EEG Signal Classification using Memristor-based Reservoir Computing System

Md Razuan Hossain, Nicholas X. Armendarez, Ahmed S. Mohamed, Anurag Dhungel, Joseph S. Najem, Md Sakib Hasan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Investigations into aberrant brain activities arising from seizure events utilize time series Electroencephalogram (EEG) signals recorded from various regions within the brain. Reservoir Computing (RC) has emerged as an effective machine learning approach for categorizing temporal signals, offering reduced training expenses compared to conventional recurrent neural networks. Recently, memristors have gained significant traction in neuromorphic applications due to their appealing similarity to biological synapses. As a biological application, EEG signal classification is a compatible problem to analyze using memristor-based RC system. This work aims to classify EEG signals (Epileptic vs. Healthy) using a volatile memristor-based reservoir computing system in simulation platform. We have proposed a new RC framework that helps reduce the feature size in the readout layer for classification and achieves 100% accuracy.

Original languageEnglish (US)
Title of host publicationProceedings of the 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350399189
DOIs
StatePublished - 2023
Event16th IEEE Dallas Circuits and Systems Conference, DCAS 2023 - Denton, United States
Duration: Apr 14 2023Apr 16 2023

Publication series

NameProceedings of the 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023

Conference

Conference16th IEEE Dallas Circuits and Systems Conference, DCAS 2023
Country/TerritoryUnited States
CityDenton
Period4/14/234/16/23

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Instrumentation

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