Unsupervised Adaptation of DNN for Brain-Computer Interface Spellers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Brain-computer interface (BCI) spellers, based on the steady-state evoked potentials (SSVEP), significantly contribute to the communication of individuals with neuromuscular disorders. These systems aim to predict a target character that a user is intended to spell as fast as possible while maintaining high accuracy. Accordingly, target character identification methods aim to reach the high information transfer rate (ITR). Methods reaching high ITR values in the literature use participants’ labeled data for user calibration, which requires long and exhausting experiments for every individual that will use the speller. In this study, we developed a method that does not require labeled data from the new users; as the system is used it utilizes the accumulated unlabeled data effectively. Our method transfers the information obtained from previous users to the new user by training a deep neural network (DNN). Afterward, it uses accumulated unlabeled data of the new user to adapt the transferred DNN to that user. Adaptation is performed by assuming the DNN model’s predicted target labels on the data as correct. And the model is updated in every iteration by utilizing dropout layers. Our method is compared with online template transfer canonical correlation analysis (OTT-CCA) and adaptive combined transfer canonical correlation analysis (Adaptive-C3A) methods. The comparison is performed on two large publicly available datasets (benchmark and BETA) for signal lengths between 0.2 − 1.0 seconds (s). The results have shown that our method reached approximately 5% higher maximum ITR.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it