Active Rehabilitation Training Research Articles

Interactive Virtual Ankle Movement Controlled by Wrist sEMG Improves Motor Imagery: An Exploratory Study.

Virtual reality (VR) techniques can significantly enhance motor imagery training by creating a strong illusion of action for central sensory stimulation. In this article, we establish a precedent by using surface electromyography (sEMG) of contralateral wrist movement to trigger virtual ankle movement through an improved data-driven approach with a continuous sEMG signal for fast and accurate intention recognition. Our developed VR interactive system can provide feedback training for stroke patients in the early stages, even if there is no active ankle movement. Our objectives are to evaluate: 1) the effects of VR immersion mode on body illusion, kinesthetic illusion, and motor imagery performance in stroke patients; 2) the effects of motivation and attention when utilizing wrist sEMG as a trigger signal for virtual ankle motion; 3) the acute effects on motor function in stroke patients. Through a series of well-designed experiments, we have found that, compared to the 2D condition, VR significantly increases the degree of kinesthetic illusion and body ownership of the patients, and improves their motor imagery performance and motor memory. When compared to conditions without feedback, using contralateral wrist sEMG signals as trigger signals for virtual ankle movement enhances patients' sustained attention and motivation during repetitive tasks. Furthermore, the combination of VR and feedback has an acute impact on motor function. Our exploratory study suggests that the sEMG-based immersive virtual interactive feedback provides an effective option for active rehabilitation training for severe hemiplegia patients in the early stages, with great potential for clinical application.

A new active rehabilitation training mode for upper limbs based on Tai Chi Pushing Hands

Robot-assisted rehabilitation is a crucial approach to restoring motor function in the limb. However, the current training trajectory lacks sufficient theoretical or practical support, and the monotony of single-mode training is a concern. Tai Chi Pushing Hands, a beneficial and effective daily exercise, has been shown to improve balance function, psychological state, and motor function of the upper extremities in patients recovering from stroke. To address these issues, we propose a new active rehabilitation training that incorporates Tai Chi Pushing Hands movements and yin-yang balance principles. The training trajectory and direction are encoded by the velocity field and consist of two processes: yang (push) and yin (return). During yang, the limb actively pushes the robot to move, while during yin, the limb actively follows the robot’s movement. To provide necessary assistance, an admittance controller with self-adaptive parameters is designed. In addition, we introduce two indexes, the ‘Intention Angle’ (ϖ) and the time ratio (Γ), to evaluate motion perception performance. Our experiment was conducted on a 4-degree-of-freedom upper limb rehabilitation robot platform, and the subjects were separated into a familiar group and an unfamiliar group. The experiment results show that the training could be completed well no matter whether the subject is familiar with Tai Chi Pushing Hands or not. The parameters and the movement of the robot can be adjusted based on the interactive force to adapt to the ability of the subject.

A novel zero-force control framework for post-stroke rehabilitation training based on fuzzy-PID method

As the number of people with neurological disorders increases, movement rehabilitation becomes progressively important, especially the active rehabilitation training, which has been demonstrated as a promising solution for improving the neural plasticity. In this paper, we developed a 5-degree-of-freedom rehabilitation robot and proposed a zero-force control framework for active rehabilitation training based on the kinematics and dynamics identification. According to the robot motion characteristics, the fuzzy PID algorithm was designed to further improve the flexibility of the robot. Experiments demonstrated that the proposed control method reduced the Root Mean Square Error and Mean Absolute Error evaluation indexes by more than 15% on average and improves the coefficient of determination ($$ R^{2} $$ ) by 4% compared with the traditional PID algorithm. In order to improve the active participation of the post-stroke rehabilitation training, this paper designed an active rehabilitation training scheme based on gamified scenarios, which further enhanced the efficiency of rehabilitation training by means of visual feedback.

