Biophysics, Neuroscience, Neurology
- Keywords
- Deep Learning, Artificial Neural Networks, Superchords, EEG, Flatcharts, Neuroscience, Brainprint
- Copyright
- © 2015 Normand et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. Superchords: decoding EEG signals in the millisecond range. PeerJ PrePrints 3:e1265v1 https://doi.org/10.7287/peerj.preprints.1265v1
Abstract
Electroencephalography (EEG) signals’ interpretation is based on waveform analysis, where meaningful information should emerge from a plethora of data. Nonetheless, the continuous increase in computational power and the development of new data processing algorithms in the recent years have put into reach the possibility of analyzing raw EEG signals. Bearing that motivation, the authors propose a new approach using raw data EEG signals and deep learning neural networks, for the classification of motor activities (executed and imagery). The hypothesis to be presented here is: each instantaneous measurement of the raw signal of all EEG channels (superchord) is unique per motor activity regardless the moment of measurement. This study has confirmed the hypothesis (results with accuracy over 80%, mean for 109 subjects), reinforcing the need of further research for the understanding of mental processes.
Author Comment
This is a pre-print version of a submission to PeerJ for review.