A Multimodal Framework Based on Integration of Cortical and Muscular Activities for Decoding Human Intentions About Lower Limb Motions

Brain-Computer Interface for Patients with Spinal Cord Injury: A Bibliometric Study

BACKGROUND

Spinal cord injury (SCI) is a debilitating condition with profound implications on patients' quality of life. Recent advancements in brain-computer interface (BCI) technology have provided novel opportunities for individuals with paralysis due to SCI. Consequently, research on the application of BCI for treating SCI has received increasing attention from scholars worldwide. However, there is a lack of rigorous bibliometric studies on the evolution and trends in this field. Hence, the present study aimed to use bibliometric methods to investigate the current status and emerging trends in the field of applying BCI for treating SCI and thus identify novel therapeutic options for SCI.

METHODS

We conducted a comprehensive review of the relevant literature on BCI applications for treating SCI published between 2005 and 2024 by using the Web of Science Core Collection database. To facilitate visualization and quantitative analysis of the published literature, we used VOSviewer and CiteSpace software tools. These tools enabled the assessment of co-authorships, co-occurrences, citations, and co-citations in the selected literature, thereby providing an overview of the current trends and predictive insights into the field.

RESULTS

The literature search yielded 714 publications from the Web of Science Core Collection database. The findings indicated a significant upward trend in the number of publications, yielding a total of 24,804 citations, with an average citation rate of 34.74 per publication and an H-index of 75. Research contributions were identified from 54 countries/regions, and the United States, China, and Germany emerged as the predominant contributors. A total of 1114 research institutions contributed to the retrieved literature, with Harvard Medical School, Brown University, and Northwestern University producing the highest number of publications. The published literature was predominantly distributed across 258 academic journals, and the Journal of Neural Engineering was the most frequently utilized publication source. Hochberg, Leigh, Henderson, Jaimie, and Collinger were the prominent authors in this field.

CONCLUSIONS

In recent years, there has been a steep increase in research on the use of BCI for treating SCI. Existing research focuses on the application of BCI for improving rehabilitation and quality of life of patients with SCI. Interdisciplinary collaboration is the current trend in this field.

A multimodal framework based on deep belief network for human locomotion intent prediction

Accurate prediction of human locomotion intent benefits the seamless switching of lower limb exoskeleton controllers in different terrains to assist humans in walking safely. In this paper, a deep belief network (DBN) was developed to construct a multimodal framework for recognizing various locomotion modes and predicting transition tasks. Three fusion strategies (data level, feature level, and decision level) were explored, and optimal network performance was obtained. This method could be tested on public datasets. For the continuous performance of steady state, the best prediction accuracy achieved was 97.64% in user-dependent testing and 96.80% in user-independent testing. During the transition state, the system accurately predicted all transitions (user-dependent: 96.37%, user-independent: 95.01%). The multimodal framework based on DBN can accurately predict the human locomotion intent. The experimental results demonstrate the potential of the proposed model in the volition control of the lower limb exoskeleton.

Improving performance of human action intent recognition: Analysis of gait recognition machine learning algorithms and optimal combination with inertial measurement units

Human action intent recognition has become increasingly dependent on computational accuracy, real-time responsiveness, and model lightness. Model selection, data filtering, and experimental design are three critical factors for the recognition of human intention in research. However, the performance of machine learning algorithms can vary depending on factors such as sensor location, the number of sensors used, channel selection, and dimensional combinations. Moreover, the collection of adequate and balanced data in such scenarios can be challenging. To address this issue, we present a comparative analysis of 12 commonly used machine learning algorithms for human action intention recognition. The synthetic minority oversampling technique is applied to fill in missing data. Traversing all possible combinations would require conducting 686 experiments, which is a daunting task in terms of both cost and efficiency. To tackle this challenge, we employ an orthogonal experiment design based on the Quasi-horizontal method. Our analysis indicates that lightGBM outperforms other algorithms in recognizing eight human daily activities. Furthermore, we conduct a polar difference and variance analysis based on a comprehensive balanced multi-metric orthogonal experiment for lightGBM using various sensor combinations and dimensions. The optimal combinations of different sensor numbers in terms of position, channel, and dimension are derived using this approach. Notably, our experimental design reduces the number of experiments required to only 49 times.

EEG-EMG coupling as a hybrid method for steering detection in car driving settings

Understanding mental processes in complex human behavior is a key issue in driving, representing a milestone for developing user-centered assistive driving devices. Here, we propose a hybrid method based on electroencephalographic (EEG) and electromyographic (EMG) signatures to distinguish left and right steering in driving scenarios. Twenty-four participants took part in the experiment consisting of recordings of 128-channel EEG and EMG activity from deltoids and forearm extensors in non-ecological and ecological steering tasks. Specifically, we identified the EEG mu rhythm modulation correlates with motor preparation of self-paced steering actions in the non-ecological task, while the concurrent EMG activity of the left (right) deltoids correlates with right (left) steering. Consequently, we exploited the mu rhythm de-synchronization resulting from the non-ecological task to detect the steering side using cross-correlation analysis with the ecological EMG signals. Results returned significant cross-correlation values showing the coupling between the non-ecological EEG feature and the muscular activity collected in ecological driving conditions. Moreover, such cross-correlation patterns discriminate the steering side earlier relative to the single EMG signal. This hybrid system overcomes the limitation of the EEG signals collected in ecological settings such as low reliability, accuracy, and adaptability, thus adding to the EMG the characteristic predictive power of the cerebral data. These results prove how it is possible to complement different physiological signals to control the level of assistance needed by the driver.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-021-09776-w.

