Brain-controlled Wheelchair Research Articles

A Brain-Controlled and User-Centered Intelligent Wheelchair: A Feasibility Study.

Recently, due to physical aging, diseases, accidents, and other factors, the population with lower limb disabilities has been increasing, and there is consequently a growing demand for wheelchair products. Modern product design tends to be more intelligent and multi-functional than in the past, with the popularization of intelligent concepts. This supports the design of a new, fully functional, intelligent wheelchair that can assist people with lower limb disabilities in their day-to-day life. Based on the UCD (user-centered design) concept, this study focused on the needs of people with lower limb disabilities. Accordingly, the demand for different functions of intelligent wheelchair products was studied through a questionnaire survey, interview survey, literature review, expert consultation, etc., and the function and appearance of the intelligent wheelchair were then defined. A brain-machine interface system was developed for controlling the motion of the intelligent wheelchair, catering to the needs of disabled individuals. Furthermore, ergonomics theory was used as a guide to determine the size of the intelligent wheelchair seat, and eventually, a new intelligent wheelchair with the features of climbing stairs, posture adjustment, seat elevation, easy interaction, etc., was developed. This paper provides a reference for the design upgrade of the subsequently developed intelligent wheelchair products.

Online BCI systems: cross-subject motor imagery classification based on weighted time-domain feature extraction methods

Motor imagery electroencephalogram (MI-EEG) is becoming increasingly important. This paper solves the problem of online signal recognition for motor imagery across subjects by finding common features across multiple subjects to improve the generality of the classification model. We analysed the EEG data from left/right-hand motor imagery of eight subjects and proposed a weighted time-domain (WTD) feature extraction method based on a weighted channel screening method. The classification model constructed by combining this feature extraction method with the support vector machine (SVM) classification method was faster in classification and achieved good cross-subject classification accuracy (The average offline classification accuracy was 91.39%). In this paper, an online control system for asynchronous brain-controlled wheelchairs was built with good performance. The online average motor imagery classification accuracy was 81.67%, and the average response time was 1.36s. This method contributes to bringing the online Brain-computer interface (BCI) system out of the laboratory and into wider application.

Design and Implementation of P300 Brain-Controlled Wheelchair with a Developed Wireless DA Converter

This article presents a P300 brain-controlled wheelchair system utilizing a wireless Digital-to-Analog converter for signal transmission. The wireless Digital-to-Analog converter addresses issues with device connectivity and simplifies signal transmission, removing the need for complex serial port protocols. A support vector machine model is trained to extract the P300 component from the Electroencephalogram signal. A P300 stimulator is designed to elicit the P300 component response, with subjects controlling the wheelchair's movement by looking at randomly flickering white circles. Experimental validation is conducted on a modified wheelchair, demonstrating the effectiveness and reliability of the proposed method.

Brain-Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control.

Brain-computer interfaces (BCIs) are widely utilized in control applications for people with severe physical disabilities. Several researchers have aimed to develop practical brain-controlled wheelchairs. An existing electroencephalogram (EEG)-based BCI based on steady-state visually evoked potential (SSVEP) was developed for device control. This study utilized a quick-response (QR) code visual stimulus pattern for a robust existing system. Four commands were generated using the proposed visual stimulation pattern with four flickering frequencies. Moreover, we employed a relative power spectrum density (PSD) method for the SSVEP feature extraction and compared it with an absolute PSD method. We designed experiments to verify the efficiency of the proposed system. The results revealed that the proposed SSVEP method and algorithm yielded an average classification accuracy of approximately 92% in real-time processing. For the wheelchair simulated via independent-based control, the proposed BCI control required approximately five-fold more time than the keyboard control for real-time control. The proposed SSVEP method using a QR code pattern can be used for BCI-based wheelchair control. However, it suffers from visual fatigue owing to long-time continuous control. We will verify and enhance the proposed system for wheelchair control in people with severe physical disabilities.

Brain-Controlled 2D Navigation Robot Based on a Spatial Gradient Controller and Predictive Environmental Coordinator.

