Investigating the Effect of COVID-19 Infection on P300 Based BCI Application Performance
Bibliographic record
Abstract
The COVID-19, which has rapidly spread and infected millions of people from all over the world, causes various problems including psychiatric, economic, educational as well as health. Many studies have been reported that COVID-19 can be characterized by vascular damage predominantly involving micro vessels. In this study, we proposed a method to examine whether COVID-19 effects on brain computer interface (BCI) performance or not. We collected P300 based electroencephalogram (EEG) signals from six subjects before and after the COVID-19 infection. For classifying the P300 and non-P300 EEG signals, single output and two-layer artificial neural network was utilized. Based on the t-test analysis, it was observed that there was a significant difference between the before and after COVID-19 infection test groups especially on Oz channel in occipital region for alpha=0.05 percent while that of for alpha=0.01 percent shows no statistical difference for P300 classification results. The latency values, on the other hand, before and after COVID-19 infection did not represent any difference for both significance levels. It is clearly understood from the literature that COVID-19 negatively effects to the microvascular bed. Therefore, it might be expected that it could cause to reduce the P300 based BCI performance. This was the first study to investigate the impact of COVID-19 on P300-based BCI performance, taking into account the EEG signals of the COVID-19 infection. The obtained results showed that although the COVID-19 infection did not generally effected P300 based BCI application performance and latency values, the performance of the occipital region electrodes slightly effected.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".