BMI-VR based Cognitive Training improves Attention Switching Processing Speed
Bibliographic record
Abstract
Cognitive decline in aging is a pressing issue that can lead to long term functional impairments, including dementia. Computer-based cognitive training applications have been shown to improve cognitive skills, however, they often lack ecological validity. Researchers have proposed the use of Brain-Machine interface (BMI) systems as cognitive training tools but still face the limitation that the user cannot move freely while performing the cognitive training. Previously, we reported the successful use of a BMI system with a physical robotic third arm that allowed users to do multitasking by doing two tasks simultaneously, thereby engaging multiple cognitive skills such as attention switching, mental focus, coordination, decision making and visual information processing. In this paper, we present a cognitive training platform based on our previous multitasking paradigm with a BMI enhanced with a virtual reality (VR) experience. We conducted an experiment to investigate the efficiency of the proposed platform and monitored the level of accuracy and processing speed of the attention switching skill and compared to the traditional Attention Switching Task (AST) cognitive training paradigm. Preliminary experimental results showed that mean difference in attention accuracy scores were 3.96 s faster for the BMI-VR group compared to the AST group. Although there was a high degree of intersubject variability making the result not statistically significant, preliminary evidence reflects a potential for the proposed training approach to improve attention switching speed.
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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) | 0.000 | 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) | 0.002 | 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".