The most popular noninvasive Brain Robot Interaction (BRI) technology uses the electroencephalogram- (EEG-) based Brain Computer Interface (BCI), to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior. This article reviews the major techniques needed for developing BRI systems. In this review article, we first briefly introduce the background and development of mind-controlled robot technologies. Second, we discuss the EEG-based brain signal models with respect to generating principles, evoking mechanisms, and experimental paradigms. Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples. Next, we describe a few BRI applications, including wheelchairs, manipulators, drones, and humanoid robots with respect to synchronous and asynchronous BCI-based techniques. Finally, we address some existing problems and challenges with future BRI techniques.

In this paper, we evaluate the control performance of SSVEP (steady-state visual evoked potential)- and P300-based models using Cerebot—a mind-controlled humanoid robot platform. Seven subjects with diverse experience participated in experiments concerning the open-loop and closed-loop control of a humanoid robot via brain signals. The visual stimuli of both the SSVEP- and P300- based models were implemented on a LCD computer monitor with a refresh frequency of 60 Hz. Considering the operation safety, we set the classification accuracy of a model over 90.0% as the most important mandatory for the telepresence control of the humanoid robot. The open-loop experiments demonstrated that the SSVEP model with at most four stimulus targets achieved the average accurate rate about 90%, whereas the P300 model with the six or more stimulus targets under five repetitions per trial was able to achieve the accurate rates over 90.0%. Therefore, the four SSVEP stimuli were used to control four types of robot behavior; while the six P300 stimuli were chosen to control six types of robot behavior. Both of the 4-class SSVEP and 6-class P300 models achieved the average success rates of 90.3% and 91.3%, the average response times of 3.65 s and 6.6 s, and the average information transfer rates (ITR) of 24.7 bits/min 18.8 bits/min, respectively. The closed-loop experiments addressed the telepresence control of the robot; the objective was to cause the robot to walk along a white lane marked in an office environment using live video feedback. Comparative studies reveal that the SSVEP model yielded faster response to the subject’s mental activity with less reliance on channel selection, whereas the P300 model was found to be suitable for more classifiable targets and required less training. To conclude, we discuss the existing SSVEP and P300 models for the control of humanoid robots, including the models proposed in this paper.

A constant tension towing winch maintains constant towing rope tension between the cable ship and the cable laying machine. In this paper, an active constant tension controller based upon a hybrid FUZZY P+ID strategy is proposed to improve the performance yield of the conventional PID controller that was implemented on the towing winch. The FUZZY P+ID controller replaces the P item of the conventional PID with the FUZZY P item, but the I and D items remain unchanged. The comparative studies of the FUZZY P+ID and traditional PID controllers were conducted, and the numerical simulation results show that the performance of the proposed FUZZY P+ID controller is superior to that of the PID controller. The proposed FUZZY P+ID controller was implemented in the constant tension towing winch to substitute for the existing PID controller. The real applications for driving the submarine cable laying ship under harsh undersea conditions demonstrated the robustness of the FUZZY P+ID for a number of unmeasurable disturbances, such as the buoyancy, the drag force, and wave effects.

Recent research has demonstrated improved performance of a brain-computer interface (BCI) using fusion based approaches. This paper proposes a novel decision-making selector (DMS) to integrate classification decisions of different frequency recognition methods based on canonical correlation analysis (CCA) which were used in decoding steady state visual evoked potentials (SSVEPs). Methods: The DMS method selects a decision more likely to be correct from two methods namely as M1 and M2 by separating the M1-false and M2-false trials. To measure the uncertainty of each decision, feature vectors were extracted using the largest and second largest correlation coefficients corresponding to all the stimulus frequencies. The proposed method was evaluated by integrating all pairs of 7 CCA-based algorithms, including CCA, individual template-based CCA (ITCCA), multi-set CCA (MsetCCA), L1-regularized multi-way CCA (L1-MCCA), filter bank CCA (FBCCA), extended CCA (ECCA), and task-related component analysis (TRCA). Main results: The experimental results obtained from a 40-target dataset of thirty-five subjects showed that the proposed DMS method was validated to obtain an enhanced performance by integrating the algorithms with close accuracies. Conclusion: The results suggest that the proposed DMS method is effective in integrating decisions of different methods to improve the performance of SSVEP-based BCIs.

This paper develops a virtual platform of an underwater manipulator mounted on a submersible vehicle via the three-dimensional simulator “Webots” for teleoperation through a replica master arm. The graphical, kinematic, and dynamic models of the manipulator refer to a master-slave servo hydraulic manipulator with seven functions, consisting of six degrees of freedom and a parallel gripper, while the “Jiaolong” deep-manned submersible vehicle, operating below the sea surface down to 7000 m, is chosen as the underwater manipulator carrier. This study uses the virtual platform for training an operator to telepresence control the virtual manipulator to complete basic tasks in subsea environments. When training the operator, one has to consider uncertain external disturbances and the visual impacts that stem from subsea environments. In order to demonstrate the feasibility and effectiveness of the virtual platform, one designs two typical underwater operational tasks: grasping a marine organism sample and reaching at a given position. This paper presents the comparative studies: 1) the performances demonstrated by remotely controlling the virtual manipulator and the real manipulator; 2) the operating performances delivered by three operators before and after training when using the platform.

The cross-session and cross-subject classification of motor imagery Electroencephalogram (EEG) signals is challenging. This paper presents a Transfer Learning (TL) method to address the cross-session and cross-subject classification of motor imagery Electroencephalogram (EEG) signals, a tricky procedure in brain-computer interface (BCI). Method: We propose a rotation alignment domain adaptation method with Riemannian mean (RMRA). The method uses covariance matrix to represent data feature, and achieves data alignment by rotating the symmetric positive definite (SPD) matrix in Riemannian space. In this process, our proposed matrix-TCA extends the traditional transfer component analysis (TCA) to a matrix form in order to function in the Riemannian framework. Data labels are not required, so the proposed method is unsupervised. In addition, we simplify the calculation process through Riemannian mean. Results: We have performed both offline and online experiments on multiple motor imagery EEG data sets. Our results show that RMRA improves the cross-session and cross-subject classification accuracy. Conclusion and significance: This paper presents a new approach to cross-domain learning, which achieves desirable results and shows great promise in real-life application of the service robot (intelligent wheelchair).