Fractal dimension-based EEG biofeedback system
Bibliographic record
Abstract
Biofeedback plays an increasingly important role in mainstream computer applications, including hands-free human-machine interaction. The most obvious use of this technology is to help disabled people interact with their environment. The main challenge in brain computer interfaces is to identify the particular EEG signal components that can be successfully used as control commands. In this study, we show that the fractal dimension of EEG is such a component. This is verified via the experiments carried. The fractal dimension is known to be a good measure of the chaotic behavior of the EEG signal. An algorithm proposed by Higuchi is used to estimate the fractal dimension. Visual and auditory information extracted from the EEG signal of each subject are fed back to himself. Each subject was asked to perform any mental activity so as to alter the value of his EEC's fractal dimension. Our experiments show the feasibility of the EEG biofeedback method to change the fractal dimension of the EEG signal. Our study introduces the fractal dimension as a new feature that can be controlled by a person and used in brain computer interface systems.
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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".