Navigating in Virtual Reality using Thought: The Development and\n Assessment of a Motor Imagery based Brain-Computer Interface
Bibliographic record
Abstract
Brain-computer interface (BCI) systems have potential as assistive\ntechnologies for individuals with severe motor impairments. Nevertheless,\nindividuals must first participate in many training sessions to obtain adequate\ndata for optimizing the classification algorithm and subsequently acquiring\nbrain-based control. Such traditional training paradigms have been dubbed\nunengaging and unmotivating for users. In recent years, it has been shown that\nthe synergy of virtual reality (VR) and a BCI can lead to increased user\nengagement. This study created a 3-class BCI with a rather elaborate EEG signal\nprocessing pipeline that heavily utilizes machine learning. The BCI initially\npresented sham feedback but was eventually driven by EEG associated with motor\nimagery. The BCI tasks consisted of motor imagery of the feet and left and\nright hands, which were used to navigate a single-path maze in VR. Ten of the\neleven recruited participants achieved online performance superior to chance (p\n< 0.01), while the majority successfully completed more than 70% of the\nprescribed navigational tasks. These results indicate that the proposed\nparadigm warrants further consideration as neurofeedback BCI training tool. A\nparadigm that allows users, from their perspective, control from the outset\nwithout the need for prior data collection sessions.\n
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".