  Hearing-impaired listeners usually have troubles attending target talker in
multi-talker scenes, even with hearing aids (HAs). The problem can be solved
with eye-gaze steering HAs, which requires listeners eye-gazing on the target.
In a situation where head rotates, eye-gaze is subject to both behaviors of
saccade and head rotation. However, existing methods of eye-gaze estimation did
not work reliably, since the listener's strategy of eye-gaze varies and
measurements of the two behaviors were not properly combined. Besides, existing
methods were based on hand-craft features, which could overlook some important
information. In this paper, a head-fixed and a head-free experiments were
conducted. We used horizontal electrooculography (HEOG) and neck
electromyography (NEMG), which separately measured saccade and head rotation to
commonly estimate eye-gaze. Besides traditional classifier and hand-craft
features, deep neural networks (DNN) were introduced to automatically extract
features from intact waveforms. Evaluation results showed that when the input
was HEOG with inertial measurement unit, the best performance of our proposed
DNN classifiers achieved 93.3%; and when HEOG was with NEMG together, the
accuracy reached 72.6%, higher than that with HEOG (about 71.0%) or NEMG (about
35.7%) alone. These results indicated the feasibility to estimate eye-gaze with
HEOG and NEMG.
