Effect of Transcranial Direct Current Stimulation on an Individual’s Ability to Learn to Control a Brain-Computer Interface
Bibliographic record
Abstract
Purpose: Brain-computer interfaces (BCI) are systems which enable direct communication between a brain and an external device by translating electrical brain activity into meaningful output. This technology can be used by individuals with motor impairments to interact and communicate with their external environment. BCIs based upon manipulating the sensorimotor rhythm (SMR) through motor imagery have lengthy learning periods, which present a significant barrier to using this technology. We hypothesize that this learning period will be significantly reduced by transcranial direct current stimulation (tDCS), which temporarily augments cortical excitability.Methods: Participants were assigned into two groups - the experimental group, which received tDCS, and a control group, which received sham stimulation. Following tDCS, the participants used a SMR-based BCI to move a falling ball to hit targets that appeared on the left or right side of screen. The effect of tDCS was assessed by comparing the overall task accuracy and the SMR change during motor imagery between the two groups .Results: The experimental group was significantly more accurate in controlling the BCI than the control group (p = 0.021); however, there was no significant difference between groups in the SMR change upon motor imagery (p = 0.22). Conclusions. tDCS can be used to improve the performance of healthy individuals learning to use an SMR-based BCI.
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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) | 0.003 | 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".