A Deep Learning Framework for Trial-Level Assessment of Task-Evoked fNIRS Motor Activation Quality

This manuscript presents a trial-level framework for assessing task-evoked fNIRS motor activation quality using physiologically motivated HbO/HbR pseudo-labelling and temporal deep learning models. The study evaluates CNN-BiLSTM, ResNet1D, and HTCNet architectures under leave-one-subject-out validation using a publicly available hand-gripping fNIRS dataset.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-14
DOI
https://doi.org/10.5281/zenodo.20180290
Primary Topic
EEG and Brain-Computer Interfaces
Type
preprint
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preprint

A Deep Learning Framework for Trial-Level Assessment of Task-Evoked fNIRS Motor Activation Quality

Henry Ndu, Augusta Nneka Ndu
Zenodo (CERN European Organization for Nuclear Research)
EEG and Brain-Computer Interfaces
preprint

A Deep Learning Framework for Trial-Level Assessment of Task-Evoked fNIRS Motor Activation Quality

Henry Ndu, Augusta Nneka Ndu
preprint en

Abstract

This manuscript presents a trial-level framework for assessing task-evoked fNIRS motor activation quality using physiologically motivated HbO/HbR pseudo-labelling and temporal deep learning models. The study evaluates CNN-BiLSTM, ResNet1D, and HTCNet architectures under leave-one-subject-out validation using a publicly available hand-gripping fNIRS dataset.

Zenodo (CERN European Organization for Nuclear Research)
Leeds Beckett University (GB)
EEG and Brain-Computer Interfaces
DOIOpen Access PDFOpenAlex
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A Deep Learning Framework for Trial-Level Assessment of Task-Evoked fNIRS Motor Activation Quality — Henry Ndu, Augusta Nneka Ndu · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS