A Flexible Software-Hardware Framework for Brain EEG Multiple Artifact Identification
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Date
2020
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Citation of Original Publication
Khatwani, Mohit; Rashid, Hasib-Al; Paneliya, Hirenkumar; Horton, Mark; Homayoun, Houman; Waytowich, Nicholas; Hairston, W. David; Mohsenin, Tinoosh; A Flexible Software-Hardware Framework for Brain EEG Multiple Artifact Identification; UMBC Energy Efficient High Performance Computing Lab (2020); http://eehpc.csee.umbc.edu/publications/pdf/2020/A_Flexible_Software_Hardware_Framework_for_Brain_EEG_Multiple_Artifact_Identification.pdf
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0
This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Abstract
This chapter presents an energy efficient and flexible multichannel Electroencephalogram (EEG) artifact identification network and its hardware using
depthwise and separable convolutional neural networks (DS-CNN). EEG signals are
recordings of the brain activities. The EEG recordings that are not originated from
cerebral activities are termed as artifacts. Our proposed model does not need expert
knowledge for feature extraction or pre-processing of EEG data and has a very efficient architecture implementable on mobile devices. The proposed network can be
reconfigured for any number of EEG channel and artifact classes. Experiments were
done with the proposed model with the goal of maximizing the identification accuracy while minimizing the weight parameters and required number of operations.