Combined Action Observation, Motor Imagery and SSMVEP BCI Enhances Movement Related Cortical Potential
Bibliographic record
Abstract
Congruent action observation (AO) and motor imagery (MI) has been shown to enhance cortical excitability. In this study, a Combined AO, MI and Steady state motion visual evoked potential (SSMVEP) brain computer interface (CAMS BCI) was proposed. The study hypothesized that a short CAMS BCI intervention can alter cortical excitability in the movement related cortical areas manifesting as changes in the movement related cortical potential (MRCP). A 40-minute intervention of gait observation and imagination was performed on nineteen healthy volunteers. The MRCP related to ankle dorsiflexion was measured Pre- and Post- intervention. The analysis compared several MRCP components$(\mathbf{BP}_{1}, \mathbf{BP}_{2}, \mathbf{PN}$, Sloper and Slope2) on five EEG channels$(\mathbf{C}_{1}, \mathbf{C}_{\mathrm{z}}, \mathbf{C}_{2}, \mathbf{FC}_{z}$and$\mathbf{CP}_{\mathrm{z}})$. A consistent increase in the negativity across all MRCP components was observed. Specifically, a significant increase in negativity of the readiness potential was observed in channels$\mathbf{C}_{1}, \mathbf{C}_{\mathbf{z}}$, and$\mathbf{C}_{2}$placed over the primary motor cortex. The results demonstrated that CAMS BCI enhances cortical excitability related to movement preparation and execution. Furthermore, the proposed CAMS BCI not only can evoke SSMVEP and sensorimotor rhythm, but can also enhance MRCP when applied as an intervention. The proposed CAMS BCI paradigm is appealing for neuro-rehabilitation applications and informs future BCI designs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".