Traditional Brain-computer Interface Research Articles

The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked potential

Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a "third hand", offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.

Benchmarking brain–computer interface algorithms: Riemannian approaches vs convolutional neural networks

Objective.To date, a comprehensive comparison of Riemannian decoding methods with deep convolutional neural networks for EEG-based brain-computer interfaces remains absent from published work. We address this research gap by using MOABB, The Mother Of All BCI Benchmarks, to compare novel convolutional neural networks to state-of-the-art Riemannian approaches across a broad range of EEG datasets, including motor imagery, P300, and steady-state visual evoked potentials paradigms.Approach.We systematically evaluated the performance of convolutional neural networks, specifically EEGNet, shallow ConvNet, and deep ConvNet, against well-established Riemannian decoding methods using MOABB processing pipelines. This evaluation included within-session, cross-session, and cross-subject methods, to provide a practical analysis of model effectiveness and to find an overall solution that performs well across different experimental settings.Main results.We find no significant differences in decoding performance between convolutional neural networks and Riemannian methods for within-session, cross-session, and cross-subject analyses.Significance.The results show that, when using traditional Brain-Computer Interface paradigms, the choice between CNNs and Riemannian methods may not heavily impact decoding performances in many experimental settings. These findings provide researchers with flexibility in choosing decoding approaches based on factors such as ease of implementation, computational efficiency or individual preferences.

Continuous tracking using deep learning-based decoding for noninvasive brain-computer interface.

Brain-computer interfaces (BCI) using electroencephalography provide a noninvasive method for users to interact with external devices without the need for muscle activation. While noninvasive BCIs have the potential to improve the quality of lives of healthy and motor-impaired individuals, they currently have limited applications due to inconsistent performance and low degrees of freedom. In this study, we use deep learning (DL)-based decoders for online continuous pursuit (CP), a complex BCI task requiring the user to track an object in 2D space. We developed a labeling system to use CP data for supervised learning, trained DL-based decoders based on two architectures, including a newly proposed adaptation of the PointNet architecture, and evaluated the performance over several online sessions. We rigorously evaluated the DL-based decoders in a total of 28 human participants, and found that the DL-based models improved throughout the sessions as more training data became available and significantly outperformed a traditional BCI decoder by the last session. We also performed additional experiments to test an implementation of transfer learning by pretraining models on data from other subjects, and midsession training to reduce intersession variability. The results from these experiments showed that pretraining did not significantly improve performance, but updating the models' midsession may have some benefit. Overall, these findings support the use of DL-based decoders for improving BCI performance in complex tasks like CP, which can expand the potential applications of BCI devices and help to improve the quality of lives of healthy and motor-impaired individuals.

The role of eye movement signals in non-invasive brain-computer interface typing system.

Brain-Computer Interfaces (BCIs) have shown great potential in providing communication and control for individuals with severe motor disabilities. However, traditional BCIs that rely on electroencephalography (EEG) signals suffer from low information transfer rates and high variability across users. Recently, eye movement signals have emerged as a promising alternative due to their high accuracy and robustness. Eye movement signals are the electrical or mechanical signals generated by the movements and behaviors of the eyes, serving to denote the diverse forms of eye movements, such as fixations, smooth pursuit, and other oculomotor activities like blinking. This article presents a review of recent studies on the development of BCI typing systems that incorporate eye movement signals. We first discuss the basic principles of BCI and the recent advancements in text entry. Then, we provide a comprehensive summary of the latest advancements in BCI typing systems that leverage eye movement signals. This includes an in-depth analysis of hybrid BCIs that are built upon the integration of electrooculography (EOG) and eye tracking technology, aiming to enhance the performance and functionality of the system. Moreover, we highlight the advantages and limitations of different approaches, as well as potential future directions. Overall, eye movement signals hold great potential for enhancing the usability and accessibility of BCI typing systems, and further research in this area could lead to more effective communication and control for individuals with motor disabilities.

