Study on Novel Designs with Reduced Fatigue for Steady State Motion Visual Evoked Potentials
Bibliographic record
Abstract
The paper focuses on incorporation of Brain Computer Interfacing (BCI) within an Augmented Reality (AR) platform to provide means for individuals with communication disabilities to interact with the outer world. Recently, there has been a recent surge of interest on Steady-State Visual Evoked Potentials (SSVEP). In a typical SSVEP-based BCI system, the virtual object within the AR environment flickers with a specific frequency while the signal processing module extracts the effects of the flickering frequency on the Electrophysiological (EEG) signals. Despite the popularity of SSVEPs, their utilization for practical application especially for assistive technologies is complicated and challenging due to eye fatigue and risk of induced epileptic seizure. In this regard, the key issue being targeted in this paper is addressing fatigue of flicker (or brightness modulation) by development of flicker-free steady-state motion visual evoked potential (SSMVEP). Two novel SSMVEP paradigms, i.e., Square-based and Circle-based paradigms, with low luminance contrast and oscillating expansion and contraction motions are designed, and integrated within a BCI system. Through experimental evaluations, high detection accuracy of 95.31% is achieved for the square-based SSMVEP.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".