Towards Single-Channel & Single-Cycle EOG Based Multi-Directional Eye Tracking in Wearable System
Bibliographic record
Abstract
Electrooculography (EOG) is a promising bioelectrical technique for tracking ocular motion, offering substantial potential in Human-Computer Interaction (HCI) and wearable assistive technologies. However, existing EOGbased interfaces often rely on two-channel configurations, which increase hardware complexity, power consumption, and user discomfort. To overcome these challenges, this study investigates the feasibility of single-channel EOG acquisition for robust direction classification using single-cycle EOG signals, enabling decision-making shorter than human reaction time. EOG data were recorded from multiple participants under controlled laboratory conditions using standard two-channel setup. Horizontal and vertical channel data were isolated and analyzed independently to emulate single-channel operation. Nine distinct eye movement classes were classified using machine learning models trained on single-cycle segments. Feature extraction incorporated both statistical and gradientbased descriptors to enhance the classification performance. Our approach demonstrated accuracy, precision, recall & F1score of$\sim 83 \%$for both the vertical and horizontal configurations, with a latency of 27 ms. Despite using only half the number of electrodes, the model effectively captured the dynamics of diverse eye movements, demonstrating that singlechannel EOG can retain robust performance while substantially simplifying system architecture. This single-cycle, singlechannel paradigm offers fast, low-latency response and minimal electrode usage, making it well suited for next-generation wearable and real-time control interfaces.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".