Brain-computer interfaces (BCIs) have been used in two-dimensional (2D) navigation robotic devices, such as brain-controlled wheelchairs and brain-controlled vehicles. However, contemporary BCI systems are driven by binary selective control. On the one hand, only directional information can be transferred from humans to machines, such as "turn left" or "turn right", which means that the quantified value, such as the radius of gyration, cannot be controlled. In this study, we proposed a spatial gradient BCI controller and corresponding environment coordinator, by which the quantified value of brain commands can be transferred in the form of a 2D vector, improving the flexibility, stability and efficiency of BCIs. A horizontal array of steady-state visual stimulation was arranged to excite subject (EEG) signals. Covariance arrays between subjects' electroencephalogram (EEG) and stimulation features were mapped into quantified 2-dimensional vectors. The generated vectors were then inputted into the predictive controller and fused with virtual forces generated by the robot's predictive environment coordinator in the form of vector calculation. The resultant vector was then interpreted into the driving force for the robot, and real-time speed feedback was generated. The proposed SGC controller generated a faster (27.4 s vs. 34.9 s) response for the single-obstacle avoidance task than the selective control approach. In practical multiobstacle tasks, the proposed robot executed 39% faster in the target-reaching tasks than the selective controller and had better robustness in multiobstacle avoidance tasks (average failures significantly dropped from 27% to 4%). This research proposes a new form of brain-machine shared control strategy that quantifies brain commands in the form of a 2-D control vector stream rather than selective constant values. Combined with a predictive environment coordinator, the brain-controlled strategy of the robot is optimized and provided with higher flexibility. The proposed controller can be used in brain-controlled 2D navigation devices, such as brain-controlled wheelchairs and vehicles.

Brain-Controlled Multi-Robot at Servo-Control Level Based on Nonlinear Model Predictive Control

Using a brain-computer interface (BCI) rather than limbs to control multiple robots (i.e., brain-controlled multi-robots) can better assist people with disabilities in daily life than a brain-controlled single robot. For example, one person with disabilities can move by a brain-controlled wheelchair (leader robot) and simultaneously transport objects by follower robots. In this paper, we explore how to control the direction, speed, and formation of a brain-controlled multi-robot system (consisting of leader and follower robots) for the first time and propose a novel multi-robot predictive control framework (MRPCF) that can track users' control intents and ensure the safety of multiple robots. The MRPCF consists of the leader controller, follower controller, and formation planner. We build a whole brain-controlled multi-robot physical system for the first time and test the proposed system through human-in-the-loop actual experiments. The experimental results indicate that the proposed system can track users' direction, speed, and formation control intents when guaranteeing multiple robots' safety. This paper can promote the study of brain-controlled robots and multi-robot systems and provide some novel views into human-machine collaboration and integration.

A novel brain-controlled wheelchair combined with computer vision and augmented reality

BackgroundBrain-controlled wheelchairs (BCWs) are important applications of brain–computer interfaces (BCIs). Currently, most BCWs are semiautomatic. When users want to reach a target of interest in their immediate environment, this semiautomatic interaction strategy is slow.MethodsTo this end, we combined computer vision (CV) and augmented reality (AR) with a BCW and proposed the CVAR-BCW: a BCW with a novel automatic interaction strategy. The proposed CVAR-BCW uses a translucent head-mounted display (HMD) as the user interface, uses CV to automatically detect environments, and shows the detected targets through AR technology. Once a user has chosen a target, the CVAR-BCW can automatically navigate to it. For a few scenarios, the semiautomatic strategy might be useful. We integrated a semiautomatic interaction framework into the CVAR-BCW. The user can switch between the automatic and semiautomatic strategies.ResultsWe recruited 20 non-disabled subjects for this study and used the accuracy, information transfer rate (ITR), and average time required for the CVAR-BCW to reach each designated target as performance metrics. The experimental results showed that our CVAR-BCW performed well in indoor environments: the average accuracies across all subjects were 83.6% (automatic) and 84.1% (semiautomatic), the average ITRs were 8.2 bits/min (automatic) and 8.3 bits/min (semiautomatic), the average times required to reach a target were 42.4 s (automatic) and 93.4 s (semiautomatic), and the average workloads and degrees of fatigue for the two strategies were both approximately 20.ConclusionsOur CVAR-BCW provides a user-centric interaction approach and a good framework for integrating more advanced artificial intelligence technologies, which may be useful in the field of disability assistance.

Clinical Validation of BCI-Controlled Wheelchairs in Subjects With Severe Spinal Cord Injury.

