Search

With the increasing popularity of autonomous vehicles, it is important to understand their user satisfaction because such aspects are likely to determine their future development. This article studied the effects of perceived benefit, perceived risk, interpersonal feelings, infrastructure adaptability, driving self-efficacy and mental workload on satisfaction when using autonomous vehicles, designed three road scenarios (simple, general, and complex), and established a structural equation model of satisfaction with mediators. A total of 553 responses were retrieved through online questionnaires, the path coefficients among various factors and indicators were calculated, and the importance of each factor in different road scenarios was discussed. The results show that the six factors have different association with user satisfaction in different road scenarios. The effect of mental workload on satisfaction in all road scenarios is mediated by self-efficacy. These results provide insights for the improvement of the autonomous vehicle industry.

Limited by the field of view of the computer-generated hologram (CGH), full-aperture measurement of long cylindrical mirror with large curvature have been challenging. This paper reports a stitching interferometry based on sub-aperture adjustment with an aberration model represented by 2D Chebyshev polynomials. Firstly, a mathematical model under null measurement between the sub-aperture adjustment and its corresponding aberration is established by combining 2-D Chebyshev polynomials, which can correct sub-aperture position deviation. Secondly, on the basis of the adjustment aberration model, a global stitching algorithm based on the self-residual iterative algorithm (SRIA) is proposed. The algorithm uses the global stitching matrix and the sub-aperture spatial position constraint of the adjustment aberration model to iterate repeatedly for sub-aperture position. The experimental results verify the validity and feasibility of the proposed method. Finally, compared with full-aperture measurements using CGH, the proposed stitching method can obtain high-precision smooth stitching surface, and the repeatability is better than 0.7 nm RMS (P-V).

Somatic embryogenesis is an example of induced cellular totipotency, where embryos develop from vegetative cells rather than from gamete fusion. Somatic embryogenesis can be induced in vitro by exposing explants to growth regulators and/or stress treatments. The BABY BOOM (BBM) and LEAFY COTYLEDON1 (LEC1) and LEC2 transcription factors are key regulators of plant cell totipotency, as ectopic overexpression of either transcription factor induces somatic embryo formation from Arabidopsis (Arabidopsis thaliana) seedlings without exogenous growth regulators or stress treatments. Although LEC and BBM proteins regulate the same developmental process, it is not known whether they function in the same molecular pathway. We show that BBM transcriptionally regulates LEC1 and LEC2, as well as the two other LAFL genes, FUSCA3 (FUS3) and ABSCISIC ACID INSENSITIVE3 (ABI3). LEC2 and ABI3 quantitatively regulate BBM-mediated somatic embryogenesis, while FUS3 and LEC1 are essential for this process. BBM-mediated somatic embryogenesis is dose and context dependent, and the contextdependent phenotypes are associated with differential LAFL expression. We also uncover functional redundancy for somatic embryogenesis among other Arabidopsis BBM-like proteins and show that one of these proteins, PLETHORA2, also regulates LAFL gene expression. Our data place BBM upstream of other major regulators of plant embryo identity and totipotency.

This paper develops an event-related potential (ERP)-based adaptive model for the control of humanoid robot movements in an environment cluttered with obstacles based on live video feedback. This model adaptively determines the repetition number according to an individual’s mental state to speed up the robot control cycle. N200 and P300 potential features increase in the frontal and occipital areas when using robot images as visual stimuli, so it is able to effectively recognize target visual stimuli by processing Fisher’s linear discriminant analysis (FLDA) and to identify a subject’s intention by using support vector machine (SVM), in parallel. The offline evaluations show that, compared with a nonadaptive model, the adaptive model increases the accuracy rate from 88.8% to 92.9%, a change of 4.1%, and the information transfer rate (ITR) from 41.3 to 46.3 bits/min, a change of 5.0 bits/min. Eight subjects participated in telepresence controlling a NAO humanoid robot to move in an office environment cluttered with obstacles. The successful maneuvers demonstrate that the brain-controlled humanoid robot can be applied for surveillance and exploration in unknown environments based on live video feedback, which are evaluated by using new metrics for the performance of the brain–robot interaction (BRI) system.

