A Bayesian Shared Control Approach for Wheelchair Robot With Brain Machine Interface

EEG-controlled tele-grasping for undefined objects

This paper presents a teleoperation system of robot grasping for undefined objects based on a real-time EEG (Electroencephalography) measurement and shared autonomy. When grasping an undefined object in an unstructured environment, real-time human decision is necessary since fully autonomous grasping may not handle uncertain situations. The proposed system allows involvement of a wide range of human decisions throughout the entire grasping procedure, including 3D movement of the gripper, selecting proper grasping posture, and adjusting the amount of grip force. These multiple decision-making procedures of the human operator have been implemented with six flickering blocks for steady-state visually evoked potentials (SSVEP) by dividing the grasping task into predefined substeps. Each substep consists of approaching the object, selecting posture and grip force, grasping, transporting to the desired position, and releasing. The graphical user interface (GUI) displays the current substep and simple symbols beside each flickering block for quick understanding. The tele-grasping of various objects by using real-time human decisions of selecting among four possible postures and three levels of grip force has been demonstrated. This system can be adapted to other sequential EEG-controlled teleoperation tasks that require complex human decisions.

Phase-Locked Time-Shift Data Augmentation Method for SSVEP Brain-Computer Interfaces

Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) have achieved an information transfer rate (ITR) of over 300 bits/min, but abundant training data is required. The performance of SSVEP algorithms deteriorates greatly under limited data, and the existing time-shift data augmentation method fails to improve it because the phase-locked requirement between training samples is violated. To address this issue, this study proposes a novel augmentation method, namely phase-locked time-shift (PLTS), for SSVEP-BCI. The similarity between epochs at different time moments was evaluated, and a unique time-shift step was calculated for each class to augment additional data epochs in each trial. The results showed that the PLTS significantly improved the classification performance of SSVEP algorithms on the BETA SSVEP datasets. Moreover, under the condition of one calibration block, by slightly prolonging the calibration duration (from 48 s to 51.5 s), the ITR increased from 40.88±4.54 bits/min to 122.61±7.05 bits/min with the PLTS. This study provides a new perspective on augmenting data epochs for training-based SSVEP-BCI, promotes the classification accuracy and ITR under limited training data, and thus facilitates the real-life applications of SSVEP-based brain spellers.

A Calibration-Free Hybrid Approach Combining SSVEP and EOG for Continuous Control

While SSVEP-BCI has been widely developed to control external devices, most of them rely on the discrete control strategy. The continuous SSVEP-BCI enables users to continuously deliver commands and receive real-time feedback from the devices, but it suffers from the transition state problem, a period the erroneous recognition, when users shift their gazes between targets. To resolve this issue, we proposed a novel calibration-free Bayesian approach by hybridizing SSVEP and electrooculography (EOG). First, canonical correlation analysis (CCA) was applied to detect the evoked SSVEPs, and saccade during the gaze shift was detected by EOG data using an adaptive threshold method. Then, the new target after the gaze shift was recognized based on a Bayesian optimization approach, which combined the detection of SSVEP and saccade together and calculated the optimized probability distribution of the targets. Eighteen healthy subjects participated in the offline and online experiments. The offline experiments showed that the proposed hybrid BCI had significantly higher overall continuous accuracy and shorter gaze-shifting time compared to FBCCA, CCA, MEC, and PSDA. In online experiments, the proposed hybrid BCI significantly outperformed CCA-based SSVEP-BCI in terms of continuous accuracy (77.61 ± 1.36%vs. 68.86 ± 1.08% and gaze-shifting time (0.93 ± 0.06s vs. 1.94 ± 0.08s). Additionally, participants also perceived a significant improvement over the CCA-based SSVEP-BCI when the newly proposed decoding approach was used. These results validated the efficacy of the proposed hybrid Bayesian approach for the BCI continuous control without any calibration. This study provides an effective framework for combining SSVEP and EOG, and promotes the potential applications of plug-and-play BCIs in continuous control.