Brain-controlled wheelchairs are one of the most promising applications that can help people gain mobility after their normal interaction pathways have been compromised by neuromuscular diseases. The feasibility of using brain signals to control wheelchairs has been well demonstrated by healthy people in previous studies. However, most potential users of brain-controlled wheelchairs are people suffering from severe physical disabilities or who are in a "locked-in" state. To further validate the clinical practicability of our previously proposed P300-based brain-controlled wheelchair, in this study, 10 subjects with severe spinal cord injuries participated in three experiments and completed ten predefined tasks in each experiment. The average accuracy and information transfer rate (ITR) were 94.8% and 4.2 bits/min, respectively. Moreover, we evaluated the physiological and cognitive burdens experienced by these individuals before and after the experiments. There were no significant changes in vital signs during the experiment, indicating minimal physiological and cognitive burden. The patients' average systolic blood pressure before and after the experiment was 113±13.7 mmHg and 114±11.9 mmHg, respectively (P = 0.122). The patients' average heart rates before and after the experiment were 79±8.4/min and 79±8.2/min, respectively (P = 0.147). The average task load, measured by the National Aeronautics and Space Administration task load index, ranged from 10.0 to 25.5. The results suggest that the proposed P300-based brain-controlled wheelchair is safe and reliable; additionally, it does not significantly increase the patient's physical and mental task burden, demonstrating its potential value in clinical applications. Our study promotes the development of a more practical brain-controlled wheelchair system.

Brain-Computer Interfacing for Wheelchair Control by Detecting Voluntary Eye Blinks

The human brain is considered as one of the most powerful quantum computers and combining the human brain with technology can even outperform artificial intelligence. Using a Brain-Computer Interface (BCI) system, the brain signals can be analyzed and programmed for specific tasks. This research work employs BCI technology for a medical application that gives the unfortunate paralyzed individuals the capability to interact with their surroundings solely using voluntary eye blinks. This research contributes to the existing technology to be more feasible by introducing a modular design with three physically separated components: a headwear, a computer, and a wheelchair. As the signal-to-noise ratio (SNR) of the existing systems is too high to separate the eye blink artifacts from the regular EEG signal, a precise ThinkGear module is used which acquired the raw EEG signal through a single dry electrode. This chip offers an advanced filtering technology that has a high noise immunity along with an embedded Bluetooth module using which the acquired signal is transferred wirelessly to a computer. A MATLAB program captures voluntary eye blink artifacts from the brain waves and commands the movement of a miniature wheelchair via Bluetooth. To distinguish voluntary eye blinks from involuntary eye blinks, blink strength thresholds are determined. A Graphical User Interface (GUI) designed in MATLAB displays the EEG waves in real-time and enables the user to determine the movements of the wheelchair which is specially designed to take commands from the GUI. The findings from the testing phase unveil the advantages of a modular design and the efficacy of using eye blink artifacts as the control element for brain-controlled wheelchairs. The work presented here gives a basic understanding of the functionality of a BCI system, and provides eye blink-controlled navigation of a wheelchair for patients suffering from severe paralysis.

Brain-Controlled Wheelchair for Indoor Environments

Brain-Controlled Wheelchair for Indoor Environments

A Self-Paced BCI With a Collaborative Controller for Highly Reliable Wheelchair Driving: Experimental Tests With Physically Disabled Individuals

Brain-controlled wheelchairs (BCWs) are a promising solution for people with severe motor disabilities, who cannot use conventional interfaces. However, the low reliability of electroencephalographic signal decoding and the high user's workload imposed by continuous control of a wheelchair requires effective approaches. In this article, we propose a self-paced P300-based brain–computer interface (BCI) combined with dynamic time-window commands and a collaborative controller. The self-paced approach allows users to switch between control and noncontrol states without requiring any additional task or mental strategy, while the dynamic time-window commands allow balancing the reliability and speed of the BCI. The collaborative controller, combining user's intentions and navigation information, offers the possibility to navigate in complex environments and to improve the overall system reliability. The feasibility of the proposed approach and the impact of each system component (self-paced, dynamic time window, and collaborative controller) are systematically validated in a set of experiments conducted with seven able-bodied participants and six physically disabled participants steering a robotic wheelchair in real-office-like environments. These two groups controlled the BCW with a final driving accuracy greater than 99%. Quantitative and subjective results, assessed through questionnaires, attest to the effectiveness of the proposed approach. Altogether, these findings contribute to improving the usability of BCWs and, hence, the potential for their use by target users in home settings.

Brain-Controlled Wheelchair Review: From Wet Electrode to Dry Electrode, From Single Modal to Hybrid Modal, From Synchronous to Asynchronous

Brain-computer interface (BCI) is a novel human-computer interaction model, which does not depend on the conventional output pathway (peripheral nerve and muscle tissue). In the past three decades, it has attracted the interest of researchers and gradually become a research hotspot. As a typical BCI application, the brain-controlled wheelchair (BCW) could provide a new communicating channel with the external environment for physically disabled people. However, the main challenge of BCW is how to decode multi-degree of freedom control instruction from electroencephalogram (EEG) as soon as possible. The research progress of BCW has been developed rapidly over the past fifteen years. In this review, we investigate the BCW from multiple perspectives, include the type of signal acquisition, the pattern of commands for the control system and the working mechanism of the control system. Furthermore, we summarize the development trend of BCW based on the previous investigation, and it is mainly manifested in three aspects: from a wet electrode to dry electrode, from single-mode to multi-mode, and from synchronous control to asynchronous control. With the continuous development of BCW, we also find new functions have been introduced into BCW to increase its stability and robustness. It is believed that BCW will be able to enter the real-life from the laboratory and will be widely used in rehabilitation medicine in the future.