Variations in a lake area constitute an important indicator of the modifications of the lake hydrology. This paper explores the inter-annual variations of the Poyang Lake area during the dry seasons occurring within the 1961 to 2010 period and further quantifies the severity of dryness recently endured during the 2000s. A physically based hydrodynamic model of Poyang Lake established the relation between the lake area and lake level. The lake area was calculated using the observed lake water level. Results indicated the average lake area in the dry seasons was 1,015 km2. There was a considerable inter-annual variation of the minimum lake area that varied from 702.8 km2 to 1,259.7 km2. Poyang Lake experienced the most severe dryness in the 2000s, resulting in an average lake area during 2001 to 2010 of 124 km2 less than that of the preceding period. During the dry seasons, the catchment of the river discharge is likely the primary cause of the changes in lake area. This study evaluated the inter-annual variations of the Poyang Lake over a period of 50 years. Our results may provide support for an integrated management of the lake-catchment system, securing the water supply.

Setting up an asynchronous event-related potential based brain-computer interface system is a challenge, since low signal-to-noise ratio of event-related potential makes it hard to discriminate the work and the idle states of a brain. We find the visual interface based on an odd-ball paradigm can simultaneously evoke event-related potential and transient visual evoked potential. The frequency of the transient visual evoked potential is modulated by the flashing rate of the visual stimulus that is used to induce event-related potential. This paper proposes an asynchronous brain-computer interface system that combines these two kinds of induced potentials to detect the two mental states in the time-frequency domain. The novel method achieves an accuracy of 96.50%. Compared with the traditional method, it improves the accuracy of 46.50%.

Individual differences of P300 potentials lead to that a large amount of training data must be collected to construct pattern recognition models in P300-based brain-computer interface system, which may cause subjects' fatigue and degrade the system performance. TrAdaBoost is a method that transfers the knowledge from source area to target area, which improves learning effect in the target area. Our research purposed a TrAdaBoost-based linear discriminant analysis and a TrAdaBoost-based support vector machine to recognize the P300 potentials across multiple subjects. This method first trains two kinds of classifiers separately by using the data deriving from a small amount of data from same subject and a large amount of data from different subjects. Then it combines all the classifiers with different weights. Compared with traditional training methods that use only a small amount of data from same subject or mixed different subjects' data to directly train, our algorithm improved the accuracies by 19.56% and 22.25% respectively, and improved the information transfer rate of 14.69 bits/min and 15.76 bits/min respectively. The results indicate that the TrAdaBoost-based method has the potential to enhance the generalization ability of brain-computer interface on the individual differences.P300 电位的个体差异导致基于该电位的脑-机交互系统需要每位被试的大量训练数据来构建模式识别模型，引起被试的训练疲劳，并可能由此导致系统性能降低。TrAdaBoost 是一种把源领域的知识迁移到目标领域，进而使目标领域能获得更好的学习效果的迁移学习方法。本研究针对 P300 电位的跨脑辨识问题，提出基于 TrAdaBoost 的线性判别分类算法和支持向量机，将同被试的少量数据训练的分类器与不同被试的大量数据训练的分类器按权重组成融合分类器。与只采用少量同被试数据或者混合不同被试数据来直接进行训练的传统学习方式相比，本文算法在少量样本情况下将准确率分别提高了 19.56% 和 22.25%，信息传输率分别提高了 14.69 bits/min 和 15.76 bits/min，有望提高脑-机交互系统对被试个体差异的泛化能力。.

Eukaryotic genomes are densely packaged into hierarchical three-dimensional (3D) structures that contain information about gene regulation and many other biological processes. With the development of imaging and sequencing-based technologies, 3D genome studies have revealed that the high-order chromatin structure is composed of hierarchical levels, including chromosome territories, A/B compartments, topologically associated domains, and chromatin loops. However, how this chromatin architecture is formed and maintained is not completely clear. In this review, we introduce experimental methods to investigate the 3D genome, review major architectural proteins that regulate 3D chromatin organization in mammalian cells, such as CTCF (CCCTC-binding factor), cohesin, lamins, and transcription factors, and discuss relevant mechanisms such as phase separation.

The eukaryotic genome is folded into higher-order conformation accompanied with constrained dynamics for coordinated genome functions. However, the molecular machinery underlying these hierarchically organized chromatin architecture and dynamics remains poorly understood. Here by combining imaging and Hi-C sequencing, we studied the role of lamin B1 in chromatin architecture and dynamics. We found that lamin B1 depletion leads to chromatin redistribution and decompaction. Consequently, the inter-chromosomal interactions and overlap between chromosome territories are increased. Moreover, Hi-C data revealed that lamin B1 is required for the integrity and segregation of chromatin compartments but not for the topologically associating domains (TADs). We further proved that depletion of lamin B1 leads to increased chromatin dynamics, owing to chromatin decompaction and redistribution toward nuclear interior. Taken together, our data suggest that chromatin-lamin B1 interactions promote chromosomal territory segregation and genomic compartmentalization, and confine chromatin dynamics, supporting its crucial role in chromatin higher-order structure and dynamics.