Continuous shared control of a mobile robot with brain-computer interface and autonomous navigation for daily assistance

Although the electroencephalography (EEG) based brain-computer interface (BCI) has been successfully developed for rehabilitation and assistance, it is still challenging to achieve continuous control of a brain-actuated mobile robot system. In this study, we propose a continuous shared control strategy combining continuous BCI and autonomous navigation for a mobile robot system. The weight of shared control is designed to dynamically adjust the fusion of continuous BCI control and autonomous navigation. During this process, the system uses the visual-based simultaneous localization and mapping (SLAM) method to construct environmental maps. After obtaining the global optimal path, the system utilizes the brain-based shared control dynamic window approach (BSC-DWA) to evaluate safe and reachable trajectories while considering shared control velocity. Eight subjects participated in two-stage training, and six of these eight subjects participated in online shared control experiments. The training results demonstrated that naïve subjects could achieve continuous control performance with an average percent valid correct rate of approximately 97 % and an average total correct rate of over 80 %. The results of online shared control experiments showed that all of the subjects could complete navigation tasks in an unknown corridor with continuous shared control. Therefore, our experiments verified the feasibility and effectiveness of the proposed system combining continuous BCI, shared control, autonomous navigation, and visual SLAM. The proposed continuous shared control framework shows great promise in BCI-driven tasks, especially navigation tasks for brain-driven assistive mobile robots and wheelchairs in daily applications.

Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities

Brain-Computer Interfaces (BCI) is an exciting and emerging research area for researchers and scientists. It is a suitable combination of software and hardware to operate any device mentally. This review emphasizes the significant stages in the BCI domain, current problems, and state-of-the-art findings. This article also covers how current results can contribute to new knowledge about BCI, an overview of BCI from its early developments to recent advancements, BCI applications, challenges, and future directions. The authors pointed to unresolved issues and expressed how BCI is valuable for analyzing the human brain. Humans' dependence on machines has led humankind into a new future where BCI can play an essential role in improving this modern world.

CVT-Based Asynchronous BCI for Brain-Controlled Robot Navigation

Brain-computer interface (BCI) is a typical direction of integration of human intelligence and robot intelligence. Shared control is an essential form of combining human and robot agents in a common task, but still faces a lack of freedom for the human agent. This paper proposes a Centroidal Voronoi Tessellation (CVT)-based road segmentation approach for brain-controlled robot navigation by means of asynchronous BCI. An electromyogram-based asynchronous mechanism is introduced into the BCI system for self-paced control. A novel CVT-based road segmentation method is provided to generate optional navigation goals in the road area for arbitrary goal selection. An event-related potential of the BCI is designed for target selection to communicate with the robot. The robot has an autonomous navigation function to reach the human selected goals. A comparison experiment in the single-step control pattern is executed to verify the effectiveness of the CVT-based asynchronous (CVT-A) BCI system. Eight subjects participated in the experiment, and they were instructed to control the robot to navigate toward a destination with obstacle avoidance tasks. The results show that the CVT-A BCI system can shorten the task duration, decrease the command times, and optimize navigation path, compared with the single-step pattern. Moreover, this shared control mechanism of the CVT-A BCI system contributes to the promotion of human and robot agent integration control in unstructured environments.

Teleoperation control of a wheeled mobile robot based on Brain-machine Interface

This paper presents a novel teleoperation system using Electroencephalogram (EEG) to control the motion of a wheeled mobile robot (WMR). Different from the other traditional motion controlling method, the WMR is braked with the EEG classification results. Furthermore, the EEG will be induced by using the online BMI (Brain Machine Interface) system, and adopting the non-intrusion induced mode SSVEP (steady state visually evoked potentials). Then, user's motion intention can be recognized by canonical correlation analysis (CCA) classifier, which will be converted into motion commands of the WMR. Finally, the teleoperation technique is utilized to manage the information of the movement scene and adjust the control instructions based on the real-time information. Bezier curve is used to parameterize the path planning of the robot, and the trajectory can be adjusted in real time by EEG recognition results. A motion controller based on error model is proposed to track the planned trajectory by using velocity feedback control, providing excellent track tracking performance. Finally, the feasibility and performance of the proposed teleoperation brain-controlled WMR system are verified using demonstration experiments.