Research and experiment on active training of lower limb based on five-bar mechanism of man-machine integration system

AbstractIn view of the fact that the current research on active and passive rehabilitation training of lower limbs is mainly based on the analysis of exoskeleton prototype and the lack of analysis of the actual movement law of limbs, the human-machine coupling dynamic characteristics for active rehabilitation training of lower limbs are studied. In this paper, the forward and inverse kinematics are solved on the basis of innovatively integrating the lower limb and rehabilitation prototype into a human-machine integration system and equivalent to a five-bar mechanism. According to the constraint relationship of hip joint, knee joint and ankle joint, the Lagrange dynamic equation and simulation model of five-bar mechanism under the constraint of human physiological joint motion are constructed, and the simulation problem of closed-loop five-bar mechanism is solved. The joint angle experimental system was built to carry out rehabilitation training experiments to analyze the relationship between lower limb error and height, weight and BMI, and then, a personalized training planning method suitable for people with different lower limb sizes was proposed. The reliability of the method is proved by experiments. Therefore, we can obtain the law of limb movement on the basis of traditional rehabilitation training, appropriately reduce the training speed or reduce the man-machine position distance and reduce the training speed or increase the man-machine distance to reduce the error to obtain the range of motion angle closer to the theory of hip joint and knee joint respectively, so as to achieve better rehabilitation.

Prediction of Actively Exerted Torque From Ankle Joint Complex Based on Muscle Synergy

Prediction of actively exerted torque from the ankle joint complex (AJC) is critical for human-robot interaction because this is the real torque that the patient exerts. In this work, we develop a prediction framework to predict the actively exerted torque from the surface electromyography (sEMG) signal. The modified non-negative matrix factorization (mNMF) algorithm is used to extract muscle activation information (MAI) and muscle synergy information (MSI); the pseudo-siamese network (PSN) is used to fit the MAI and MSI to actively exerted torque. The prediction accuracy is compared under different ankle attitudes, synergistic feature extraction algorithms, and prediction algorithms, and the results show that the prediction accuracy of the proposed framework is 94.15%, and the variance of prediction accuracy is just 3.56% under different ankle attitudes. The proposed framework can provide the real actively exerted torque for the active rehabilitation training of ankle joint, so as to improve the enthusiasm and initiative of patients during rehabilitation training.

Synergetic gait prediction and compliant control of SEA-driven knee exoskeleton for gait rehabilitation.

In recent years, lower limb exoskeletons have achieved satisfactory clinical curative effects in rehabilitating stroke patients. Furthermore, generating individualized trajectories for each patient and avoiding secondary injury in rehabilitation training are important issues. This paper explores the utilization of series elastic actuator (SEA) to deliver compliant force and enhance impact resistance in human-robot interaction, and we present the design of novel knee exoskeleton driven by SEA. Subsequently, the novel gait trajectory prediction method and compliant control method are proposed. The attention-based CNN-LSTM model is established to generate personalized gait trajectories for affected limbs, in which the spatial-temporal attention mechanism is adopted to improve the prediction accuracy. The compliant control strategy is proposed to nonlinearly and adaptively tune impedance parameters based on artificial potential field (APF) method, and active rehabilitation training is carried out in the coordination space to guarantee patient safety. The experimental results based on four healthy subjects demonstrated that synergetic gait prediction model could satisfactorily characterize the coordination movement with higher accuracy. The compliant control could limit the patient's movement in the safe coordination tunnel while considering personalization and flexibility.

Development and Validation of Virtual Reality Combined with Shoulder Wheel Device for Active Rehabilitation Training

Development and Validation of Virtual Reality Combined with Shoulder Wheel Device for Active Rehabilitation Training

The vulnerable superhero-a qualitative study investigating how spinal cord injury peer mentors experience their role in Active Rehabilitation training programs.

Qualitative study. To explore how peer mentors with spinal cord injury (SCI) experience their role in Active Rehabilitation programs in Sweden. Community peer-based training programs in Sweden. Twenty active peer mentors were strategically selected and invited to participate. Nine individuals of diverse age, level of SCI and time since injury participated in semi-structured interviews via video link. Qualitative content analysis was used to analyze the data. The theme: Being a vulnerable superhero-a balancing act with rewards was based upon five categories: Being a suitable fit for a demanding culture, Offering a shortcut to coping with spinal cord injury life, Feeling rewarded by helping others, Building trust is reciprocal, and Constituting the norm in a temporary community. The peer mentors expressed vulnerability of sharing life experiences, being a role model and building relationships with mentees. The theme emerged from notions of high expectations and demands of the SCI peer mentor role. Being a peer mentor empowered, educated and inspired the peer mentors themselves. Peer mentors were continuously balancing between being open and vulnerable, and being strong and capable. Personality traits were reported as more important aspects of the role than physical skills. The individual mentor-mentee meetings and relationships were considered more important than the actual training sessions. Feeling part of a community and the norm through meeting others in a similar situation was perceived as a reward and motivation for participating as a peer mentor.

