Bibliographic record
Abstract
Brain-machine interface (BMI) systems are used to classify biological signals from the brain, such as electroencephalogram (EEG) data, to determine control commands. There are several different signals that can be used for the interface. Among them, one finds the P300 signal. The P300 signal is a potential signal that is passively produced when a user observes, hears or pays attention to a desired stimulus. This signal has been used in conjunction with a graphical user interface (Gill) to allow a person to choose commands from a list of possible actions. Traditionally, the visual stimuli are repeated and averaged to increase classification accuracy, which, in turn, reduces the maximum possible command rate. In order to improve command rate, this paper describes a system wherein feature extraction and classifier training could be tested offline. Then, live testing in a mobile robot steering simulation was carried out. Finally, a live experiment is reported. The features to be used in classification are selected using a genetic algorithm (GA). Using the chosen features, 78.3% signal detection accuracy was achieved fur single epochs. Using multiple-epochs to improve classifier performance in simulated and real-world steering experiments we were able to successfully navigate a simple maze while maintaining classifier accuracy (Sim: 79.9±5.3%, Real: 88.8±10.1%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".