Multi-source domain generalization with few-shot fine-tuning (MSDG-FT) for cross-dataset EEG mental workload classification

= 0.28), demonstrating simple baselines suffice. Cross-session benefits vary by dataset: CogBCI +15.6%, Neuro2021 +3.5%, indicating task-dependent effectiveness.

Authors

Abinaya G
Dinakaran k

Institutions

Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN)
Saveetha University (IN)
National Institute of Technology Meghalaya (IN)

Publication Details

Journal: MethodsX
Published: 2026-04-15
DOI: https://doi.org/10.1016/j.mex.2026.103913
Primary Topic: EEG and Brain-Computer Interfaces
Type: article
Field-Weighted Citation Impact: 0.00

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article

Multi-source domain generalization with few-shot fine-tuning (MSDG-FT) for cross-dataset EEG mental workload classification

Abinaya G, Dinakaran k

Apr 15, 2026

MethodsX

EEG and Brain-Computer Interfaces

article

Multi-source domain generalization with few-shot fine-tuning (MSDG-FT) for cross-dataset EEG mental workload classification

Abinaya G, Dinakaran k

April 15, 2026article en

Abstract

= 0.28), demonstrating simple baselines suffice. Cross-session benefits vary by dataset: CogBCI +15.6%, Neuro2021 +3.5%, indicating task-dependent effectiveness.

MethodsXVol. 16

Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN), Saveetha University (IN), National Institute of Technology Meghalaya (IN)

Openalex Percentile: Top 20%

EEG and Brain-Computer Interfaces

DOI Open Access PDF OpenAlex

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Multi-source domain generalization with few-shot fine-tuning (MSDG-FT) for cross-dataset EEG mental workload classification — Abinaya G, Dinakaran k · MethodsX (2026) | TGRS Research Map | TGRS