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DEMI — Detecting Errors in Motor Imagery

Behaviour + EEG analyses for two related studies on imagined movement accuracy (that is, do we make "errors" during motor imagery?), and how scalp‑level dynamics reflect those processes.

Behavioural task figure   EEG results summary

Left: Behavioural task. Right: EEG results — work in progress; plotting cleanup pending.

At a glance:

  • Behaviour (Published): Participants performed (overtly or imagined) a complex motor task designed to challenge motor acuity. Results support notion of motor imagery accuracy; similarly affected by drivers of overt movement error.
  • EEG: Approach: task‑locked theta/alpha/beta dynamics modeled with hierarchical GAMs (mgcv); contrasts via difference‑of‑smooths; confirmatory cluster‑based permutation inference. Results interpretation pending (manuscript in prep).

What’s inside (brief)

  • /_Scripts/all analysis code (behaviour + EEG).
    Convention: scripts 00–03 = Behaviour paper; scripts 04+ = EEG paper.
  • /external/ — EEG preprocessing pipeline (git submodule, pinned to a specific commit).
  • /legacy/ — dissertation‑era code (read‑only).
  • /media/ — curated figures displayed here (bulk plot dumps are ignored).
  • /_Data/local‑only; only _Data/eeg/BESA-81.csv (channel map) is tracked.
  • renv.lock, .Rprofile — reproducible R environment via renv.

Methods snapshot

  • Behavioural:
    • Behavioural task: single-session touchscreen path-tracing with imagery and overt execution, repeated vs random shapes, varying complexity and stimulus durations.
    • Metrics: overt error = DTW-aligned mean Euclidean deviation; performance = z(speed/error); imagery expected performance obtained by fitting a hierarchical model to overt trials and projecting to imagery.
    • Modelling: Bayesian multilevel regressions (participant random effects; standardized predictors, weakly informative priors) tested self-reported accuracy ~ expected/actual performance with condition interactions; secondary models examined movement time ~ condition × stimulus-time × complexity.
  • EEG: (WIP); manuscript in prep. See scripts under /_Scripts/.

Results / Manuscripts

  • Behaviour:

Ingram, T. G. J., Hurst, A. J., Solomon, J. P., Stratas, A., & Boe, S. G. (2022). Imagined movement accuracy is strongly associated with drivers of overt movement error and weakly associated with imagery vividness. Journal of Experimental Psychology: Human Perception and Performance, 48(12), 1362–1372. https://doi.org/10.1037/xhp0001064

  • EEG: (WIP); manuscript in prep. See scripts under /_Scripts/.

Reproducibility & setup

Reproduce (collapsed)

Clone with submodules

git clone --recurse-submodules https://github.com/toniolio/DEMI.git
cd DEMI

Restore R environment

R -q -e 'install.packages("renv", repos="https://cloud.r-project.org"); renv::restore(); renv::status()'

Data live under _Data/ (local only). See scripts in /_Scripts/ for run order and expected inputs.

Submodule (EEG preprocessing) — details (collapsed)

The pipeline in /external/ is pinned to a specific commit. To intentionally update it:

cd external/DEMI_EEG_Pipeline
git fetch origin
git checkout <new-commit-or-tag>
cd ../..
git add external/DEMI_EEG_Pipeline
git commit -m "external: bump EEG pipeline to <sha|tag>"

Citation

Please cite this repository and the related manuscripts when using the code, figures, or results.

  • Use GitHub’s Cite this repository button (powered by this repo’s CITATION.cff) to export BibTeX/APA/EndNote.
  • The full citation metadata (authors, title, version, release date) live in CITATION.cff.

License

Code in this repository is released under the MIT License. See LICENSE.

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Analysis of neurophysiological data from motor imagery studies (my PhD dissertation).

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