EarEOG: Using Headphones and Around-the-Ear EOG Signals for Real-Time Wheelchair Control
Bibliographic record
Abstract
We present EarEOG, a real-time wheelchair control system using around-ear electrooculogram (EOG) signals. Electrodes are placed in standard over-the-ear headphones to improve user comfort. By detecting around-the-ear signals from eye gestures and jaw clenching, EarEOG offers a non-invasive and intuitive approach to low-latency wheelchair control. We describe the methods for signal acquisition, as well as the algorithms used for signal processing and classification. The feasibility, robustness, and low latency of EarEOG were confirmed through two experiments. The algorithm demonstrated a classification accuracy of 94.1% for all motion signals, which further improved to 97.3% when personalized models were applied. To ensure stability, we examined electrode impedance and algorithm accuracy across multiple trials where participants operated simulated wheelchairs while wearing EarEOG. The results indicated that when the electrode impedance was below 1 MΩ, all participants successfully controlled the simulated wheelchair. Furthermore, EarEOG demonstrated low latency, with recognition delays of less than 125 ms.
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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 | 0.002 | 0.001 |
| 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".