A Brain-Myoelectric Signal-Based Approach to Hand Rehabilitation in Stroke

The existing hand function rehabilitation training model for stroke patients has problems such as single mode, low patient participation, poor rehabilitation effect and long rehabilitation period. In this paper, we propose an active stroke hand rehabilitation training method based on brain EMG signals, including the use of EEG signals to help stroke patients achieve brain neural remodelling and EMG signals to achieve real-time hand function rehabilitation training to assist patients to complete hand rehabilitation. Firstly, a multimodal guided motor imagery experimental paradigm with a mixture of pictures and Chinese characters was designed to improve the stability of the spontaneous motor imagery EEG signals. Then, a gesture acquisition paradigm based on surface EMG signals was designed to exercise the flexibility of stroke patients' arm muscles and fingers. The experimental results showed that the stroke hand rehabilitation training method based on brain-myoelectric signals could achieve better rehabilitation results.

A Safe Admittance Boundary Algorithm for Rehabilitation Robot Based on Space Classification Model

Existing studies on rehabilitation robots are generally devoted to robot-assisted active rehabilitation training, which is conducive to facilitating muscle and nerve regeneration. However, human–robot interaction (HRI) requires imposing a limit on the workspace within which the robot operates, so as to ensure patient safety. A safe admittance boundary algorithm for a rehabilitation robot is proposed based on the space classification model which works by constructing a virtual boundary for the HRI workspace in the control layer. First, point cloud isodensification is performed for the workspaces within which the human body and the robot operate. Next, the nearest neighbor density is determined for the point cloud, and the space classification model is built on this basis. Finally, the space classification model is integrated with admittance control to derive the safe admittance boundary algorithm, which can be used for safety control. This algorithm is then subjected to space verification experiments and out–of–bounds experiments using a dynamic arm simulator (DAS). As indicated by the experimental results, when the side length of the voxel grid for voxel center filtering is set to 0.06 m, the accuracy of space definition is 98.40%. The average maximum response time for out–of–bounds behaviors is 165.62 ms. The safe admittance boundary algorithm can impose reasonable virtual constraints on the robot workspace, thereby improving HRI safety.

Spring damping based control for a novel lower limb rehabilitation robot with active flexible training planning.

During neurological rehabilitation training for patients with lower limb dysfunction, active rehabilitation training based on interactive force recognition can effectively improve participation and efficiency in rehabilitation training. This study proposes an active training strategy for lower-limb rehabilitation robots based on a spring damping model. The active training strategy included a kinetic model of the human-machine system, calculated and verified using a pull-pressure force sensor We used a dynamic model of the human-machine system and tensile force sensors to identify the human-machine interaction forces exerted by the patient Finally, the spring damping model is used to convert the active interaction force into the offset angle of each joint, obtaining the active interaction force followed by the active movement of the lower limbsRESULTS:The experimental results showed that the rehabilitation robot could follow the active interaction force of the subject to provide assistance, thus generating the following movement and effectively helping patients improve joint mobility. The active flexibility training control strategy based on the virtual spring damping model proposed in this study is feasible, and motion is stable for patients with lower limb dysfunction after stroke Finally, the proposed active training method can be implemented in future work in other rehabilitation equipment and combined virtual reality technology to improve rehabilitation training experience and increase patient participation.

An active and passive upper limb rehabilitation training system based on a hybrid brain–computer interface

