Evaluating Validation Strategies in Motor Imagery EEG: A Full-Cohort GAF-PLV Analysis and Matched Sensitivity Study.

Overview

1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China; (W.C.); (R.L.)
2. Department of Information Science, University of North Texas, Denton, TX 76207, USA
3. School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK

Abstract

Background: Performance estimates in motor-imagery electroencephalography (MI-EEG) can depend strongly on how observations are partitioned for training, model selection, and testing. Random sample- or window-level splitting may place data from the same participant in different folds and therefore does not answer the same question as evaluation on previously unseen participants. Methods: We evaluated a previously developed Gramian angular field–phase-locking value (GAF–PLV) classifier on the retained full cohort (N=105) using binary left-versus-right MI and leave-one-subject-out cross-validation (LOSO). Separately, a predefined, outcome-independent subset (N=30) was used for a matched sensitivity analysis of eight classifiers, binary and four-class tasks, and three validation strategies: random five-fold cross-validation, LOSO, and nested LOSO with subject-grouped inner model selection. Results: In the full-cohort GAF–PLV analysis, mean accuracy was 58.07% ± 8.27% and Macro-F1 was 53.48% ± 11.19%, with substantial between-subject variability. In the predefined matched subset, performance estimates and numerical model rankings changed across validation strategies. For example, the numerically highest binary-accuracy model was ShallowConvNet under random five-fold cross-validation, DeepConvNet under LOSO, and ATCNet under nested LOSO. Conclusions: Random within-cohort classification and generalisation to previously unseen subjects are distinct evaluation targets. MI-EEG reports should state the cohort, partition unit, validation design, and model-selection procedure. Rankings in the multi-model analysis are conditional on the predefined 30-subject subset and common 0.4 s input setting and are not presented as definitive full-cohort or architecture-optimal rankings.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• doi:10.13026/c28g6p, at the source; found in the references
• physionet.org/content/eegmmidb, at PhysioNet; found in “Data Availability Statement”

Data Availability Statement

The EEG Motor Movement/Imagery dataset analysed in this study is publicly available from PhysioNet at https://www.physionet.org/content/eegmmidb/1.0.0/ (accessed on 1 January 2025). No new public dataset was created in this study.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 7 keywords, 2 funders, 33 references.

Cite

This paper

Chang, W., Akbari, H., Sadiq, M. T., Lv, R., & Nawaz, R. (2026). Evaluating Validation Strategies in Motor Imagery EEG: A Full-Cohort GAF-PLV Analysis and Matched Sensitivity Study. Brain sciences, 16(8), 873. https://doi.org/10.3390/brainsci16080873

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