The Viability and Performance of P300 Responses using Low Fidelity Equipment
Bibliographic record
Abstract
In this paper we investigate the viability, practicability and efficacy of eliciting P300 responses based on the P300 speller BCI paradigm (oddball) and the xDAWN algorithm using five healthy subjects; while using a non-invasive Brain Computer Interface (BCI) based on low fidelity electroencephalographic (EEG) equipment. In the past decade there was a proliferation of cheap EEG equipment, including user-made equipment, which exposed an evident necessity to validate the equipment's suitability. Moreover a number of researchers and end users are currently using off-the-shelf equipment as a "black box" approach without any qualitative testing. Part of our contribution will be to create awareness of what type of hardware components are being utilized in our low fidelity equipment, vis--vis the results achieved. Our main contribution is to assess the functionality and reliability of our low cost equipment in its ability to detect the P300 component in a consistent, reliable and effective manner as a basis for future studies. This work forms part of a wider project where we plan to introduce a number of distractions and assessing the ways and extents to which different degrees of distractions affect the detection success achievable of the P300 component while using our low cost equipment. Our results demonstrate the applicability of using this off-the-shelf equipment as a means to successfully and effectively detect P300 responses.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".