A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding

Humans learn about the environment by interacting with it. With an increasing use of computer and virtual applications as well as robotic and prosthetic devices, there is a need for intuitive interfaces that allow the user to have an embodied interaction with the devices they are controlling. Muscle-machine interfaces can provide an intuitive solution by decoding human intentions utilizing myoelectric activations. There are several different methods that can be utilized to develop MuMIs, such as electromyography, ultrasonography, mechanomyography, and near-infrared spectroscopy. In this paper, we analyze the advantages and disadvantages of different myography methods by reviewing myography fusion methods. In a systematic review following the PRISMA guidelines, we identify and analyze studies that employ the fusion of different sensors and myography techniques, while also considering interface wearability. We also explore the properties of different fusion techniques in decoding user intentions. The fusion of electromyography, ultrasonography, mechanomyography, and near-infrared spectroscopy as well as other sensing such as inertial measurement units and optical sensing methods has been of continuous interest over the last decade with the main focus decoding the user intention for the upper limb. From the systematic review, it can be concluded that the fusion of two or more myography methods leads to a better performance for the decoding of a user's intention. Furthermore, promising sensor fusion techniques for different applications were also identified based on the existing literature.

Hardware Circuits Design and Performance Evaluation of a Soft Lower Limb Exoskeleton

Soft lower limb exoskeletons (LLEs) are wearable devices that have good potential in walking rehabilitation and augmentation. While a few studies focused on the structure design and assistance force optimization of the soft LLEs, rarely work has been conducted on the hardware circuits design. The main purpose of this work is to present a new soft LLE for walking efficiency improvement and introduce its hardware circuits design. A soft LLE for hip flexion assistance and a hardware circuits system with scalability were proposed. To assess the efficacy of the soft LLE, the experimental tests that evaluate the sensor data acquisition, force tracking performance, lower limb muscle activity and metabolic cost were conducted. The time error in the peak assistance force was just 1%. The reduction in the normalized root-mean-square EMG of the rectus femoris was 7.1%. The net metabolic cost in exoskeleton on condition was reduced by 7.8% relative to walking with no exoskeleton. The results show that the designed hardware circuits can be applied to the soft LLE and the soft LLE is able to improve walking efficiency of wearers.

Action Recognition of Lower Limbs Based on Surface Electromyography Weighted Feature Method

To improve the recognition rate of lower limb actions based on surface electromyography (sEMG), an effective weighted feature method is proposed, and an improved genetic algorithm support vector machine (IGA-SVM) is designed in this paper. First, for the problem of high feature redundancy and low discrimination in the surface electromyography feature extraction process, the weighted feature method is proposed based on the correlation between muscles and actions. Second, to solve the problem of the genetic algorithm selection operator easily falling into a local optimum solution, the improved genetic algorithm-support vector machine is designed by championship with sorting method. Finally, the proposed method is used to recognize six types of lower limb actions designed, and the average recognition rate reaches 94.75%. Experimental results indicate that the proposed method has definite potentiality in lower limb action recognition.

Prediction of Myoelectric Biomarkers in Post-Stroke Gait

Electromyography (EMG) is sensitive to neuromuscular changes resulting from ischemic stroke and is considered a potential predictive tool of post-stroke gait and rehabilitation management. This study aimed to evaluate the potential myoelectric biomarkers for the classification of stroke-impaired muscular activity of the stroke patient group and the muscular activity of the control healthy adult group. We also proposed an EMG-based gait monitoring system consisting of a portable EMG device, cloud-based data processing, data analytics, and a health advisor service. This system was investigated with 48 stroke patients (mean age 70.6 years, 65% male) admitted into the emergency unit of a hospital and 75 healthy elderly volunteers (mean age 76.3 years, 32% male). EMG was recorded during walking using the portable device at two muscle positions: the bicep femoris muscle and the lateral gastrocnemius muscle of both lower limbs. The statistical result showed that the mean power frequency (MNF), median power frequency (MDF), peak power frequency (PKF), and mean power (MNP) of the stroke group differed significantly from those of the healthy control group. In the machine learning analysis, the neural network model showed the highest classification performance (precision: 88%, specificity: 89%, accuracy: 80%) using the training dataset and highest classification performance (precision: 72%, specificity: 74%, accuracy: 65%) using the testing dataset. This study will be helpful to understand stroke-impaired gait changes and decide post-stroke rehabilitation.