BCI Application in Stroke Rehabilitation: Robotic Assisted System

Stroke remains a pervasive global health concern, exacting enduring motor, cognitive, and emotional tolls on affected individuals. The evolving landscape of neural interfaces has recently shown promising strides in reinstating lost sensorimotor functions. Non-invasive brain-computer interfaces (BCIs) have gained widespread recognition for their inherent advantages—simplicity, safety, and cost-effectiveness. The ongoing refinement of BCIs involves addressing previous technological and neurophysiological limitations, focusing on understanding distinctive neurophysiological alterations observed in individuals with disabilities. The exploration of brain connectivity attributes is pivotal in implementing BCI-based control systems. Emerging as a notable prospect for post-stroke motor rehabilitation, Brain-Computer Interface (BCI)-based therapy has made significant progress in restoring sensorimotor function. Revolutionary advancements have been made to traditional brain-computer interface (BCI) systems, which rely on brain signals obtained from electroencephalography (EEG) and rule-based translation algorithms. This comprehensive review delves into the realm of BCI Robotic Assisted Systems, offering a nuanced analysis of contributions from diverse researchers. It meticulously dissects these systems' advantages and limitations, providing valuable insights into their efficacy. Ongoing endeavors within the field prioritize enhancing the portability, simplicity, and cost-effectiveness of BCI technology, ultimately refining its overall usability. As research progresses, emphasizing larger sample sizes, there is a tangible potential to augment the reliability of BCI systems for stroke rehabilitation significantly. This trajectory holds promise for extending the benefits of BCI technology to a broader spectrum of patients, marking a transformative leap in neurorehabilitation methodologiest.

Identifying temporal correlations between natural single-shot videos and EEG signals

Objective. Electroencephalography (EEG) is a widely used technology for recording brain activity in brain-computer interface (BCI) research, where understanding the encoding-decoding relationship between stimuli and neural responses is a fundamental challenge. Recently, there is a growing interest in encoding-decoding natural stimuli in a single-trial setting, as opposed to traditional BCI literature where multi-trial presentations of synthetic stimuli are commonplace. While EEG responses to natural speech have been extensively studied, such stimulus-following EEG responses to natural video footage remain underexplored. Approach. We collect a new EEG dataset with subjects passively viewing a film clip and extract a few video features that have been found to be temporally correlated with EEG signals. However, our analysis reveals that these correlations are mainly driven by shot cuts in the video. To avoid the confounds related to shot cuts, we construct another EEG dataset with natural single-shot videos as stimuli and propose a new set of object-based features. Main results. We demonstrate that previous video features lack robustness in capturing the coupling with EEG signals in the absence of shot cuts, and that the proposed object-based features exhibit significantly higher correlations. Furthermore, we show that the correlations obtained with these proposed features are not dominantly driven by eye movements. Additionally, we quantitatively verify the superiority of the proposed features in a match-mismatch task. Finally, we evaluate to what extent these proposed features explain the variance in coherent stimulus responses across subjects. Significance. This work provides valuable insights into feature design for video-EEG analysis and paves the way for applications such as visual attention decoding.

Brain-computer interface paradigms and neural coding.

Brain signal patterns generated in the central nervous system of brain-computer interface (BCI) users are closely related to BCI paradigms and neural coding. In BCI systems, BCI paradigms and neural coding are critical elements for BCI research. However, so far there have been few references that clearly and systematically elaborated on the definition and design principles of the BCI paradigm as well as the definition and modeling principles of BCI neural coding. Therefore, these contents are expounded and the existing main BCI paradigms and neural coding are introduced in the review. Finally, the challenges and future research directions of BCI paradigm and neural coding were discussed, including user-centered design and evaluation for BCI paradigms and neural coding, revolutionizing the traditional BCI paradigms, breaking through the existing techniques for collecting brain signals and combining BCI technology with advanced AI technology to improve brain signal decoding performance. It is expected that the review will inspire innovative research and development of the BCI paradigm and neural coding.

Non-invasive Brain-Computer Interfaces: State of the Art and Trends.

