TY - JOUR A1 - Golparvar, Ata Jedari A1 - Yapici, Murat Kaya T1 - Toward graphene textiles in wearable eye tracking systems for human–machine interaction JF - Beilstein Journal of Nanotechnology PY - 2021/// VL - 12 SP - 180 EP - 189 SN - 2190-4286 DO - 10.3762/bjnano.12.14 PB - Beilstein-Institut JA - Beilstein J. Nanotechnol. UR - https://doi.org/10.3762/bjnano.12.14 KW - electrooculography (EOG) KW - flexible electronics KW - graphene KW - human–computer interaction (HCI) KW - human–machine interface (HMI) KW - personal assistive device (PAD) KW - wearable smart textile N2 - The study of eye movements and the measurement of the resulting biopotential, referred to as electrooculography (EOG), may find increasing use in applications within the domain of activity recognition, context awareness, mobile human–computer and human–machine interaction (HCI/HMI), and personal medical devices; provided that, seamless sensing of eye activity and processing thereof is achieved by a truly wearable, low-cost, and accessible technology. The present study demonstrates an alternative to the bulky and expensive camera-based eye tracking systems and reports the development of a graphene textile-based personal assistive device for the first time. This self-contained wearable prototype comprises a headband with soft graphene textile electrodes that overcome the limitations of conventional “wet” electrodes, along with miniaturized, portable readout electronics with real-time signal processing capability that can stream data to a remote device over Bluetooth. The potential of graphene textiles in wearable eye tracking and eye-operated remote object interaction is demonstrated by controlling a mouse cursor on screen for typing with a virtual keyboard and enabling navigation of a four-wheeled robot in a maze, all utilizing five different eye motions initiated with a single channel EOG acquisition. Typing speeds of up to six characters per minute without prediction algorithms and guidance of the robot in a maze with four 180° turns were successfully achieved with perfect pattern detection accuracies of 100% and 98%, respectively. 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