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A Flexible Multichannel EEG Artifact Identification Processor using Depthwise-Separable Convolutional Neural Networks

Published: 15 April 2021 Publication History

Abstract

This article presents an energy-efficient and flexible multichannel Electroencephalogram (EEG) artifact identification network and its hardware using depthwise and separable convolutional neural networks. EEG signals are recordings of the brain activities. EEG recordings that are not originated from cerebral activities are termed 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. Our proposed network achieves 93.14% classification accuracy using an EEG dataset collected by 64-channel BioSemi ActiveTwo headsets, averaged across 17 patients and 10 artifact classes. Our hardware architecture is fully parameterized with number of input channels, filters, depth, and data bit-width. The number of processing engines (PE) in the proposed hardware can vary between 1 to 16, providing different latency, throughput, power, and energy efficiency measurements. We implement our custom hardware architecture on Xilinx FPGA (Artix-7), which on average consumes 1.4 to 4.7 mJ dynamic energy with different PE configurations. Energy consumption is further reduced by 16.7× implementing on application-specified integrated circuit at the post layout level in 65-nm CMOS technology. Our FPGA implementation is 1.7 × to 5.15 × higher in energy efficiency than some previous works. Moreover, our Application-Specified Integrated Circuit implementation is also 8.47 × to 25.79 × higher in energy efficiency compared to previous works. We also demonstrated that the proposed network is reconfigurable to detect artifacts from another EEG dataset collected in our lab by a 14-channel Emotiv EPOC+ headset and achieved 93.5% accuracy for eye blink artifact detection.

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Information

Published In

cover image ACM Journal on Emerging Technologies in Computing Systems
ACM Journal on Emerging Technologies in Computing Systems  Volume 17, Issue 2
Hardware and Algorithms for Efficient Machine Learning
April 2021
360 pages
ISSN:1550-4832
EISSN:1550-4840
DOI:10.1145/3446841
  • Editor:
  • Ramesh Karri
Issue’s Table of Contents
© 2021 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Publication History

Published: 15 April 2021
Accepted: 01 September 2020
Revised: 01 August 2020
Received: 01 May 2020
Published in JETC Volume 17, Issue 2

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Author Tags

  1. ASIC
  2. EEG
  3. FPGA
  4. artifact
  5. depthwise separable CNN
  6. flexible reconfigurable hardware

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  • Refereed

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  • Army Research Laboratory and was accomplished under Cooperative

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  • (2023)Mental Pressure Recognition Method Based on CNN Model and EEG Signal under Cross SessionSymmetry10.3390/sym1506117315:6(1173)Online publication date: 30-May-2023
  • (2023)Computer Aided Detection of Dominant Artifacts in Ear-EEG Signal2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53992.2023.10394374(4423-4428)Online publication date: 1-Oct-2023
  • (2023)Flex-SNN: Spiking Neural Network on Flexible SubstrateIEEE Sensors Letters10.1109/LSENS.2023.32719887:5(1-4)Online publication date: May-2023
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  • (2023)Ocular Artefact Removal from Electroencephalogram Signals: A Review2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)10.1109/ICCCIS60361.2023.10425613(437-443)Online publication date: 3-Nov-2023
  • (2023)EEG and fMRI Artifact Detection Techniques: A Survey of Recent DevelopmentsSN Computer Science10.1007/s42979-023-01959-y4:5Online publication date: 15-Jul-2023
  • (2022)E2EdgeAI: Energy-Efficient Edge Computing for Deployment of Vision-Based DNNs on Autonomous Tiny Drones2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)10.1109/SEC54971.2022.00077(504-509)Online publication date: Dec-2022
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