Movement function rehabilitation of patients with craniocerebral injuries is an important issue facing neurorehabilitation science. The use of brain–computer interface technology in rehabilitation training systems can allow patients to actively participate in the rehabilitation training process and use the brain’s neuroplasticity to enhance the effects from rehabilitation training. At present, the brain–computer interface-based rehabilitation training system still has problems such as insufficient active participation of patients, resulting in slowed motor neural circuit repair or low action execution accuracy. In response to the above problems, this paper designed an active and passive upper limb rehabilitation training system based on a hybrid brain–computer interface of steady-state visual evoked potentials (SSVEP) and movement-related cortical potentials (MRCPs). The system includes six parts: task setting and training guidance module, EEG signal acquisition module, EEG signal preprocessing and recognition module, rehabilitation training module, training completion evaluation module, and communication module. The system drives the rehabilitation robot to complete the training actions by identifying the participant’s SSVEP and evaluates the completion of the rehabilitation training based on the patient’s movement intention recognition results. In this study, 12 participants were recruited. In the online test, the system achieved an average action execution accuracy of 99.3%. The movement intention detection based on MRCPs reached an average accuracy of 82.7%. The participants’ average completion rate was 0.91. The experimental results show that the system can achieve a high rate of execution accuracy. In addition, it can evaluate the active participation level of patients in rehabilitation training based on the movement intention detection results, accelerate the reconstruction of motor neural circuits, improve the effects of training, and provide more effective ways of thinking for the study of upper limb rehabilitation training systems for patients with craniocerebral injuries.

Design and Performance Analysis of a Bioelectronic Controlled Hybrid Serial-Parallel Wrist Exoskeleton.

Wrist exoskeletons are increasingly being used in the rehabilitation of stroke and hand dysfunction because of its ability to assist patients in high intensity, repetitive, targeted and interactive rehabilitation training. However, the existing wrist exoskeletons cannot effectively replace the work of therapist and improve hand function, mainly because the existing exoskeletons cannot assist patients to perform natural hand movement covering the entire physiological motor space (PMS). Here, we present a bioelectronic controlled hybrid serial-parallel wrist exoskeleton HrWr-ExoSkeleton (HrWE) which is based on the PMS design guidance, the gear set can carry out forearm pronation/supination (P/S) and the 2-DoF parallel configuration fixed on the gear set can carry out wrist flexion/extension (F/E) and radial/ulnar deviation (R/U). This special configuration not only provides enough range of motion (RoM) for rehabilitation training (85F/85E, 55R/55U, and 90P/90S), but also makes it easier to provide the interface for finger exoskeletons and be adapted to upper limb exoskeletons. In addition, to further improve the rehabilitation effect, we propose a HrWE-assisted active rehabilitation training platform based on surface electromyography signals.

A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals.

The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilitation training is a hot research topic in human-computer interaction control. Therefore, improving the accuracy of active motion intention recognition is the premise of the human-machine interaction controller design. Furthermore, there are external disturbances (bounded/unbounded disturbances) during rehabilitation training, which seriously threaten the safety of subjects. Thereby, eliminating external disturbances (especially unbounded disturbances) is the difficulty and key to the human-machine interaction control of the upper limb rehabilitation robots. In response to these problems, based on the surface electromyogram signal of the human upper limb, this paper proposes a fuzzy neural network active motion intention recognition method to explore the internal connection between the surface electromyogram signal of the human upper limb and active motion intention, and improve the real-time and accuracy of recognition. Based on this, two types of human-machine interaction controllers, which can be called as zeroing neural network controller and noise-suppressing zeroing neural network controller are designed to establish a safe and comfortable training environment to avoid secondary damage to the affected limb. Numerical experiments verify the feasibility and effectiveness of the proposed theories and methods.

Design and kinematical performance analysis of the 7-DOF upper-limb exoskeleton toward improving human-robot interface in active and passive movement training

Upper-limb rehabilitation robots have become an important piece of equipment in stroke rehabilitation. The design of exoskeleton mechanisms plays a key role to improve human-robot interface in the upper-limb movements under passive and active rehabilitation training. This paper proposes a novel of the 7-DOF (RR-RR-PRR) under-actuated exoskeleton mechanism based on the characteristics of the upper-limb movements in both of active and passive training. This aim of the proposed work is to improve human-robot interface in rehabilitation training with robots. Firstly, the characteristics of active and passive movement training are analyzed depending on the human upper-limb model. Then, a novel 7-DOF (RR-RR-PRR) exoskeleton mechanism is proposed based on the analyzed characteristics. After that, kinematical performances of the proposed exoskeleton are analyzed on the workspace, manipulability and manipulability ellipsoid by compared with the common exoskeleton configuration of the 7 DOFs (RRR-R-PRR) mechanism. In the end, the prototype is manufactured and tested by undergoing the experiments of single-joint passive movement training and multi-joint active movement training. The human-robot interface of the proposed exoskeleton is demonstrated by root mean square error, Pearson correlation coefficient, and the time-delay difference. The results of the kinematical performance show that the effective workspace and the flexibility of the exoskeleton with the proposed configuration are increased by 10.44% and 1.7%. In the single-joint passive movement training experiment, the root mean square errors are 6.986, 7.568, 5.846, and Pearson correlation coefficients are 0.989, 0.984, 0.988 at the shoulder joint and the elbow joint, respectively. The time-delay differences are not beyond 3.1%. In the multi-joint active movement training experiment, the root mean square errors are 9.312 and 7.677, and Pearson correlation coefficients are 0.906 and 0.968 at the shoulder joint and the elbow joint, respectively. The time-delay differences are not beyond 3.28%. The proposed 7 DOFs exoskeleton mechanism shows uniformity with that of the common exoskeleton on the same rehabilitation trajectory which is effective to improve human-robot interface under passive and active rehabilitation training.