Driving Mode Selection through SSVEP-Based BCI and Energy Consumption Analysis

Background: The brain–computer interface (BCI) is a highly cross-discipline technology and its successful application in various domains has received increasing attention. However, the BCI-enabled automobile industry is has been comparatively less investigated. In particular, there are currently no studies focusing on brain-controlled driving mode selection. Specifically, different driving modes indicate different driving styles which can be selected according to the road condition or the preference of individual drivers. Methods: In this paper, a steady-state visual-evoked potential (SSVEP)-based driving mode selection system is proposed. Upon this system, drivers can select the intended driving modes by only gazing at the corresponding SSVEP stimuli. A novel EEG processing algorithm named inter-trial distance minimization analysis (ITDMA) is proposed to enhance SSVEP detection. Both offline and real-time experiments were carried out to validate the effectiveness of the proposed system. Conclusion: The results show that a high selection accuracy of up to 92.3% can be realized, although this depends on the specific choice of flickering duration, the number of EEG channels, and the number of training signals. Additionally, energy consumption is investigated in terms of which the proposed brain-controlled system considerably differs from a traditional driving mode selection system, and the main reason is shown to be the existence of a detection error.

Application of Neuroengineering Based on EEG Features in the Industrial Design of Comfort

The smart wheelchair is a service robot that can be used as a means of transportation for the elderly and the disabled. The patients were given an intelligent wheelchair designed by electroencephalogram (EEG), which was used for more than 8 hours and tested continuously for 1 month. By ridit analysis, the difference between the two groups was statistically significant (U = 3.72, P < 0.01). The scores of visual analogue scale (VAS) and joint ground visuality (JGV) in the observation group were significantly better than those in the control group. The modules of physiological function (PF), physical pain (PP), overall health (OH), vitality (VT), social function (SF), emotional function (EF), and mental health (MH) in the SF-36 scores of the two groups were significantly improved (P < 0.05), and the improvement of each module in the observation group was significantly better than that in the control group (P < 0.05). The levels of serum IL-6, IL-10, and superoxide dismutase (SOD) in the two groups were significantly improved (P < 0.05), and the improvement of serum IL-6, IL-10, and SOD in the observation group was significantly better than that in the control group (P < 0.05). It is suggested that neural engineering based on EEG characteristics can be well applied in comfort industrial design.

A Regional Smoothing Block Sparse Bayesian Learning Method With Temporal Correlation for Channel Selection in P300 Speller

The P300-based brain-computer interfaces (BCIs) enable participants to communicate by decoding the electroencephalography (EEG) signal. Different regions of the brain correspond to various mental activities. Therefore, removing weak task-relevant and noisy channels through channel selection is necessary when decoding a specific type of activity from EEG. It can improve the recognition accuracy and reduce the training time of the subsequent models. This study proposes a novel block sparse Bayesian-based channel selection method for the P300 speller. In this method, we introduce block sparse Bayesian learning (BSBL) into the channel selection of P300 BCI for the first time and propose a regional smoothing BSBL (RSBSBL) by combining the spatial distribution properties of EEG. The RSBSBL can determine the number of channels adaptively. To ensure practicality, we design an automatic selection iteration strategy model to reduce the time cost caused by the inverse operation of the large-size matrix. We verified the proposed method on two public P300 datasets and on our collected datasets. The experimental results show that the proposed method can remove the inferior channels and work with the classifier to obtain high-classification accuracy. Hence, RSBSBL has tremendous potential for channel selection in P300 tasks.

Intelligent Classification Technique of Hand Motor Imagery Using EEG Beta Rebound Follow-Up Pattern

To apply EEG-based brain-machine interfaces during rehabilitation, separating various tasks during motor imagery (MI) and assimilating MI into motor execution (ME) are needed. Previous studies were focusing on classifying different MI tasks based on complex algorithms. In this paper, we implement intelligent, straightforward, comprehensible, time-efficient, and channel-reduced methods to classify ME versus MI and left- versus right-hand MI. EEG of 30 healthy participants undertaking motional tasks is recorded to investigate two classification tasks. For the first task, we first propose a “follow-up” pattern based on the beta rebound. This method achieves an average classification accuracy of 59.77% ± 11.95% and can be up to 89.47% for finger-crossing. Aside from time-domain information, we map EEG signals to feature space using extraction methods including statistics, wavelet coefficients, average power, sample entropy, and common spatial patterns. To evaluate their practicability, we adopt a support vector machine as an intelligent classifier model and sparse logistic regression as a feature selection technique and achieve 79.51% accuracy. Similar approaches are taken for the second classification reaching 75.22% accuracy. The classifiers we propose show high accuracy and intelligence. The achieved results make our approach highly suitable to be applied to the rehabilitation of paralyzed limbs.

