An independent-BCI based on SSVEP using Figure-Ground Perception (FGP)
Bibliographic record
Abstract
The main idea of a traditional Steady State Visually Evoked Potentials (SSVEP)-BCI is the activation of commands through gaze control. However, the widely named “dependent” SSVEP-BCIs might not be applicable for patients with ocular motor impairments or severe neuromuscular problems. Nevertheless, an “independent” SSVEP-BCIs might be a potential approach to solve this problem. This study presents a novel independent-BCI based on SSVEP using Figure-Ground Perception (FGP), terminology widely known and used in Gestalt psychology for object recognition by means of changes in perception. This BCI proposes to identify two different targets that represent commands in a limited visual space without needing to shift the gaze by the paradigm of covert attention. For that purpose, the well-known example of Rubin's face-vase in FGP was used. The traditional EEG signal analysis consists of three steps: filtering, feature extraction and classification. In this work, two techniques were used for performance comparison, and the classification was obtained through a criterion of maxima for both techniques. Ten subjects participated in this study in offline tests and five subjects for online tests. The flickering frequencies were 15.0 Hz (vase) and 11.0 Hz (faces). Our results demonstrate that the electrode Oz is the best channel for characterization of visual perception, from a quantitative point of view based on the canonical correlation, after a channel analysis by independent way. Regarding the classification, MSI technique was more accurate in relation to CCA, in all the cases with same conditions, either using three electrodes or a single electrode (Oz), even for different window lengths. The online performance appeared to decrease as participants switched from Face (82.7%) to Vase (76%) stimulus. These results are consistent with our results in offline tasks. Muscular activity related to the eye movements was also evaluated using a commercial device of eye tracking (Eye Tribe). These findings strongly support the hypothesis of visual selectivity by means of perception and neural mechanism of spatial attention .
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".