A Training Method for a Sensor-Based Exercise Rehabilitation Robot

In order to solve the problem that the traditional mirror therapy did not take into account the real-time recovery of the affected limb and the training effect was limited, a training method of sports rehabilitation robot based on sensor was proposed. A mirror active rehabilitation training system was proposed, which was composed of four steps including trajectory acquisition of the limb inertial measurement unit (IMU), fuzzy adaptive proportion differentiation (PD) control in closed-loop variable domain, muscle force estimation of the surface electromyographic signal (sEMG) of the affected limb, and power compensation of the outer ring of the affected limb. The experimental results showed that the sagittal forward flexion angle of the healthy shoulder increased from 0° to 128° at a relatively uniform speed, and the sagittal forward flexion angle of the shoulder was basically consistent with that of the healthy limb after the adaptive power compensation of the affected limb. The calculated trajectory tracking error of the healthy limb controlled by the fuzzy adaptive PD controller in the variable domain was 0.21 ± 1.35 ° . The horizontal backward extension angle of the healthy shoulder joint increased from 0° to 43°, and the following trajectory of the affected limb was roughly consistent with the movement trajectory of the healthy limb. The calculated tracking error of the healthy limb trajectory was 0.39 ± 1.45 ° . It was concluded that the control system could provide the real-time power compensation according to the recovery of the affected limb, give full play to the training initiative of the affected limb, and make the affected limb achieve a better rehabilitation training effect.

RBF Sliding Mode Control Method for an Upper Limb Rehabilitation Exoskeleton Based on Intent Recognition

Aiming at the lack of active willingness of patients to participate in the current upper limb exoskeleton rehabilitation training control methods, this study proposed a radial basis function (RBF) sliding mode impedance control method based on surface electromyography (sEMG) to identify the movement intention of upper limb rehabilitation. The proposed control method realizes the process of active and passive rehabilitation training according to the wearer’s movement intention. This study first established a joint angle prediction model based on sEMG for the problem of poor human–machine coupling and used the least-squares support vector machine method (LSSVM) to complete the upper limb joint angle prediction. In addition, in view of the problem of poor compliance in the rehabilitation training process, an adaptive sliding mode controller based on the RBF network approximation system model was proposed. In the process of active training, an impedance model was added based on the position loop control, which could dynamically adjust the motion trajectory according to the interaction force. The experiment results showed that the impedance control method based on the RBF could effectively reduce the interaction force between the human and machine to improve the compliance of the exoskeleton manipulator and achieve the purpose of stabilizing the impedance characteristics of the system.

Current State of Robotics in Hand Rehabilitation after Stroke: A Systematic Review

Among the methods of hand function rehabilitation after stroke, robot-assisted rehabilitation is widely used, and the use of hand rehabilitation robots can provide functional training of the hand or assist the paralyzed hand with activities of daily living. However, patients with hand disorders consistently report that the needs of some users are not being met. The purpose of this review is to understand the reasons why these user needs are not being adequately addressed, to explore research on hand rehabilitation robots, to review their current state of research in recent years, and to summarize future trends in the hope that it will be useful to researchers in this research area. This review summarizes the techniques in this paper in a systematic way. We first provide a comprehensive review of research institutions, commercial products, and literature. Thus, the state of the art and deficiencies of functional hand rehabilitation robots are sought and guide the development of subsequent hand rehabilitation robots. This review focuses specifically on the actuation and control of hand functional rehabilitation robots, as user needs are primarily focused on actuation and control strategies. We also review hand detection technologies and compare them with patient needs. The results show that the trends in recent years are more inclined to pursue new lightweight materials to improve hand adaptability, investigating intelligent control methods for human-robot interaction in hand functional rehabilitation robots to improve control robustness and accuracy, and VR virtual task positioning to improve the effectiveness of active rehabilitation training.