Research and Development of a Brain-Controlled Wheelchair for Paralyzed Patients

Smart wheelchairs play a significant role in supporting disabled people. Individuals with motor function impairments due to some disorders such as strokes or multiple sclerosis face frequent moving difficulties. Hence, they need constant support from an assistant. This paper presents a brain-controlled wheelchair model to assist disabled and paralyzed patients. The wheelchair is controlled by interpreting Electroencephalogram (EEG) signals, also known as brain waves. In the EEG technique, an electrode cap is positioned on the user’s scalp to receive EEG signals, which are detected and transformed by the Arduino microcontroller into motion commands, which drive the wheelchair. The proposed wheelchair is implemented using an Arduino-based robot controlled by a human brain wave using a Brain-Computer Interface (BCI). The human brain wave is captured using a low-cost Neurosky MindWave headset. The proposed wheelchair has the potential to have an immense effect on the healthcare industry. The use of this brain-controlled wheelchair can improve the quality of life of a paralyzed patient.

Indoor simulation training system for brain-controlled wheelchair based on steady-state visual evoked potentials

Brain-controlled wheelchair (BCW) is one of the important applications of brain-computer interface (BCI) technology. The present research shows that simulation control training is of great significance for the application of BCW. In order to improve the BCW control ability of users and promote the application of BCW under the condition of safety, this paper builds an indoor simulation training system based on the steady-state visual evoked potentials for BCW. The system includes visual stimulus paradigm design and implementation, electroencephalogram acquisition and processing, indoor simulation environment modeling, path planning, and simulation wheelchair control, etc. To test the performance of the system, a training experiment involving three kinds of indoor path-control tasks is designed and 10 subjects were recruited for the 5-day training experiment. By comparing the results before and after the training experiment, it was found that the average number of commands in Task 1, Task 2, and Task 3 decreased by 29.5%, 21.4%, and 25.4%, respectively ( P < 0.001). And the average number of commands used by the subjects to complete all tasks decreased by 25.4% ( P < 0.001). The experimental results show that the training of subjects through the indoor simulation training system built in this paper can improve their proficiency and efficiency of BCW control to a certain extent, which verifies the practicability of the system and provides an effective assistant method to promote the indoor application of BCW.

Indoor Simulated Training Environment for Brain-Controlled Wheelchair Based on Steady-State Visual Evoked Potentials.

Brain-controlled wheelchair (BCW) has the potential to improve the quality of life for people with motor disabilities. A lot of training is necessary for users to learn and improve BCW control ability and the performances of BCW control are crucial for patients in daily use. In consideration of safety and efficiency, an indoor simulated training environment is built up in this paper to improve the performance of BCW control. The indoor simulated environment mainly realizes BCW implementation, simulated training scenario setup, path planning and recommendation, simulated operation, and scoring. And the BCW is based on steady-state visual evoked potentials (SSVEP) and the filter bank canonical correlation analysis (FBCCA) is used to analyze the electroencephalography (EEG). Five tasks include individual accuracy, simple linear path, obstacles avoidance, comprehensive steering scenarios, and evaluation task are designed, 10 healthy subjects were recruited and carried out the 7-days training experiment to assess the performance of the training environment. Scoring and command-consuming are conducted to evaluate the improvement before and after training. The results indicate that the average accuracy is 93.55% and improves from 91.05% in the first stage to 96.05% in the second stage (p = 0.001). Meanwhile, the average score increases from 79.88 in the first session to 96.66 in the last session and tend to be stable (p < 0.001). The average number of commands and collisions to complete the tasks decreases significantly with or without the approximate shortest path (p < 0.001). These results show that the performance of subjects in BCW control achieves improvement and verify the feasibility and effectiveness of the proposed simulated training environment.

