Development of brain-computer interface: preliminary results
Bibliographic record
Abstract
Reports on the development of an experimental setup and initial results for the evaluation of electronencephalogram (EEG) signals for control and communication. In our experiments, we recorded and analyzed surface EEG signals above sensory-motor areas, while the subjects were attempting to use only mental activities to modulate their EEG signals, resulting in desired movements of an animated object on the feedback computer screen. To discover possible communication channels based on EEG signals, we asked our subjects to determine which mental activity produced reproducible control actions. We calculated the power spectral density (PSD) of the recorded EEG signals and used sensory-motor rhythm (SMR) as the feedback-generating variable, i.e. the object's movement direction and speed depended on the integrated PSD in the SMR range. In our initial experiments with three subjects, during the first session all three demonstrated the ability to determine which mental activity resulted in the desired movements. Off-line analysis showed that 60% classification accuracy can be achieved using a linear classifier on a subject with only two previous training sessions. These results are very encouraging and provide a good basis for the development of a direct brain-computer interface using cognitively modulated EEG signals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".