Brain-computer interface (BCI) is a rapidly evolving technology that has the potential to widely influence research, clinical and recreational use. Non-invasive BCI approaches are particularly common as they can impact a large number of participants safely and at a relatively low cost. Where traditional non-invasive BCIs were used for simple computer cursor tasks, it is now increasingly common for these systems to control robotic devices for complex tasks that may be useful in daily life. In this review, we provide an overview of the general BCI framework as well as the various methods that can be used to record neural activity, extract signals of interest, and decode brain states. In this context, we summarize the current state-of-the-art of non-invasive BCI research, focusing on trends in both the application of BCIs for controlling external devices and algorithm development to optimize their use. We also discuss various open-source BCI toolboxes and software, and describe their impact on the field at large.

Adaptive filters based efficient EEG classification for steady state visually evoked potential based BCI system

Brain-computer interfaces (BCIs) system is a link to generate a communication between disable people and physical devices. Thus, steady state visually evoked potential (SSVEP) is analysed to improve performance efficiency of BCIs system using multi-class classification process. Thus, an adaptive filtering-based component analysis (AFCA) method is adopted to examine SSVEP from multiple-channel electroencephalography (EEG) signals for BCIs system efficiency enhancement. Further, flickering at varied frequencies is used in a visual stimulation process to examine user intentions and brain responses. A detailed solution for optimization problem and efficient feature extraction is also presented. Here, a large SSVEP dataset is utilized which contains 256 channel EEG data. Experimental results are evaluated in terms of classification accuracy and information transfer rate to measure efficiency of proposed SSVEP extraction method against varied traditional SSVEP-based BCIs. The average information transfer rate (ITR) results are 308.23 bits per minute and classification accuracy is 93.48% using proposed AFCA method. Thus, proposed AFCA method shows decent performance in comparison with state-of-art-SSVEP extraction methods.

Subject-independent EEG classification based on a hybrid neural network.

A brain-computer interface (BCI) based on the electroencephalograph (EEG) signal is a novel technology that provides a direct pathway between human brain and outside world. For a traditional subject-dependent BCI system, a calibration procedure is required to collect sufficient data to build a subject-specific adaptation model, which can be a huge challenge for stroke patients. In contrast, subject-independent BCI which can shorten or even eliminate the pre-calibration is more time-saving and meets the requirements of new users for quick access to the BCI. In this paper, we design a novel fusion neural network EEG classification framework that uses a specially designed generative adversarial network (GAN), called a filter bank GAN (FBGAN), to acquire high-quality EEG data for augmentation and a proposed discriminative feature network for motor imagery (MI) task recognition. Specifically, multiple sub-bands of MI EEG are first filtered using a filter bank approach, then sparse common spatial pattern (CSP) features are extracted from multiple bands of filtered EEG data, which constrains the GAN to maintain more spatial features of the EEG signal, and finally we design a convolutional recurrent network classification method with discriminative features (CRNN-DF) to recognize MI tasks based on the idea of feature enhancement. The hybrid neural network proposed in this study achieves an average classification accuracy of 72.74 ± 10.44% (mean ± std) in four-class tasks of BCI IV-2a, which is 4.77% higher than the state-of-the-art subject-independent classification method. A promising approach is provided to facilitate the practical application of BCI.

Eliminating or Shortening the Calibration for a P300 Brain-Computer Interface Based on a Convolutional Neural Network and Big Electroencephalography Data: An Online Study.

A brain-computer interface (BCI) measures and analyzes brain activity and converts it into computer commands to control external devices. Traditional BCIs usually require full calibration, which is time-consuming and makes BCI systems inconvenient to use. In this study, we propose an online P300 BCI spelling system with zero or shortened calibration based on a convolutional neural network (CNN) and big electroencephalography (EEG) data. Specifically, three methods are proposed to train CNNs for the online detection of P300 potentials: (i) training a subject-independent CNN with data collected from 150 subjects; (ii) adapting the CNN online via a semisupervised learning/self-training method based on unlabeled data collected during the user's online operation; and (iii) fine-tuning the CNN with a transfer learning method based on a small quantity of labeled data collected before the user's online operation. Note that the calibration process is eliminated in the first two methods and dramatically shortened in the third method. Based on these methods, an online P300 spelling system is developed. Twenty subjects participated in our online experiments. Average accuracies of 89.38%, 94.00% and 93.50% were obtained by the subject-independent CNN, the self-training-based CNN and the transfer learning-based CNN, respectively. These results demonstrate the effectiveness of our methods, and thus, the convenience of the online P300-based BCI system is substantially improved.

