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-signals decoding. We employ recent advances in dry-EEG technology to stream and collect the cortical waveforms from subjects while they 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. Onscreen object selection via such a dry-EEG enabled SSVEP methodology, facilitates the on-line decoding of human cortical brain signals, via a specialised secondary CNN, directly 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 classification demonstrates high performance with mean accuracy of 85 for the real-time robot navigation experiment across multiple test subjects.
Brain-computer interface (BCI) systems can translate the human mind into control commands, which makes it feasible to improve the life quality of physically challenged people. However, in real-life situations, it is still difficult for users to utilize robots to provide basic services with BCI systems. We aimed to propose a BCI-based system with a visual servo module to operate a service robot. We recorded single-channel steady-state visual evoked potentials (SSVEP) as input signals for the BCI system of this study. The visual stimuli for inducing SSVEP were modulated at seven different frequencies with the sampled sinusoidal method. Correspondingly, this SSVEP-based BCI system can generate seven control commands for the operation of the service robot, which can provide three fundamental services: mobility, manipulation, and delivery. The visual servo module was established to reduce the burden of users and accelerate service procedures. To evaluate the performance of this system, subjects were recruited to participate in the experiments. All the participants succeed in operating the robot to provide the basic services. According to the experimental results, this SSVEP-based BCI system that incorporates the visual servo module can be effectively used to operate service robots with reduced number of channels and increased ability to perform multiple tasks.  The efficient control of our body and successful interaction with the environment are possible through the integration of multisensory information. Brain–computer interface (BCI) may allow people with sensorimotor disorders to actively interact in the world. In this study, visual information was paired with auditory feedback to improve the BCI control of a humanoid surrogate. Healthy and spinal cord injured (SCI) people were asked to embody a humanoid robot and complete a pick-and-place task by means of a visual evoked potentials BCI system. Participants observed the remote environment from the robot’s perspective through a head mounted display. Human-footsteps and computer-beep sounds were used as synchronous asynchronous auditory feedback. Healthy participants achieved better placing accuracy when listening to human footstep sounds relative to a computer-generated sound. SCI people demonstrated more difficulty in steering the robot during asynchronous auditory feedback conditions. Importantly, subjective reports highlighted that the BCI mask overlaying the display did not limit the observation of the scenario and the feeling of being in control of the robot. Overall, the data seem to suggest that sensorimotor-related information may improve the control of external devices. Further studies are required to understand how the contribution of residual sensory channels could improve the reliability of BCI systems.