Deep Learning for micro-Electrocorticographic ({\mu}ECoG) Data
Abstract
Machine learning can extract information from neural recordings, e.g., surface EEG, ECoG and {\mu}ECoG, and therefore plays an important role in many research and clinical applications. Deep learning with artificial neural networks has recently seen increasing attention as a new approach in brain signal decoding. Here, we apply a deep learning approach using convolutional neural networks to {\mu}ECoG data obtained with a wireless, chronically implanted system in an ovine animal model. Regularized linear discriminant analysis (rLDA), a filter bank component spatial pattern (FBCSP) algorithm and convolutional neural networks (ConvNets) were applied to auditory evoked responses captured by {\mu}ECoG. We show that compared with rLDA and FBCSP, significantly higher decoding accuracy can be obtained by ConvNets trained in an end-to-end manner, i.e., without any predefined signal features. Deep learning thus proves a promising technique for {\mu}ECoG-based brain-machine interfacing applications.
Cite
@article{arxiv.1810.02584,
title = {Deep Learning for micro-Electrocorticographic ({\mu}ECoG) Data},
author = {Xi Wang and C. Alexis Gkogkidis and Robin T. Schirrmeister and Felix A. Heilmeyer and Mortimer Gierthmuehlen and Fabian Kohler and Martin Schuettler and Thomas Stieglitz and Tonio Ball},
journal= {arXiv preprint arXiv:1810.02584},
year = {2018}
}
Comments
6 pages, 7 figures, 2018 IEEE EMBS conference