Two-channel in-ear EEG system for detection of visuomotor tracking state: A preliminary study
Bibliographic record
Abstract
Evaluating an operator's mental workload during work activities is crucial to maintain safety and performance. By minimizing human error associated with work demands, especially in a hazardous environment, potentially serious errors may be avoided. This study aims to assess the feasibility of using an in-ear EEG system to classify the user's state in a visuomotor tracking task that may influence mental workload and motor action. A two-channel wireless in-ear EEG system was used to record EEG signals while subjects performed the task using a joystick to manipulate an object displayed on a monitor. A highly comparative time series analysis was employed on the processed signals to extract and select the top features for each subject individually. The features sets were trained and tested with support vector machines, random forest, linear discriminant analysis, subspace discriminant, and neural network to compare their performances. Models trained on two trials, each 14 minutes in duration and tested on the other trial were able to yield an accuracy of 79.30 ± 4.85% on average across the ten participants with an individualized moving average threshold filter and classifier. This proof-of-concept study demonstrates the feasibility of using a two-channel wireless in-ear EEG system as a viable solutions to develop wearable devices to detect mental workload associated with the execution of visuomotor tasks.
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 | 0.001 | 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".