Thinker invariance: enabling deep neural networks for BCI across more people
Bibliographic record
Abstract
OBJECTIVE: Most deep neural networks (DNNs) used as brain computer interfaces (BCI) classifiers are rarely viable for more than one person and are relatively shallow compared to the state-of-the-art in the wider machine learning literature. The goal of this work is to frame these as a unified challenge and reconsider how transfer learning is used to overcome these difficulties. APPROACH: We present two variations of a holistic approach to transfer learning with DNNs for BCI that rely on a deeper network called TIDNet. Our approaches use multiple subjects for training in the interest of creating a more universal classifier that is applicable for new (unseen) subjects. The first approach is purely subject-invariant and the second targets specific subjects, without loss of generality. We use five publicly accessible datasets covering a range of tasks and compare our approaches to state-of-the-art alternatives in detail. MAIN RESULTS: We observe that TIDNet in conjunction with our training augmentations is more consistent when compared to shallower baselines, and in some cases exhibits large and significant improvements, for instance motor imagery classification improvements of over 8%. Furthermore, we show that our suggested multi-domain learning (MDL) strategy strongly outperforms simply fine-tuned general models when targeting specific subjects, while remaining more generalizable to still unseen subjects. SIGNIFICANCE: TIDNet in combination with a data alignment-based training augmentation proves to be a consistent classification approach of single raw trials and can be trained even with the inclusion of corrupted trials. Our MDL strategy calls into question the intuition to fine-tune trained classifiers to new subjects, as it proves simpler and more accurate while remaining general. Furthermore, we show evidence that augmented TIDNet training makes better use of additional subjects, showing continued and greater performance improvement over shallower alternatives, indicating promise for a new subject-invariant paradigm rather than a subject-specific one.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".