Impact of Generative AI Models on Neurocybernetics for Enhancing Brain-Computer Interface Adaptability in Motor Disabilities

Motor disabilities can arise from conditions such as stroke, spinal cord injuries, or neurodegenerative diseases, severely limiting an individual's movement and communication abilities. Brain-computer interfaces (BCIs) represent a promising technology aimed at establishing a direct connection between the brain and external devices, allowing individuals with motor impairments to control assistive technologies using their brain signals. However, conventional BCIs often rely on fixed signal patterns or require extensive user training, presenting challenges for some users and constraining system flexibility. Variability in brain signals and the inability to adapt further impede widespread BCI adoption among this population. This study explores the integration of generative AI models to enhance BCI adaptability for people with motor disabilities. By leveraging generative AI, our framework generates realistic brain signals tailored to each user's specific characteristics. Through rigorous experimentation and case studies, we demonstrate the efficacy of our approach in improving BCI performance and usability. Our findings underscore the transformative potential of generative artificial intelligence in neurocybernetics, illustrating its capacity to advance BCI accessibility and effectiveness in rehabilitating motor disabilities. The limitations of traditional BCIs through innovative AI-driven solutions, this research contributes to the evolving field of assistive technologies. It highlights the pivotal role of generative AI in fostering greater autonomy and quality of life for individuals with motor impairments, paving the way for more inclusive and responsive neurotechnology in the future.

Advances in P300 brain-computer interface spellers: toward paradigm design and performance evaluation.

A brain-computer interface (BCI) is a non-muscular communication technology that provides an information exchange channel for our brains and external devices. During the decades, BCI has made noticeable progress and has been applied in many fields. One of the most traditional BCI applications is the BCI speller. This article primarily discusses the progress of research into P300 BCI spellers and reviews four types of P300 spellers: single-modal P300 spellers, P300 spellers based on multiple brain patterns, P300 spellers with multisensory stimuli, and P300 spellers with multiple intelligent techniques. For each type of P300 speller, we further review several representative P300 spellers, including their design principles, paradigms, algorithms, experimental performance, and corresponding advantages. We particularly emphasized the paradigm design ideas, including the overall layout, individual symbol shapes and stimulus forms. Furthermore, several important issues and research guidance for the P300 speller were identified. We hope that this review can assist researchers in learning the new ideas of these novel P300 spellers and enhance their practical application capability.

Hybrid mental tasks based human computer interface via integration of pronunciation and motor imagery

Objective. Among the existing active brain–computer interfaces (BCI), the motor imagination (MI) is widely used. To operate the MI BCI effectively, subjects need to carry out trainings on corresponding imagining tasks. Here, we studied how to reduce the discomfort and fatigue of active BCI imaginary tasks and the inability to concentrate on them while improving the accuracy. Approach. This paper proposes a hybrid BCI composed of MI and pronunciation imagination (PI). The electroencephalogram signals of ten subjects are recognized by the adaptive Riemannian distance classification and the improved frequency selective filter-bank Common Spatial Pattern recognition. Main results. The results show that under the new paradigm with the combination of MI and PI, the recognition accuracy is higher than the MI alone. The highest recognition rate of the proposed hybrid system can reach more than 90%. Furthermore, through the subjects’ scoring results of the operation difficulty, it is concluded that the designed hybrid paradigm is more operable than the traditional BCI paradigm. Significance. The separable tasks in the active BCI are limited and the accuracy needs to be improved. The new hybrid paradigm proposed by us improves the accuracy and operability of the active BCI system, providing a new possibility for the research direction of the active BCI.

