EarEOG: Using Headphones and Around-the-Ear EOG Signals for Real-Time Wheelchair Control

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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Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2025-05-22
DOI
https://doi.org/10.1145/3725833
Primary Topic
Gaze Tracking and Assistive Technology
Type
article
Field-Weighted Citation Impact
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article

EarEOG: Using Headphones and Around-the-Ear EOG Signals for Real-Time Wheelchair Control

Ian Scott MacKenzie, John Paulin Hansen, Peichen Liu, Sadasivan Puthusserypady et al.
Proceedings of the ACM on Human-Computer Interaction
Gaze Tracking and Assistive Technology
article

EarEOG: Using Headphones and Around-the-Ear EOG Signals for Real-Time Wheelchair Control

Ian Scott MacKenzie, John Paulin Hansen, Peichen Liu, Sadasivan Puthusserypady, Cihan Uyanik
article en

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.

Proceedings of the ACM on Human-Computer InteractionVol. 9(3)
York University (CA), Technical University of Denmark (DK)
Openalex Percentile: Top 90%
Gaze Tracking and Assistive Technology
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