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 bject 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. On-screen 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.

/pdf/using-variable-natural-environment-brain-computer-interface-4quq59osix.pdf

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation

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.

/pdf/using-variable-natural-environment-brain-computer-interface-55ien02jpp.pdf

Brain-computer interface (BCI)-based robotic telepresence provides an opportunity for people with disabilities to control robots remotely without any actual physical movement. However, traditional BCI systems usually require the user to select the navigation direction from visual stimuli in a fixed background, which makes it difficult to control the robot in a dynamic environment during the locomotion. In this paper, a novel SSVEP-based BCI stimuli system is proposed for robotic telepresence. The novel system utilized the live video streamed from the robot onboard camera as the input. By altering and flickering the detected objects in the scene with different frequencies predefined based on their relative positions on the screen, the robot can be navigated based on the user’s attention in a dynamic manner. In order to better differentiate multiple objects (more than the number of frequencies predefined), the task-related component analysis (TRCA) model was trained with a priori offline experimental data to select the front objects with priority. Experiments were conducted to validate the proposed system. Using the system, four human subjects are able to control a humanoid robot to navigate through multiple objects to reach the desired goal. The success rate reaches 87.5% in average.

Design of an SSVEP-based BCI Stimuli System for Attention-based Robot Navigation in Robotic Telepresence*

Brain-computer Interface is a new technology to communicate with the outside world without dependence on the peripheral nerves and muscle tissues. It has provided a brand-new interaction way for those suffering from neuromuscular diseases. Using the brain-computer interaction method based on steady state visual evoked potential, 3 instructions are designed to control the robot's motion. In this paper, band-pass filters and independent component analysis are used for artifacts reduction, the power spectral density of the signals is extracted with sliding windows to train the neural network classifier. The experimental results show that the system can complete various experimental tasks with high control precision.

Research of Mobile Robot Control System Based on SSVEP Brain Computer Interaction

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Research of Mobile Robot Control System Based on SSVEP Brain Computer Interaction