Decoding Single-Hand and Both-Hand Movement Directions From Noninvasive Neural Signals

Decoding human movement parameters from electroencephalograms (EEG) signals is of great value for human-machine collaboration. However, existing studies on hand movement direction decoding concentrate on the decoding of a single-hand movement direction from EEG signals given the opposite hand is maintained still. In practice, the cooperative movement of both hands is common. In this paper, we investigated the neural signatures and decoding of single-hand and both-hand movement directions from EEG signals. The potentials of EEG signals and power sums in the low frequency band of EEG signals from 24 channels were used as decoding features. The linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used for decoding. Experimental results showed a significant difference in the negative offset maximums of movement-related cortical potentials (MRCPs) at electrode Cz between single-hand and both-hand movements. The recognition accuracies for six-class classification, including two single-hand and four both-hand movement directions, reached 70.29%± 10.85% by using EEG potentials as features with the SVM classifier. These findings showed the feasibility of decoding single-hand and both-hand movement directions. This work can lay a foundation for the future development of an active human-machine collaboration system based on EEG signals and open a new research direction in the field of decoding hand movement parameters from EEG signals.

Accurate recognition of lower limb ambulation mode based on surface electromyography and motion data using machine learning

Background and Objective The lower limb activity of recognition of the elderly, the weak, the disabled and the sick is an irreplaceable role in the caring of daily life. The main purpose of this study is to assess the feasibility of using the surface electromyography (sEMG) signal and inertial measurement units (IMUs) data to determine the optimal fusion features and classifier for the study of daily ambulation mode recognition. Methods We have carried out several steps of experiments to obtain and test the optimal combination of the sEMG data and the body motion data at the feature level and the most suitable machine learning classification algorithm. Firstly, the sEMG and IMUs signals of eighteen participants performing four different ambulatory activities have recorded using wearable sensors. Secondly, several features of the sEMG sensors and IMU data were extracted and tested by the Markov Random Field based Fisher-Markov feature selector. Finally, four ML classifiers with several feature combinations were estimated with sensitivity, precision and recognition accurate rate of ambulatory activity classification. Results The results of this work showed that all selected features were significantly statistical difference in four ambulation modes. The principal component analysis was used to reduce the dimension of selected sEMG features and IMU features to form a fusion feature input support vector machine classifier, which could predict ambulatory activities with good classification performance. Conclusions It is concluded that the results demonstrate the feasibility of the ML classification model, which could provide a more novel way to guarantee the recognition rate and effectiveness of monitor daily ambulatory activity.

Memristive Circuit Design of Emotional Generation and Evolution Based on Skin-Like Sensory Processor

Sensory processor in human skin is used for processing and transmitting sensations to the brain, which leads to body actions and emotional responses. In this paper, a memristive circuit of emotional generation and evolution based on skin-like sensory processor is proposed. The circuit includes: first, memristive skin-like sensory processor module; second, emotional generation and evolution module; and third, emotional expression module. The first module consists of four single-memristor skin-like sensory processors, which correspond to process sensations of pain, cold, warm, and tactile. It will automatically return to its initial state if sensory signals disappear. But if sensory signals are much strong, it will not automatically return to initial state unless applied "restoring signal" just like a surgical operation. The second module realizes a conversion mechanism from sensations to emotions using memristor as emotional synapse. Given signals from skin-like sensory processor, the memristance will decrease, which means the extent of emotion will increase, such as more happy. This is the emotional generation. The extent of emotion will be changed if the same sensation is applied to skin-like sensory processor repeatedly, which is the emotional evolution. The third module can show the generated emotions visually. The simulation results in PSPICE show that the proposed circuit can generate and evolve emotions like human beings after processing sensory signals from skin. The proposed circuit can be applied in a perceptual robot platform to realize the conversion from sensations to emotions, enabling the robot to have the ability to sense and process information.

Anxiety detection and training task adaptation in robot-assisted active stroke rehabilitation

In the therapist-centered rehabilitation program, the experienced therapists can observe emotional changes of stroke patients and make corresponding decisions on their intervention strategies. Likewise, robotic-assisted stroke rehabilitation systems will be more appreciated if they can also perceive emotional states of the stroke patients and enhance their engagements by exploring emotion-based dynamic difficulty adjustments. Nevertheless, few research have addressed this issue. A two-phase pilot study with anxiety as the target emotion state was conducted in this article. In phase I, the motor performances and the physiological responses to the stroke subject’s anxiety with high, medium, and low intensities were statistically analyzed, and anxiety models with three intensities were offline developed using support vector machine–based classifiers. In phase II, anxiety-based closed-loop robot-aided training task adaptation and its impacts on patient–robot interaction engagements were explored. As a comparison, a performance-based robotic behavior adaptation was also implemented. Experimental results with 12 recruited stroke patients conducted on the Barrett WAMTM manipulator verified that the rehabilitation robot can implicitly recognize the anxiety intensities of the stroke survivors and the anxiety-based real-time robotic behavior adaptation shows more engagements in the human–robot interactions.