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

doi:10.1109/DCAS57389.2023.10130258

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

Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings of the 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350399189
DOIs	https://doi.org/10.1109/DCAS57389.2023.10130258
State	Published - 2023
Event	16th IEEE Dallas Circuits and Systems Conference, DCAS 2023 - Denton, United States Duration: Apr 14 2023 → Apr 16 2023

Publication series

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

Conference

Conference	16th IEEE Dallas Circuits and Systems Conference, DCAS 2023
Country/Territory	United States
City	Denton
Period	4/14/23 → 4/16/23

All Science Journal Classification (ASJC) codes

Hardware and Architecture
Electrical and Electronic Engineering
Instrumentation

Access to Document

10.1109/DCAS57389.2023.10130258

Cite this

Hossain, M. R., Armendarez, N. X., Mohamed, A. S., Dhungel, A., Najem, J. S., & Hasan, M. S. (2023). EEG Signal Classification using Memristor-based Reservoir Computing System. In Proceedings of the 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023 (Proceedings of the 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DCAS57389.2023.10130258

Hossain, Md Razuan ; Armendarez, Nicholas X. ; Mohamed, Ahmed S. et al. / EEG Signal Classification using Memristor-based Reservoir Computing System. Proceedings of the 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of the 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023).

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title = "EEG Signal Classification using Memristor-based Reservoir Computing System",

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.",

author = "Hossain, {Md Razuan} and Armendarez, {Nicholas X.} and Mohamed, {Ahmed S.} and Anurag Dhungel and Najem, {Joseph S.} and Hasan, {Md Sakib}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023 ; Conference date: 14-04-2023 Through 16-04-2023",

year = "2023",

doi = "10.1109/DCAS57389.2023.10130258",

language = "English (US)",

series = "Proceedings of the 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of the 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023",

address = "United States",

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Hossain, MR, Armendarez, NX, Mohamed, AS, Dhungel, A, Najem, JS & Hasan, MS 2023, EEG Signal Classification using Memristor-based Reservoir Computing System. in Proceedings of the 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023. Proceedings of the 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023, Institute of Electrical and Electronics Engineers Inc., 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023, Denton, United States, 4/14/23. https://doi.org/10.1109/DCAS57389.2023.10130258

EEG Signal Classification using Memristor-based Reservoir Computing System. / Hossain, Md Razuan; Armendarez, Nicholas X.; Mohamed, Ahmed S. et al.
Proceedings of the 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of the 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - EEG Signal Classification using Memristor-based Reservoir Computing System

AU - Hossain, Md Razuan

AU - Armendarez, Nicholas X.

AU - Mohamed, Ahmed S.

AU - Dhungel, Anurag

AU - Najem, Joseph S.

AU - Hasan, Md Sakib

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

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M3 - Conference contribution

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T3 - Proceedings of the 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023

BT - Proceedings of the 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023

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Hossain MR, Armendarez NX, Mohamed AS, Dhungel A, Najem JS, Hasan MS. EEG Signal Classification using Memristor-based Reservoir Computing System. In Proceedings of the 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings of the 16th IEEE Dallas Circuits and Systems Conference, DCAS 2023). doi: 10.1109/DCAS57389.2023.10130258

EEG Signal Classification using Memristor-based Reservoir Computing System

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