Title: Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation,
Abstract: This paper addresses the challenge of humanoid robot teleoperation in a
natural indoor environment via a Brain-Computer Interface (BCI). We leverage
deep Convolutional Neural Network (CNN) based image and signal understanding to
facilitate both real-time object detection and dry-Electroencephalography (EEG)
based human cortical brain bio-signal decoding. We employ recent advances in
dry-EEG technology to stream and collect the cortical waveforms from subjects
while the subjects fixate on variable Steady-State Visual Evoked Potential
(SSVEP) stimuli generated directly from the environment the robot is
navigating. To these ends, we propose the use of novel variable BCI stimuli by
utilising the real-time video streamed via the on-board robot camera as visual
input for SSVEP where the CNN detected natural scene objects are altered and
flickered with differing frequencies (10Hz, 12Hz and 15Hz). These stimuli are
not akin to traditional stimuli - as both the dimensions of the flicker regions
and their on-screen position changes depending on the scene objects detected in
the scene. On-screen object selection via dry-EEG enabled SSVEP in this way,
facilitates the on-line decoding of human cortical brain signals via a
secondary CNN approach into teleoperation robot commands (approach object, move
in a specific direction: right, left or back). This SSVEP decoding model is
trained via a priori offline experimental data in which very similar visual
input is present for all subjects. The resulting offline classification
demonstrates extremely high performance and with mean accuracies of 96% and 90%
for the real-time robot navigation experiment across multiple test subjects.