// Junwu Zhao 1,2 , Zhenguang Li 2 , Yannan Cong 2 , Jinbiao Zhang 2 , Ming Tan 2 , Haixia Zhang 2 , Na Geng 2 , Mengfan Li 2 , Wenwen Yu 3 and Peiyan Shan 1 1 Department of Neurology, Qilu Hospital of Shandong University, Jinan, Shandong, China 2 Department of Neurology, Weihai Municipal Hospital, Weihai, Shandong, China 3 Department of Clinical Medicine (Neurology), Weifang Medical University, Weifang, Shandong, China Correspondence to: Peiyan Shan, email: // Keywords : rTMS, cognitive function, AD Received : September 12, 2016 Accepted : October 24, 2016 Published : November 03, 2016 Abstract Repetitive transcranial magnetic stimulation (rTMS) acts as a kind of widely-applied and non-invasive method in the intervention of some neurological disorders. This prospective, randomized, double-blind, placebo-controlled trial investigates the effect of rTMS on 30 cases of Alzheimer’s disease (AD) participants, who were classified into mild and moderate groups. Neuropsychological tests were carried out using the AD Assessment Scale-cognitive subscale (ADAS-cog), Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and World Health Organization University of California-Los Angeles, Auditory Verbal Learning Test (WHO-UCLA AVLT) before, immediately after, and 6 weeks after the intervention. In this work, data from 30 AD patients revealed that there was no obvious interaction effect of time-by-group. The ADAS-cog, MMSE and WHO-UCLA AVLT score in the rTMS group was significantly improved compared with baselines at 6 weeks after treatment (all p

This paper proposes a method for achieving a high performance of N200 and P300 classification by applying independent component analysis to select the channels, which deliver brain signals with large N200 and P300 potentials and small artifacts. In this study, the authors find out the relationship between the highest accuracy and the weights of the independent components and use this relationship to predict the optimal channels of each individual subject. They compare five channel selection methods: the ICA-based method and the curve-fitting-based method proposed in this paper, the amplitude-based method, the experiential optimal 8 channel combination and all 30 channel combination methods. The comparative studies show that the ICA-based method achieves an average accuracy of 99.3% across four subjects, which is superior to the other four methods.

Green products are gaining popularity because of their environmental friendliness compared with conventional products. This study examines a word-of-mouth (WOM) diffusion problem of a newly launched green product and investigates the effects of consumers’ environmental awareness (CEA). Analytical results show that CEA significantly affects green product diffusion in terms of firms’ pricing strategies and market shares. The market structure also has effects on diffusing a green product: the pricing strategies and environmental performance of diffusing a green product in a Bertrand competition market are different from those in a Stackelberg game market. When considering green-design decision, it is interesting to show that the green product’s price decreases with the increase of CEA, which is contrary to the pricing strategy without green design. From the numerical study of the CEA-featured WOM diffusion case, we find that CEA-featured WOM does not affect the pricing strategies of the green and conventional products, but does have a significant impact on the market shares of both products.

Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23.0 or 17.8 to 39.1 bits/min at an accuracy of 91.7%. These results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.

In this paper, a radial basis function neural network was used to solve the robot arm with 4 DOF of a self-designed service robot. Firstly, the D-H model was established for the manipulator and the analysis of forward kinematics was carried out, and the transition relation between the connecting rod coordinates was obtained. Then, according to this relationship, the sample data from joint space to Cartesian space was trained. Finally, the radial basis function neural network was used to find the inverse solution of the manipulator, based on a large amount number of the sample data, and the mapping relationship from the Cartesian space to the joint space. The method was verified by MATLAB simulation, and it can be used to solve the inverse kinematics of robot arm. It met the design requirements of the service robot in a certain range of errors.

An improved method based on the D-H method is proposed for a 5-DOF manipulator with special structure which is difficult to establish joint transformation by using normal D-H method. The forward kinematics of this manipulator with special structure is established by adding virtual joint. The method is suitable for manipulators with rotating joints, and the coordinate transformation must be achieved by transform along the Z axis. The process of inverse kinematics of the manipulator is analyzed. The conditions for the existence of inverse solution and the number of inverse solutions are also analyzed. Finally, the simulation of the proposed scheme is carried out to verify its correctness. And it has been applied to the physical mechanical arm.