Task-Related Component Analysis Combining Paired Character Decoding for Miniature Asymmetric Visual Evoked Potentials

Brain-computer interface (BCI) technology based on event-related potentials (ERP) of electroencephalography (EEG) is widely used in daily life and medical treatment. However, the research of identifying the miniature and more informative asymmetric visual evoked potentials (aVEPs), which belongs to ERP, needs further exploration. Herein, a task-related component analysis combining paired character decoding (TRCA-PCD) method, which can enhance reproducibility of aVEPs in multiple trials and strengthen the features of different samples, was designed to realize fast decoding of aVEPs. The BCI performance and the influence of repetition times between the TRCA-PCD method, the discriminative canonical pattern matching (DCPM) method and traditional task-related component analysis (TRCA) method were compared using a 32-class aVEPs dataset recorded from 32 subjects. The highest average recognition accuracy and information transfer rate (ITR) of TRCA-PCD after parameter selection were 70.37 ± 2.49% (DCPM: 64.91 ± 2.81%, TRCA: 44.01 ± 3.25%) with the peak value of 97.92% and 28.90 ± 3.83 bits/min (DCPM: 21.29 ± 3.35 bits/min, TRCA: 11.54 ± 2.81 bits/min) with the peak value of 94.55 bits/min respectively. Statistical analysis indicated that the highest average recognition rate could be obtained when the repetition time was six, and the highest ITR could be obtained when the repetition time was one. Overall, the results verified the effectiveness and superiority of TRCA-PCD in recognition of aVEPs and provided a reference for parameter selection. Therefore, the TRCA-PCD method can promote the further application of aVEPs in the BCI speller field.

Far-field electric potentials provide access to the output from the spinal cord from wrist-mounted sensors

OBJECTIVE

Neural interfaces need to become more unobtrusive and socially acceptable to appeal to general consumers outside rehabilitation settings.

APPROACH

We developed a non-invasive neural interface that provides access to spinal motor neuron activities from the wrist, which is the preferred location for a wearable. The interface decodes far-field potentials present at the tendon endings of the forearm muscles using blind source separation. First, we evaluated the reliability of the interface to detect motor neuron firings based on far-field potentials, and thereafter we used the decoded motor neuron activity for the prediction of finger contractions in offline and real-time conditions.

MAIN RESULTS

The results showed that motor neuron activity decoded from the far-field potentials at the wrist accurately predicted individual and combined finger commands and therefore allowed for highly accurate real-time task classification.

SIGNIFICANCE

These findings demonstrate the feasibility of a non-invasive, neural interface at the wrist for precise real-time control based on the output of the spinal cord.

Control of a Robotic Arm With an Optimized Common Template-Based CCA Method for SSVEP-Based BCI

Recently, the robotic arm control system based on a brain-computer interface (BCI) has been employed to help the disabilities to improve their interaction abilities without body movement. However, it's the main challenge to implement the desired task by a robotic arm in a three-dimensional (3D) space because of the instability of electroencephalogram (EEG) signals and the interference by the spontaneous EEG activities. Moreover, the free motion control of a manipulator in 3D space is a complicated operation that requires more output commands and higher accuracy for brain activity recognition. Based on the above, a steady-state visual evoked potential (SSVEP)-based synchronous BCI system with six stimulus targets was designed to realize the motion control function of the seven degrees of freedom (7-DOF) robotic arm. Meanwhile, a novel template-based method, which builds the optimized common templates (OCTs) from various subjects and learns spatial filters from the common templates and the multichannel EEG signal, was applied to enhance the SSVEP recognition accuracy, called OCT-based canonical correlation analysis (OCT-CCA). The comparison results of offline experimental based on a public benchmark dataset indicated that the proposed OCT-CCA method achieved significant improvement of detection accuracy in contrast to CCA and individual template-based CCA (IT-CCA), especially using a short data length. In the end, online experiments with five healthy subjects were implemented for achieving the manipulator real-time control system. The results showed that all five subjects can accomplish the tasks of controlling the manipulator to reach the designated position in the 3D space independently.

