Inner Speech Classification using EEG Signals: A Deep Learning Approach

Bram van den Berg; Sander van Donkelaar; Maryam Alimardani

doi:10.1109/ICHMS53169.2021.9582457

Inner Speech Classification using EEG Signals: A Deep Learning Approach

Bram van den Berg, Sander van Donkelaar, Maryam Alimardani

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Brain computer interfaces (BCIs) provide a direct communication pathway between humans and computers. There are three major BCI paradigms that are commonly employed: motor-imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP). In our study, we sought to expand this by focusing on 'Inner Speech' paradigm using EEG signals. Inner Speech refers to the internalized process of imagining one's own 'voice'. Using a 2D Convolutional Neural Network (CNN) based on the EEGNet architecture, we classified the EEG signals from eight subjects when they internally thought about four different words. Our results showed an average accuracy of 29.7% for word recognition, which is slightly above chance. We discuss the limitations and provide suggestions for future research.

Original language	English
Title of host publication	Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021
Editors	A. Nurnberger, G. Fortino, A. Guerrieri, D. Kaber, D. Mendonca, M. Schilling, Z. Yu
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665401708
DOIs	https://doi.org/10.1109/ICHMS53169.2021.9582457
Publication status	Published - 8 Sept 2021
Externally published	Yes
Event	2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021 - Magdeburg, Germany Duration: 8 Sept 2021 → 10 Sept 2021

Conference

Conference	2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021
Country/Territory	Germany
City	Magdeburg
Period	8/09/21 → 10/09/21

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10.1109/ICHMS53169.2021.9582457

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Berg, B. V. D., Donkelaar, S. V., & Alimardani, M. (2021). Inner Speech Classification using EEG Signals: A Deep Learning Approach. In A. Nurnberger, G. Fortino, A. Guerrieri, D. Kaber, D. Mendonca, M. Schilling, & Z. Yu (Eds.), Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHMS53169.2021.9582457

Berg, Bram van den ; Donkelaar, Sander van ; Alimardani, Maryam. / Inner Speech Classification using EEG Signals : A Deep Learning Approach. Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021. editor / A. Nurnberger ; G. Fortino ; A. Guerrieri ; D. Kaber ; D. Mendonca ; M. Schilling ; Z. Yu. Institute of Electrical and Electronics Engineers Inc., 2021.

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title = "Inner Speech Classification using EEG Signals: A Deep Learning Approach",

abstract = "Brain computer interfaces (BCIs) provide a direct communication pathway between humans and computers. There are three major BCI paradigms that are commonly employed: motor-imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP). In our study, we sought to expand this by focusing on 'Inner Speech' paradigm using EEG signals. Inner Speech refers to the internalized process of imagining one's own 'voice'. Using a 2D Convolutional Neural Network (CNN) based on the EEGNet architecture, we classified the EEG signals from eight subjects when they internally thought about four different words. Our results showed an average accuracy of 29.7% for word recognition, which is slightly above chance. We discuss the limitations and provide suggestions for future research.",

author = "Berg, {Bram van den} and Donkelaar, {Sander van} and Maryam Alimardani",

year = "2021",

month = sep,

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doi = "10.1109/ICHMS53169.2021.9582457",

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booktitle = "Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021",

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

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note = "2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021 ; Conference date: 08-09-2021 Through 10-09-2021",

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Berg, BVD, Donkelaar, SV & Alimardani, M 2021, Inner Speech Classification using EEG Signals: A Deep Learning Approach. in A Nurnberger, G Fortino, A Guerrieri, D Kaber, D Mendonca, M Schilling & Z Yu (eds), Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021. Institute of Electrical and Electronics Engineers Inc., 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021, Magdeburg, Germany, 8/09/21. https://doi.org/10.1109/ICHMS53169.2021.9582457

Inner Speech Classification using EEG Signals: A Deep Learning Approach. / Berg, Bram van den; Donkelaar, Sander van; Alimardani, Maryam.
Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021. ed. / A. Nurnberger; G. Fortino; A. Guerrieri; D. Kaber; D. Mendonca; M. Schilling; Z. Yu. Institute of Electrical and Electronics Engineers Inc., 2021.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Inner Speech Classification using EEG Signals

T2 - 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021

AU - Berg, Bram van den

AU - Donkelaar, Sander van

AU - Alimardani, Maryam

PY - 2021/9/8

Y1 - 2021/9/8

N2 - Brain computer interfaces (BCIs) provide a direct communication pathway between humans and computers. There are three major BCI paradigms that are commonly employed: motor-imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP). In our study, we sought to expand this by focusing on 'Inner Speech' paradigm using EEG signals. Inner Speech refers to the internalized process of imagining one's own 'voice'. Using a 2D Convolutional Neural Network (CNN) based on the EEGNet architecture, we classified the EEG signals from eight subjects when they internally thought about four different words. Our results showed an average accuracy of 29.7% for word recognition, which is slightly above chance. We discuss the limitations and provide suggestions for future research.

AB - Brain computer interfaces (BCIs) provide a direct communication pathway between humans and computers. There are three major BCI paradigms that are commonly employed: motor-imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP). In our study, we sought to expand this by focusing on 'Inner Speech' paradigm using EEG signals. Inner Speech refers to the internalized process of imagining one's own 'voice'. Using a 2D Convolutional Neural Network (CNN) based on the EEGNet architecture, we classified the EEG signals from eight subjects when they internally thought about four different words. Our results showed an average accuracy of 29.7% for word recognition, which is slightly above chance. We discuss the limitations and provide suggestions for future research.

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M3 - Conference contribution

BT - Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021

A2 - Nurnberger, A.

A2 - Fortino, G.

A2 - Guerrieri, A.

A2 - Kaber, D.

A2 - Mendonca, D.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Berg BVD, Donkelaar SV, Alimardani M. Inner Speech Classification using EEG Signals: A Deep Learning Approach. In Nurnberger A, Fortino G, Guerrieri A, Kaber D, Mendonca D, Schilling M, Yu Z, editors, Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021. Institute of Electrical and Electronics Engineers Inc. 2021 doi: 10.1109/ICHMS53169.2021.9582457

Inner Speech Classification using EEG Signals: A Deep Learning Approach

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