BARI: An Affordable Brain-Augmented Reality Interface to Support Human–Robot Collaboration in Assembly Tasks

Human–robot collaboration (HRC) is a new and challenging discipline that plays a key role in Industry 4.0. Digital transformation of industrial plants aims to introduce flexible production lines able to adapt to different products quickly. In this scenario, HRC can be a booster to support flexible manufacturing, thus introducing new interaction paradigms between humans and machines. Augmented reality (AR) can convey much important information to users: for instance, information related to the status and the intention of the robot/machine the user is collaborating with. On the other hand, traditional input interfaces based on physical devices, gestures, and voice might be precluded in industrial environments. Brain–computer interfaces (BCIs) can be profitably used with AR devices to provide technicians solutions to effectively collaborate with robots. This paper introduces a novel BCI–AR user interface based on the NextMind and the Microsoft Hololens 2. Compared to traditional BCI interfaces, the NextMind provides an intuitive selection mechanism based on visual cortex signals. This interaction paradigm is exploited to guide a collaborative robotic arm for a pick and place selection task. Since the ergonomic design of the NextMind allows its use in combination with the Hololens 2, users can visualize through AR the different parts composing the artifact to be assembled, the visual elements used by the NextMind to enable the selections, and the robot status. In this way, users’ hands are always free, and the focus can be always on the objects to be assembled. Finally, user tests are performed to evaluate the proposed system, assessing both its usability and the task’s workload; preliminary results are very encouraging, and the proposed solution can be considered a starting point to design and develop affordable hybrid-augmented interfaces to foster real-time human–robot collaboration.

Associative cued asynchronous BCI induces cortical plasticity in stroke patients.

ObjectiveWe propose a novel cue‐based asynchronous brain–computer interface(BCI) for neuromodulation via the pairing of endogenous motor cortical activity with the activation of somatosensory pathways.MethodsThe proposed BCI detects the intention to move from single‐trial EEG signals in real time, but, contrary to classic asynchronous‐BCI systems, the detection occurs only during time intervals when the patient is cued to move. This cue‐based asynchronous‐BCI was compared with two traditional BCI modes (asynchronous‐BCI and offline synchronous‐BCI) and a control intervention in chronic stroke patients. The patients performed ankle dorsiflexion movements of the paretic limb in each intervention while their brain signals were recorded. BCI interventions decoded the movement attempt and activated afferent pathways via electrical stimulation. Corticomotor excitability was assessed using motor‐evoked potentials in the tibialis‐anterior muscle induced by transcranial magnetic stimulation before, immediately after, and 30 min after the intervention.ResultsThe proposed cue‐based asynchronous‐BCI had significantly fewer false positives/min and false positives/true positives (%) as compared to the previously developed asynchronous‐BCI. Linear‐mixed‐models showed that motor‐evoked potential amplitudes increased following all BCI modes immediately after the intervention compared to the control condition (p <0.05). The proposed cue‐based asynchronous‐BCI resulted in the largest relative increase in peak‐to‐peak motor‐evoked potential amplitudes(141% ± 33%) among all interventions and sustained it for 30 min(111% ± 33%).InterpretationThese findings prove the high performance of a newly proposed cue‐based asynchronous‐BCI intervention. In this paradigm, individuals receive precise instructions (cue) to promote engagement, while the timing of brain activity is accurately detected to establish a precise association with the delivery of sensory input for plasticity induction.

Novel Hybrid Brain-Computer Interface for Virtual Reality Applications Using Steady-State Visual-Evoked Potential-Based Brain-Computer Interface and Electrooculogram-Based Eye Tracking for Increased Information Transfer Rate.

Brain–computer interfaces (BCIs) based on electroencephalogram (EEG) have recently attracted increasing attention in virtual reality (VR) applications as a promising tool for controlling virtual objects or generating commands in a “hands-free” manner. Video-oculography (VOG) has been frequently used as a tool to improve BCI performance by identifying the gaze location on the screen, however, current VOG devices are generally too expensive to be embedded in practical low-cost VR head-mounted display (HMD) systems. In this study, we proposed a novel calibration-free hybrid BCI system combining steady-state visual-evoked potential (SSVEP)-based BCI and electrooculogram (EOG)-based eye tracking to increase the information transfer rate (ITR) of a nine-target SSVEP-based BCI in VR environment. Experiments were repeated on three different frequency configurations of pattern-reversal checkerboard stimuli arranged in a 3 × 3 matrix. When a user was staring at one of the nine visual stimuli, the column containing the target stimulus was first identified based on the user’s horizontal eye movement direction (left, middle, or right) classified using horizontal EOG recorded from a pair of electrodes that can be readily incorporated with any existing VR-HMD systems. Note that the EOG can be recorded using the same amplifier for recording SSVEP, unlike the VOG system. Then, the target visual stimulus was identified among the three visual stimuli vertically arranged in the selected column using the extension of multivariate synchronization index (EMSI) algorithm, one of the widely used SSVEP detection algorithms. In our experiments with 20 participants wearing a commercial VR-HMD system, it was shown that both the accuracy and ITR of the proposed hybrid BCI were significantly increased compared to those of the traditional SSVEP-based BCI in VR environment.