Exercise and Rehabilitation of Chronically Ill Patients Assisted by Small Digital Bio-Pulse Sensors

With the continuous increase of the ageing population, the number of patients with chronic diseases has increased dramatically. The limited medical resources and the strong demand for high-quality medical services are in stark contradiction. Active rehabilitation training is one of the most effective rehabilitation methods, but it is difficult to achieve through traditional medical equipment. In this paper, aiming at effective clinical rehabilitation, an in-depth study was carried out on the exercise intention recognition experiment of patients, hoping to provide an effective rehabilitation treatment method for the recovery of patients with lower extremity motor function injury. This paper introduces the detection principle of the photoelectric pulse sensor and the design scheme that is used in physical education. In the experiment, through the hardware connection, programming, and the development of the host computer software, the pulse signal of the human body can be presented stably on the computer. The monitoring of the pulse of the students can help the physical education teacher to understand the situation of the students and prevent accidents. In addition, we believe that the acquisition of students’ pulse signals in physical education to build a database is of great significance for research and tracking of students’ health status and the research results will certainly promote the development of pulse diagnosis. In order to make the lower extremity exoskeleton rehabilitation system that can be applied in clinical rehabilitation, according to the Brunnstrom staging of patients with central nervous system injury, the needs of patients in different stages in the rehabilitation process were analysed and active and passive lower extremity rehabilitation strategies were formulated. Aiming at the problem of real-time and accurate identification of human motion intention, and to alleviate the mechanism motion delay caused by the delay of mechanical and control systems in the human-machine integrated system, a differential sEMG real-time feature extraction algorithm is proposed. The results show that the sensor and monitoring system have excellent stability, and the auxiliary system can accurately reflect the changing trend of the human biological pulse, achieve the expected effect, and effectively assist in the monitoring of exercise data for patients with chronic diseases. After treatment, the joint range of motion and muscle strength basically returned to normal levels, and the patient was able to walk independently. Compared with traditional treatment methods, the recovery time is shorter, the recovery of muscle strength is more effective, and the medical staff is more relaxed.

LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI.

Brain-computer interface (BCI) systems based on functional near-infrared spectroscopy (fNIRS) have been used as a way of facilitating communication between the brain and peripheral devices. The BCI provides an option to improve the walking pattern of people with poor walking dysfunction, by applying a rehabilitation process. A state-of-the-art step-wise BCI system includes data acquisition, pre-processing, channel selection, feature extraction, and classification. In fNIRS-based BCI (fNIRS-BCI), channel selection plays a vital role in enhancing the classification accuracy of the BCI problem. In this study, the concentration of blood oxygenation (HbO) in a resting state and in a walking state was used to decode the walking activity and the resting state of the subject, using channel selection by Least Absolute Shrinkage and Selection Operator (LASSO) homotopy-based sparse representation classification. The fNIRS signals of nine subjects were collected from the left hemisphere of the primary motor cortex. The subjects performed the task of walking on a treadmill for 10 s, followed by a 20 s rest. Appropriate filters were applied to the collected signals to remove motion artifacts and physiological noises. LASSO homotopy-based sparse representation was used to select the most significant channels, and then classification was performed to identify walking and resting states. For comparison, the statistical spatial features of mean, peak, variance, and skewness, and their combination, were used for classification. The classification results after channel selection were then compared with the classification based on the extracted features. The classifiers used for both methods were linear discrimination analysis (LDA), support vector machine (SVM), and logistic regression (LR). The study found that LASSO homotopy-based sparse representation classification successfully discriminated between the walking and resting states, with a better average classification accuracy (p < 0.016) of 91.32%. This research provides a step forward in improving the classification accuracy of fNIRS-BCI systems. The proposed methodology may also be used for rehabilitation purposes, such as controlling wheelchairs and prostheses, as well as an active rehabilitation training technique for patients with motor dysfunction.