TY - JOUR
T1 - EarEOG: Using Headphones and Around-the-Ear EOG Signals for Real-Time Wheelchair Control
AU - Liu, Peichen
AU - Puthusserypady, Sadasivan
AU - MacKenzie, I. Scott
AU - Uyanik, Cihan
AU - Hansen, John Paulin
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
U2 - 10.1145/3725833
DO - 10.1145/3725833
M3 - Journal article
SN - 2573-0142
VL - 9
SP - 1
EP - 16
JO - Proceedings of the Acm on Human-computer Interaction
JF - Proceedings of the Acm on Human-computer Interaction
IS - 3
M1 - ETRA08
ER -