System Architecture for Brain-Computer Interface based on Machine Learning and Internet of Things

Brain functions are required to be read for curing neurological illness. Brain-Computer Interface (BCI) connects the brain to the digital world for brain signals receiving, recording, processing, and comprehending. With a Brain-Computer Interface (BCI), the information from the user’s brain is fed into actuation devices, which then carry out the actions programmed into them. The Internet of Things (IoT) has made it possible to connect a wide range of everyday devices. Asynchronous BCIs can benefit from an improved system architecture proposed in this paper. Individuals with severe motor impairments will particularly get benefit from this feature. Control commands were translated using a rule-based translation algorithm in traditional BCI systems, which relied only on EEG recordings of brain signals. Examining BCI technology’s various and cross-disciplinary applications, this argument produces speculative conclusions about how BCI instruments combined with machine learning algorithms could affect the forthcoming procedures and practices. Compressive sensing and neural networks are used to compress and reconstruct ECoG data presented in this article. The neural networks are used to combine the classifier outputs adaptively based on the feedback. A stochastic gradient descent solver is employed to generate a multi-layer perceptron regressor. An example network is shown to take a 50% compression ratio and 89% reconstruction accuracy after training with real-world, medium-sized datasets as shown in this paper.

Magnetometers vs Gradiometers for Neural Speech Decoding.

Neural speech decoding aims at providing natural rate communication assistance to patients with locked-in state (e.g. due to amyotrophic lateral sclerosis, ALS) in contrast to the traditional brain-computer interface (BCI) spellers which are slow. Recent studies have shown that Magnetoencephalography (MEG) is a suitable neuroimaging modality to study neural speech decoding considering its excellent temporal resolution that can characterize the fast dynamics of speech. Gradiometers have been the preferred choice for sensor space analysis with MEG, due to their efficacy in noise suppression over magnetometers. However, recent development of optically pumped magnetometers (OPM) based wearable-MEG devices have shown great potential in future BCI applications, yet, no prior study has evaluated the performance of magnetometers in neural speech decoding. In this study, we decoded imagined and spoken speech from the MEG signals of seven healthy participants and compared the performance of magnetometers and gradiometers. Experimental results indicated that magnetometers also have the potential for neural speech decoding, although the performance was significantly lower than that obtained with gradiometers. Further, we implemented a wavelet based denoising strategy that improved the performance of both magnetometers and gradiometers significantly. These findings reconfirm that gradiometers are preferable in MEG based decoding analysis but also provide the possibility towards the use of magnetometers (or OPMs) for the development of the next-generation speech-BCIs.

A coloring and timing brain-computer interface for the nursing bed robot

The patient with a severely spinal injury needs an intention-controlled nursing bed (NB) robot to help the body actions. To recognize human intentions, the brain–computer interface (BCI) of the steady-state visual evoked potentials (SSVEPs) is usually used. For the BCI of the SSVEPs, the visual fatigue should be alleviated, and the intention recognition accuracy should be improved. Therefor this paper proposed a coloring and timing BCI (CT-BCI) to alleviate the visual fatigue, and find the optimal Wavelet vanishing moment parameter to improve the intention recognition accuracy. In addition, the intention coding and decoding are designed and implemented. Furthermore, the wireless communications are used among the CT-BCI and the robots in the internet of things. Experiments show that the proposed CT-BCI decreases the average evocation time for each intention by 0.8 s, and prolongs the eye non-fatigue duration by 5.45 s, and improves the intention recognition accuracy by 6.7% compared with the traditional BCI.