L1-norm based time-varying brain neural network and its application to dynamic analysis for motor imagery

OBJECTIVE

EEG-based motor imagery (MI) brain-computer interface offers a promising way to improve the efficiency of motor rehabilitation and motor skill learning. In recent years, the power of dynamic network analysis for MI classification has been proved. In fact, its usability mainly depends on the accurate estimation of brain connection. However, traditional dynamic network estimation strategies such as adaptive directed transfer function (ADTF) are designed in the L2-norm. Usually, they estimate a series of pseudo connections caused by outliers, which results in biased features and further limits its online application. Thus, how to accurately infer dynamic causal relationship under outlier influence is urgent.

APPROACH

In this work, we proposed a novel ADTF, which solves the dynamic system in the L1-norm space (L1-ADTF), so as to restrict the outlier influence. To enhance its convergence, we designed an iteration strategy with the alternating direction method of multipliers (ADMM), which could be used for the solution of the dynamic state-space model restricted in the L1-norm space. Furthermore, we compared L1-ADTF to traditional ADTF and its dual extension across both simulation and real EEG experiments.

MAIN RESULTS

A quantitative comparison between L1-ADTF and other ADTFs in simulation studies demonstrates that fewer bias errors and more desirable dynamic state transformation patterns can be captured by the L1-ADTF. Application to real MI EEG datasets seriously noised by ocular artifacts also reveals the efficiency of the proposed L1-ADTF approach to extract the time-varying brain neural network patterns, even when more complex noises are involved.

SIGNIFICANCE

The L1-ADTF may not only be capable of tracking time-varying brain network state drifts robustly but may also be useful in solving a wide range of dynamic systems such as trajectory tracking problems and dynamic neural networks.

Mini-review: Robotic wheelchair taxonomy and readiness

Robotic wheelchair research and development is a growing sector. This article introduces a robotic wheelchair taxonomy, and a readiness model supported by a mini-review. The taxonomy is constructed by power wheelchair and, mobile robot standards, the ICF and, PHAATE models. The mini-review of 2797 articles spanning 7 databases produced 205 articles and 4 review articles that matched inclusion/exclusion criteria. The review and analysis illuminate how innovations in robotic wheelchair research progressed and have been slow to translate into the marketplace.

A multiscale siamese convolutional neural network with cross-channel fusion for motor imagery decoding

BACKGROUND

Recently, convolutional neural networks (CNN) are widely applied in motor imagery electroencephalography (MI-EEG) signal classification tasks. However, a simple CNN framework is challenging to satisfy the complex MI-EEG signal decoding.

NEW METHOD

In this study, we propose a multiscale Siamese convolutional neural network with cross-channel fusion (MSCCF-Net) for MI-EEG classification tasks. The proposed network consists of three parts: Siamese cross-channel fusion streams, similarity module and classification module. Each Siamese cross-channel fusion stream contains multiple branches, and each branch is supplemented by cross-channel fusion modules to improve multiscale temporal feature representation capability. The similarity module is adopted to measure the feature similarity between multiple branches. At the same time, the classification module provides a strong constraint to classify the features from all Siamese cross-channel fusion streams. The combination of the similarity module and classification module constitutes a new joint training strategy to further optimize the network performance.

RESULTS

The experiment is conducted on the public BCI Competition IV 2a and 2b datasets, and the results show that the proposed network achieves an average accuracy of 87.36% and 87.33%, respectively.

COMPARISON WITH EXISTING METHODS AND CONCLUSIONS

The proposed network adopts cross-channel fusion to learn multiscale temporal characteristics and joint training strategy to optimize the training process. Therefore, the performance outperforms other state-of-the-art MI-EEG signal classification methods.

Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks

The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods.

