Deep Video Canonical Correlation Analysis for Steady State motion Visual Evoked Potential Feature Extraction
Bibliographic record
Abstract
Recently, there has been a surge of interest in development of Brain Computer Interface (BCI) systems based on Steady-State motion-Visual Evoked Potentials (SSmVEP), where motion stimulation is utilized to address high brightness and uncomfortably issues associated with conventional light-flashing/flickering. In this paper, we propose a deep learning-based classification model that extracts features of the SSmVEPs directly from the videos of stimuli. More specifically, the proposed deep architecture, referred to as the Deep Video Canonical Correlation Analysis (DvCCA), consists of a Video Feature Extractor (VFE) layer that uses characteristics of videos utilized for SSmVEP stimulation to fit the template EEG signals of each individual, independently. The proposed VFE layer extracts features that are more correlated with the stimulation video signal as such eliminates problems, typically, associated with deep networks such as overfitting and lack of availability of sufficient training data. The proposed DvCCA is evaluated based on a real EEG dataset and the results corroborate its superiority against recently proposed state-of-the-art deep models.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".