Distinct cortical activity associated with varying parameters of imagined movements
Bibliographic record
Abstract
Many EEG- and imagery-based BCI systems may have yielded limited functionality because the type of movements being imagined are too similar and/ or too vaguely characterized. To explore the relevance of distinctive imagined movements in EEG-based BCI systems, the current study employed two tasks that varied across five parameters including the somatotopic representation, side of the body, movement direction, transitiveness, and emotional valence. These two movements were: 1) signing one’s name on a $10-million lottery ticket, and 2) raising one’s left leg. Both movements were also contrasted with a no-movement, resting task. Participants (n = 28) first completed 120 trials of imagery followed by 120 trials of overt execution. Temporal and frequency-based EEG measures were assessed using a 32-electrode actiCAP system, with the main interest being to compare the tasks during the imagination. While the late positive component (LPC) over electrode P3 did not differ during the imagination of the two experimental tasks, LPC associated with both tasks was different from rest. Also, there was a larger motor-related component during the imagination of the leg-raise task at electrode Cz as compared to the sign and rest tasks. More importantly, differences were also found in the frequency domain. Imagination of the signing movement was associated with a significantly stronger pre-movement ERD at electrode C3 as compared to the leg raise and rest tasks. Also, imagination of the leg raise task was associated with a significantly stronger post-movement ERS at electrode Cz as compared to the rest and sign tasks. Critically, no single EEG measure could distinguish between all three conditions (e.g., both movements and rest) during action imagination. Also, the significant frequency measures were most likely associated with the somatotopic representation, side of the body, and transitiveness parameters. In sum, even if frequency-related measures were the most distinguishing features during action imagination, we suggest that vastly different imagined movements should be selected for the design of BCI classifiers.
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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) | 0.001 | 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".