A Review of Error-Related Potential-Based Brain–Computer Interfaces for Motor Impaired People

Regaining the lost functionality of limbs is the top priority for people with motor skills impairment as it directly affects their ability to execute activities of daily living and hence, worsens their quality of life. In the last two decades, a great deal of research has focused on error-related potential (ErrP) based brain-computer interfaces (BCIs). Many applications have been developed to assist motor-impaired people in their rehabilitation and among these are robots, spellers, gesture recognition systems, and brain-controlled wheelchairs. In this paper, we present a review of various ErrP based BCI that can potentially aid motor-disabled people in their rehabilitation and execution of their daily activities. First, we describe the ErrP phenomenon and its characteristics followed by a comprehensive application-driven discussion on ErrP based rehabilitation and assistive strategies for motor-impaired people, including studies conducted since the inception of ErrP to the current state-of-the-art applications. Lastly, we discuss the potential issues and challenges being faced by current state-of-the-art applications as well as important future pathways and research directions that might be adopted for advanced ErrP-BCIs used in clinical settings.

Evaluation of Switch and Continuous Navigation Paradigms to Command a Brain-Controlled Wheelchair.

A brain-computer interface (BCI) is a technology allowing patients with severe motor dysfunctions to use their electroencephalographic signals to create a communication channel to control devices. The objective of this paper is to study the feasibility of continuous and switch control modes for a brain-controlled wheelchair (BCW) using sensorimotor rhythms (SMR) modulated through a right-hand motor imagery task. Previous studies, which used a continuous navigation control with SMR, have reported the difficulty of maintaining the motor imagery task for a long time, especially for the forward command. The switch control has been presented as a proposal that may help to solve this issue since this task is only used temporary for either disabling or enabling the movement. Regarding the methodology, 10 of 15 able-bodied users, who had overcome the criterion of 30% error rate in the calibration phase, controlled the BCW using both paradigms. The navigation tasks consisted of a straight path divided in five sections: in three of them the users had to move forward, and in the other two the users had to maintain their position. To assess user performance in the device management, a usability approach was adopted, measuring the factors of effectiveness, efficiency, and satisfaction. Then, variables related to the time employed and commands selected by the user or parameters related to the confusion matrix were applied. In addition, the scores in NASA-TLX and two ad hoc questionnaires were considered to discuss the user experience controlling the wheelchair. Despite the results showed that the best system for a specific user relies on his/her abilities and preferences, the switch control mode obtained better accuracy (0.59 ± 0.17 for continuous and 0.72 ± 0.05 for switch). Furthermore, the switch paradigm can be recommended for the advance sections as with it users could complete the advance sections in less time (42.2 ± 28.7 s for continuous and 15.47 ± 3.43 s for switch), while the continuous mode seems to be better at keeping the wheelchair stopped (42.45 ± 16.01 s for continuous and 24.35 ± 10.94 s for switch).

Multivariate empirical mode decomposition and multiscale entropy analysis of EEG signals from SSVEP-based BCI system

The steady-state visual evoked potential (SSVEP)-based Brain-Computer Interface (BCI) has been employed in the brain-controlled wheelchair system for patients with severe dyskinesia disease. However, a long-time operation brings about users fatigue, leading to a decrease of performance of the BCI system in practical applications. The characterization of the fatigued mechanism and the improvement of the SSVEP classification accuracy remains a challenging problem of significant importance. In this letter, we first conduct SSVEP experiments to acquire the EEG signals during both normal and fatigued states. Then we develop a novel framework, which integrates the advantages of multivariate empirical mode decomposition (MEMD) and Support Vector Machine (SVM), for improving the SSVEP classification accuracy, especially during the fatigued state. The results suggest that the novel framework enables us to obtain a higher SSVEP classification accuracy compared with the method without MEMD. Furthermore, in order to reveal the fatigued behavior, we use the multivariate multiscale sample entropy (MMSE) to analyze the multi-channel EEG signals corresponding to normal and fatigued states. We interestingly find that the MMSE values in the fatigued state are lower than that in the normal state, which reflects the increase of regularity in SSVEP signals during the fatigued state. That is, a greater synchronization of neural assemblies is required to realize cognitive impairment when fatigue happens. The knowledge for the understanding of the brain fatigued behavior underlying SSVEP-based BCI experiments is gained by our analysis.

Brain Controlled Wheel Chair

Brain Controlled Wheel Chair

Research on Brain Control Technology for Wheelchair

This paper proposes a portable brain-computer interface paradigm and builds a portable brain-computer interface device suitable for brain-controlled wheelchairs. According to the characteristics of E-motiv EPOC equipment and its collected EEG, the hardware system of brain-controlled wheelchair was developed, and a real-time detection system of brain-controlled technology was built, which is used to collect EEG signals in real time to realize the wheelchair's motion control. Experimental results show that the accuracy of EEG control can meet the engineering application requirements.