In view of the shortcomings of the previous genetic algorithm in solving the problem of path planning of robot, this paper has improved it on the basis of the traditional genetic algorithm. The environment model is built by the grids and the fixed length serial number coding is used to encode. The random search method and the ascending order method are combined to generate the initial population. The preferred operator is introduced into the selection operator and this paper proposes a new adaptive adjustment formula which can automatically adjust the cross probability and the mutation probability on the basis of adaptive function value, which purpose is to avoid premature convergence. We use MATLAB to do simulation experiments. The result shows that this method make the path planning of robot more rapid and effective.

In this paper, a mobile robot path planning algorithm based on the rearrangement of gene is proposed for genetic algorithm and applied to solve the problem of mobile robot path planning. Firstly, it needs to build the robot path with the multi-plane model, and the genetic algorithm is used to search the optimal or sub optimal path. Then, with a new algorithm for the route of quadratic optimization, making the large rotation angle of optimization as a smooth path close to the flat angle under the premise of guarantee without intersecting any obstacles. The simulation results show that the algorithm combined with the classical genetic algorithm is better than the basic genetic algorithm, which can not only reduce the complexity of the genetic algorithm but also shorten the path length to obtain the relatively optimal path without collision.

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.

Nowadays, Mobile Internet has changed the Chinese shop mostly. The appearance of e-commerce mobile applications greatly overthrows the consumption structures and patterns in Chinese society. Merchandises consumers could only buy at malls before; they can now get them by simply touching the screen, and then receive at next day. Shopping efficiency has been enhanced significantly. Personal Center page, where viewing and tracking orders, payment and other acts needed to be done, plays a key role in mobile applications of e-commerce. So far, however, far too little attention has been paid to Personal Center pages. They are complex, short of ideas, and have too many levels with unfriendly interface. It is hard to give users good experience. Based on the Personal Center of the Pinzhi 365 mobile application, this innovation design, by the analysis of similar competing products, enhanced their accessibility, efficiency, memorability and friendliness, which create a model of innovation design in Personal Center interface.

Background/Aims: Integrin activation and lymphocyte migration to the vascular intima is a key event in early atherosclerosis. α4β7 integrin (LPAM-1) and its ligand, mucosal addressin cell adhesion molecule (MAdCAM-1) are known to play an important role in homing of activated lymphocytes to gut-associated lymphoid tissues. However, it is unclear whether α4β7 integrin is involved in the pathogenesis of atherosclerosis. Methods: The expressions of α4β7 integrin and its ligands in atherosclerosis plaques from 12 week high fat diet (HFD) fed ApoE-/- and C57BL/6 mice were examined using immunofluorescent and immunohistochemical assays, respectively. We also generated ApoE/β7 double deficient mice and compared atherosclerotic lesion development in β7+/+ApoE-/- and β7-/-ApoE-/- mice that were fed with HFD for 12 weeks. Results: We found an upregulation of α4β7 integrin and its ligands VCAM-1 and MAdCAM-1 at atherosclerosis plaques in Apolipoprotein E deficient (ApoE-/-) mice fed with HFD for 12 weeks. Over the 12 week HFD period, peripheral blood lymphocyte (PBL) expression of α4β7 integrin increased in parallel with aortic lesion size. A removal of α4β7 integrin by genetic deletion of the β7 chain in the ApoE-/- mouse resulted in a markedly decreased 12 week-HFD atherosclerotic plaque area. β7-/- ApoE-/- macrophages showed reduced acetylated and native LDL uptake and phagocytic activity, revealing possible roles for α4β7 at two distinct stages of macrophage dysfunction during atherogenesis. Finally, a reduced activity of integrin downstream signalling components focal adhesion kinase (FAK) and MAPK/ERK1/2 in macrophage indicates their possible engagement during α4β7 integrin signalling in atherosclerosis. Conclusions: Together our results reveal a critical role of α4β7 in diet-induced atherosclerosis in mouse.

In this paper, we develop an education-oriented portable brain-controlled robot system, consisting of a multi-parameter bioelectric signal acquisition device and a Lego NXT Mindstorms robot. The education-oriented portable brain-controlled robot system is only 45 cm3 in volume and weighs 70g. The signal acquisition section wirelessly acquires brain signals and the control section remotely navigates a Lego robot via brainwaves through a Bluetooth. Teenagers may use the low-cost brain-controlled robot to enjoy the pleasure of learning, to inspire their innovative ability and creativity, and to raise their mental concentration. Moreover, the proposed brain-controlled robot system might be used to improve the quality of adolescent education and autism recovery. The system modules include the brain signal acquisition, signal processing, and the robot control, which are developed in LabVIEW-based programming environment and easy to be expanded and upgraded. The comparative study of the education-oriented system and Cerebot equipped with the high precision Cerebus system widely used in laboratory research shows that the low-cost system delivers comparable accuracy for the robot control. An operation task is presented to train users' attentions and concentrations.