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain-Computer Interfaces: A Systematic Literature Review

Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain-computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

SD, Gomez-Vargas D, Jiménez MF, Múnera M, Cifuentes CA. Semi-Remote Gait Assistance Interface: A Joystick with Visual Feedback Capabilities for Therapists

The constant growth of pathologies affecting human mobility has led to developing of different assistive devices to provide physical and cognitive assistance. Smart walkers are a particular type of these devices since they integrate navigation systems, path-following algorithms, and user interaction modules to ensure natural and intuitive interaction. Although these functionalities are often implemented in rehabilitation scenarios, there is a need to actively involve the healthcare professionals in the interaction loop while guaranteeing safety for them and patients. This work presents the validation of two visual feedback strategies for the teleoperation of a simulated robotic walker during an assisted navigation task. For this purpose, a group of 14 clinicians from the rehabilitation area formed the validation group. A simple path-following task was proposed, and the feedback strategies were assessed through the kinematic estimation error (KTE) and a usability survey. A KTE of 0.28 m was obtained for the feedback strategy on the joystick. Additionally, significant differences were found through a Mann–Whitney–Wilcoxon test for the perception of behavior and confidence towards the joystick according to the modes of interaction (p-values of 0.04 and 0.01, respectively). The use of visual feedback with this tool contributes to research areas such as remote management of therapies and monitoring rehabilitation of people’s mobility.

A brain-actuated robotic arm system using non-invasive hybrid brain-computer interface and shared control strategy

Objective.The electroencephalography (EEG)-based brain-computer interfaces (BCIs) have been used in the control of robotic arms. The performance of non-invasive BCIs may not be satisfactory due to the poor quality of EEG signals, so the shared control strategies were tried as an alternative solution. However, most of the existing shared control methods set the arbitration rules manually, which highly depended on the specific tasks and developer's experience. In this study, we proposed a novel shared control model that automatically optimized the control commands in a dynamical way based on the context in real-time control. Besides, we employed the hybrid BCI to better allocate commands with multiple functions. The system allowed non-invasive BCI users to manipulate a robotic arm moving in a three-dimensional (3D) space and complete a pick-place task of multiple objects.Approach.Taking the scene information obtained by computer vision as a knowledge base, a machine agent was designed to infer the user's intention and generate automatic commands. Based on the inference confidence and user's characteristic, the proposed shared control model fused the machine autonomy and human intention dynamically for robotic arm motion optimization during the online control. In addition, we introduced a hybrid BCI scheme that applied steady-state visual evoked potentials and motor imagery to the divided primary and secondary BCI interfaces to better allocate the BCI resources (e.g. decoding computing power, screen occupation) and realize the multi-dimensional control of the robotic arm.Main results.Eleven subjects participated in the online experiments of picking and placing five objects that scattered at different positions in a 3D workspace. The results showed that most of the subjects could control the robotic arm to complete accurate and robust picking task with an average success rate of approximately 85% under the shared control strategy, while the average success rate of placing task controlled by pure BCI was 50% approximately.Significance.In this paper, we proposed a novel shared controller for motion automatic optimization, together with a hybrid BCI control scheme that allocated paradigms according to the importance of commands to realize multi-dimensional and effective control of a robotic arm. Our study indicated that the shared control strategy with hybrid BCI could greatly improve the performance of the brain-actuated robotic arm system.

Progress in Brain Computer Interface: Challenges and Opportunities

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications

In this paper, a Brain-Machine Interface (BMI) system is proposed to automatically control the navigation of wheelchairs by detecting the shadows on their route. In this context, a new algorithm to detect shadows in a single image is proposed. Specifically, a novel adaptive direction tracking filter (ADT) is developed to extract feature information along the direction of shadow boundaries. The proposed algorithm avoids extraction of features around all directions of pixels, which significantly improves the efficiency and accuracy of shadow features extraction. Higher-order statistics (HOS) features such as skewness and kurtosis in addition to other optical features are used as input to different Machine Learning (ML) based classifiers, specifically, a Multilayer Perceptron (MLP), Autoencoder (AE), 1D-Convolutional Neural Network (1D-CNN) and Support Vector Machine (SVM), to perform the shadow boundaries detection task. Comparative results demonstrate that the proposed MLP-based system outperforms all the other state-of-the-art approaches, reporting accuracy rates up to 84.63%.