Hybrid EEG-fNIRS phoneme classification based on imagined and perceived speech

This study represents an innovative phoneme decoding attempt based on multimodal EEG-fNIRS data, both in terms of imagined speech and perception. Four-class imagined speech classification was primarily driven by EEG features yet outperformed comparable previous studies.

Authors

Silvia Erika Kober (ORCID: https://orcid.org/0000-0003-1836-2627)
Guilherme L Wood
Selina C. Wriessnegger (ORCID: https://orcid.org/0000-0003-4345-7310)
Manuel Hons

Institutions

University of Graz (AT)
Graz University of Technology (AT)
BioTechMed-Graz (AT)

Publication Details

Journal: Frontiers in Neuroergonomics
Published: 2026-02-10
DOI: https://doi.org/10.3389/fnrgo.2026.1696865
Primary Topic: EEG and Brain-Computer Interfaces
Type: article
Field-Weighted Citation Impact: 0.00

Funders

Karl-Franzens-Universität Graz

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article

Hybrid EEG-fNIRS phoneme classification based on imagined and perceived speech

Silvia Erika Kober, Guilherme L Wood, Selina C. Wriessnegger, Manuel Hons

Feb 10, 2026

Frontiers in Neuroergonomics

EEG and Brain-Computer Interfaces

article

Hybrid EEG-fNIRS phoneme classification based on imagined and perceived speech

Silvia Erika Kober, Guilherme L Wood, Selina C. Wriessnegger, Manuel Hons

February 10, 2026article en

Abstract

This study represents an innovative phoneme decoding attempt based on multimodal EEG-fNIRS data, both in terms of imagined speech and perception. Four-class imagined speech classification was primarily driven by EEG features yet outperformed comparable previous studies.

Frontiers in NeuroergonomicsVol. 7

University of Graz (AT), Graz University of Technology (AT), BioTechMed-Graz (AT)

Karl-Franzens-Universität Graz

Peace, Justice and strong institutions

Openalex Percentile: Top 61%

EEG and Brain-Computer Interfaces

DOI Open Access PDF OpenAlex

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