Brain-Robot Interaction (BRI), which provides an innovative communication pathway between human and a robotic device via brain signals, is prospective in helping the disabled in their daily lives. The overall goal of our method is to establish an SSVEP-based experimental procedure by integrating multiple software programs, such as OpenViBE, Choregraph, and Central software as well as user developed programs written in C++ and MATLAB, to enable the study of brain-robot interaction with humanoid robots. This is achieved by first placing EEG electrodes on a human subject to measure the brain responses through an EEG data acquisition system. A user interface is used to elicit SSVEP responses and to display video feedback in the closed-loop control experiments. The second step is to record the EEG signals of first-time subjects, to analyze their SSVEP features offline, and to train the classifier for each subject. Next, the Online Signal Processor and the Robot Controller are configured for the online control of a humanoid robot. As the final step, the subject completes three specific closed-loop control experiments within different environments to evaluate the brain-robot interaction performance. The advantage of this approach is its reliability and flexibility because it is developed by integrating multiple software programs. The results show that using this approach, the subject is capable of interacting with the humanoid robot via brain signals. This allows the mind-controlled humanoid robot to perform typical tasks that are popular in robotic research and are helpful in assisting the disabled.

This paper proposes a method for achieving a high performance of N200 and P300 classification, which applies independent component analysis (ICA) to select the channels whose brain signals contain large N200 and P300 potentials and small artifacts as the optimal channels to extract the features. The study results show that our method achieves an average accuracy of 99.3% over 4 subjects.

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.

This paper investigates controlling humanoid robot behavior via motion-onset specific N200 potentials. In this study, N200 potentials are induced by moving a blue bar through robot images intuitively representing robot behaviors to be controlled with mind. We present the individual impact of each subject on N200 potentials and discuss how to deal with individuality to obtain a high accuracy. The study results document the off-line average accuracy of 93% for hitting targets across over five subjects, so we use this major component of the motion-onset visual evoked potential (mVEP) to code people's mental activities and to perform two types of on-line operation tasks: navigating a humanoid robot in an office environment with an obstacle and picking-up an object. We discuss the factors that affect the on-line control success rate and the total time for completing an on-line operation task.

In this paper, we present an SSVEP-based hierarchical architecture for control of a humanoid robot with mind, consisting of five-layer decisions from robot state level to implementation level. This architecture is able to control a variety of humanoid robot behaviors at different levels to perform a complex task. We implement this hierarchical architecture on our Cerebot platform and test this control architecture in a multi-task experiment. We compare its control performance with the one achieved using manual control by an experienced operator. The results show that the architecture with coordinating multi-layer decisions and fusing human and robot actions is a good way to solve the issue of the limited information transfer rate (ITR) in mind-controlled humanoid robot system using current EEG-based BCI technology.

In this paper, we present a N200 model for control of a humanoid robot with mind. N200 is a major component of the motion visual evoked potential (mVEP) which can be used to detect the subject's intention. In order to acquire N200, we design a visual stimulus interface in which the activation of a stimulus is a bar scanning above the image of a humanoid robot behavior. By analyzing brain signals induced by this kind of stimulus and calculating some system indexes, the results of this study demonstrate that the designed interface can induce prominent N200 potentials, and another component P300 induced by this experiment can be considered as another characteristic of the feature vector to contribute to the classification. To our best knowledge, this paper would be the first report on an application of N200 model to control a humanoid robot with visual feedback in real time.

In this paper, we present a P300 model for control of Cerebot – a mind-controlled humanoid robot, including a procedure of acquiring P300 signals, topographical distribution analysis of P300 signals, and a classification approach to identifying subjects’ mental activities regarding robot-walking behavior.

In this paper, we develop a brainwave-based control system for Cerebot, consisting of a humanoid robot and a Cerebus™ Data Acquisition System up to 128 channels. Under the OpenViBE programming environment, the control system integrates OpenGL, OpenCV, WEBOTS, Choregraph, Central software, and user-developed programs in C++ and Matlab. The proposed system is easy to be expanded or upgraded. Firstly, we describe the system structures for off-line analysis of acquired neural signals and for on-line control of a humanoid robot via brainwaves. Secondly, we discuss how to use the toolboxes provided with the OpenViBE environment to design three types of brainwave-based models: SSVEPs, P300s, and mu/beta rhythms. Finally, we use the Cerebot platform to investigate the three models by controlling four robot-walking behaviors: turning right, turning left, walking forward, and walking backward.