Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation

Abstract

As autonomous service robots become more affordable and thus available for the general public, there is a growing need for user-friendly interfaces to control these systems. Control interfaces typically get more complicated with increasing complexity of the robotic tasks and the environment. Traditional control modalities as touch, speech or gesture commands are not necessarily suited for all users. While non-expert users can make the effort to familiarize themselves with a robotic system, paralyzed users may not be capable of controlling such systems even though they need robotic assistance most. In this paper, we present a novel framework, that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The system is composed of several interacting components: non-invasive neuronal signal recording and co-adaptive deep learning which form the brain-computer interface (BCI), high-level task planning based on referring expressions, navigation and manipulation planning as well as environmental perception. We extensively evaluate the BCI in various tasks, determine the performance of the goal formulation user interface and investigate its intuitiveness in a user study. Furthermore, we demonstrate the applicability and robustness of the system in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results show, the system is capable of adapting to frequent changes in the environment and reliably accomplishes given tasks within a reasonable amount of time. Combined with high-level planning using referring expressions and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based human-robot interactions.

Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review

“…The main advantage of hDL in respect to ML is less needed for human intervention [ 17 ]. However, the cost of this advantage could be summarized in two steps: the need for larger training sets [ 43 ] and the high computational efforts required [ 64 , 65 , 66 ].…”

“…CNN-Based Hybrid Deep Learning Algorithms Convolutional Neural Networks is one of the most admired deep learning models specialized in spatial information exploration. CNN is widely used, in the reviewed literature, to discover the latent spatial information in applications such as the analysis of motor imagery data [ 40 ], robotics [ 65 , 83 ]. increasing the learning capacity of BCI systems [ 58 ], detecting depression with EEG signals and to evaluate a novel deep learning method for classifying binary motor imagery data [ 41 ].…”

Workshops of the eighth international brain–computer interface meeting: BCIs: the next frontier

“…In the area of motor imagery and stroke rehabilitation, deep learning methods and convolutional neural networks (CNN) have been used for participant specific [255,256], participant-independent [257], and adaptive classifiers [258]. CNNs have also been used in assistive robot control with online adaptive motor classification [259].…”