Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEG

Soowon Kim; Young Eun Lee; Seo Hyun Lee; Seong Whan Lee

doi:10.21437/Interspeech.2023-1381

Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEG

Soowon Kim
, Young Eun Lee
, Seo Hyun Lee
, Seong Whan Lee

Research output: Contribution to journal › Conference article › peer-review

18 Citations (Scopus)

Abstract

Decoding EEG signals for imagined speech is a challenging task due to the high-dimensional nature of the data and low signal-to-noise ratio. In recent years, denoising diffusion probabilistic models (DDPMs) have emerged as promising approaches for representation learning in various domains. Our study proposes a novel method for decoding EEG signals for imagined speech using DDPMs and a conditional autoencoder named Diff-E. Results indicate that Diff-E significantly improves the accuracy of decoding EEG signals for imagined speech compared to traditional machine learning techniques and baseline models. Our findings suggest that DDPMs can be an effective tool for EEG signal decoding, with potential implications for the development of brain-computer interfaces that enable communication through imagined speech.

Original language	English
Pages (from-to)	1159-1163
Number of pages	5
Journal	Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume	2023-August
DOIs	https://doi.org/10.21437/Interspeech.2023-1381
Publication status	Published - 2023
Event	24rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2023 - Dublin, Ireland Duration: 2023 Aug 20 → 2023 Aug 24

Bibliographical note

Publisher Copyright:
© 2023 International Speech Communication Association. All rights reserved.

Keywords

brain-computer interface
electroencephalography
imagined speech
silent communication
speech recognition

ASJC Scopus subject areas

Software
Signal Processing
Language and Linguistics
Modelling and Simulation
Human-Computer Interaction

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10.21437/Interspeech.2023-1381

Cite this

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title = "Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEG",

abstract = "Decoding EEG signals for imagined speech is a challenging task due to the high-dimensional nature of the data and low signal-to-noise ratio. In recent years, denoising diffusion probabilistic models (DDPMs) have emerged as promising approaches for representation learning in various domains. Our study proposes a novel method for decoding EEG signals for imagined speech using DDPMs and a conditional autoencoder named Diff-E. Results indicate that Diff-E significantly improves the accuracy of decoding EEG signals for imagined speech compared to traditional machine learning techniques and baseline models. Our findings suggest that DDPMs can be an effective tool for EEG signal decoding, with potential implications for the development of brain-computer interfaces that enable communication through imagined speech.",

keywords = "brain-computer interface, electroencephalography, imagined speech, silent communication, speech recognition",

author = "Soowon Kim and Lee, \{Young Eun\} and Lee, \{Seo Hyun\} and Lee, \{Seong Whan\}",

note = "Publisher Copyright: {\textcopyright} 2023 International Speech Communication Association. All rights reserved.; 24rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2023 ; Conference date: 20-08-2023 Through 24-08-2023",

year = "2023",

doi = "10.21437/Interspeech.2023-1381",

language = "English",

volume = "2023-August",

pages = "1159--1163",

journal = "Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",

issn = "2308-457X",

publisher = "International Speech Communication Association",

}

TY - JOUR

T1 - Diff-E

T2 - 24rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2023

AU - Kim, Soowon

AU - Lee, Young Eun

AU - Lee, Seo Hyun

AU - Lee, Seong Whan

PY - 2023

Y1 - 2023

N2 - Decoding EEG signals for imagined speech is a challenging task due to the high-dimensional nature of the data and low signal-to-noise ratio. In recent years, denoising diffusion probabilistic models (DDPMs) have emerged as promising approaches for representation learning in various domains. Our study proposes a novel method for decoding EEG signals for imagined speech using DDPMs and a conditional autoencoder named Diff-E. Results indicate that Diff-E significantly improves the accuracy of decoding EEG signals for imagined speech compared to traditional machine learning techniques and baseline models. Our findings suggest that DDPMs can be an effective tool for EEG signal decoding, with potential implications for the development of brain-computer interfaces that enable communication through imagined speech.

AB - Decoding EEG signals for imagined speech is a challenging task due to the high-dimensional nature of the data and low signal-to-noise ratio. In recent years, denoising diffusion probabilistic models (DDPMs) have emerged as promising approaches for representation learning in various domains. Our study proposes a novel method for decoding EEG signals for imagined speech using DDPMs and a conditional autoencoder named Diff-E. Results indicate that Diff-E significantly improves the accuracy of decoding EEG signals for imagined speech compared to traditional machine learning techniques and baseline models. Our findings suggest that DDPMs can be an effective tool for EEG signal decoding, with potential implications for the development of brain-computer interfaces that enable communication through imagined speech.

KW - brain-computer interface

KW - electroencephalography

KW - imagined speech

KW - silent communication

KW - speech recognition

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U2 - 10.21437/Interspeech.2023-1381

DO - 10.21437/Interspeech.2023-1381

M3 - Conference article

AN - SCOPUS:85171531458

SN - 2308-457X

VL - 2023-August

SP - 1159

EP - 1163

JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Y2 - 20 August 2023 through 24 August 2023

ER -

Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEG

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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