The traditional paradigm of motor-imagery-based brain-computer interface (BCI) is abstract, which cannot effectively guide users to modulate brain activity, thus limiting the activation degree of the sensorimotor cortex. It was found that the motor imagery task of Chinese characters writing was better accepted by users and helped guide them to modulate their sensorimotor rhythms. However, different Chinese characters have different writing complexity (number of strokes), and the effect of motor imagery tasks of Chinese characters with different writing complexity on the performance of motor-imagery-based BCI is still unclear. In this paper, a total of 12 healthy subjects were recruited for studying the effects of motor imagery tasks of Chinese characters with two different writing complexity (5 and 10 strokes) on the performance of motor-imagery-based BCI. The experimental results showed that, compared with Chinese characters with 5 strokes, motor imagery task of Chinese characters writing with 10 strokes obtained stronger sensorimotor rhythm and better recognition performance (P < 0.05). This study indicated that, appropriately increasing the complexity of the motor imagery task of Chinese characters writing can obtain stronger motor imagery potential and improve the recognition accuracy of motor-imagery-based BCI, which provides a reference for the design of the motor-imagery-based BCI paradigm in the future.
Aiming at feature selection problem of motor imagery task in brain computer interface (BCI), an algorithm based on mutual information and principal component analysis (PCA) for electroencephalogram (EEG) feature selection is presented. This algorithm introduces the category information, and uses the sum of mutual information matrices between features under different motor imagery category to replace the covariance matrix. The eigenvectors of the sum matrix represent the direction of the principal components and the eigenvalues of the sum matrix are used to determine the dimensionality of principal components. 2005 International BCI competition data set was used in our experiments, and four feature extraction methods were adopted, i. e. power spectrum estimation, continuous wavelet transform, wavelet packet decomposition and Hjorth parameters. The proposed feature selection algorithm was adopted to select and combine the most useful features for classification. The results showed that relative to the PCA algorithm, our algorithm had better performance in dimensionality reduction and in classification accuracy with the assistance of support vector machine classifier under the same dimensionality of principal components.
This paper aims to realize the decoding of single trial motor imagery electroencephalogram (EEG) signal by extracting and classifying the optimized features of EEG signal. In the classification and recognition of multi-channel EEG signals, there is often a lack of effective feature selection strategies in the selection of the data of each channel and the dimension of spatial filters. In view of this problem, a method combining sparse idea and greedy search (GS) was proposed to improve the feature extraction of common spatial pattern (CSP). The improved common spatial pattern could effectively overcome the problem of repeated selection of feature patterns in the feature vector space extracted by the traditional method, and make the extracted features have more obvious characteristic differences. Then the extracted features were classified by Fisher linear discriminant analysis (FLDA). The experimental results showed that the classification accuracy obtained by proposed method was 19% higher on average than that of traditional common spatial pattern. And high classification accuracy could be obtained by selecting feature set with small size. The research results obtained in the feature extraction of EEG signals lay the foundation for the realization of motor imagery EEG decoding.
Multi-modal brain-computer interface and multi-modal brain function imaging are developing trends for the present and future. Aiming at multi-modal brain-computer interface based on electroencephalogram-near infrared spectroscopy (EEG-NIRS) and in order to simultaneously acquire the brain activity of motor area, an acquisition helmet by NIRS combined with EEG was designed and verified by the experiment. According to the 10-20 system or 10-20 extended system, the diameter and spacing of NIRS probe and EEG electrode, NIRS probes were aligned with C3 and C4 as the reference electrodes, and NIRS probes were placed in the middle position between EEG electrodes to simultaneously measure variations of NIRS and the corresponding variation of EEG in the same functional brain area. The clamp holder and near infrared probe were coupled by tightening a screw. To verify the feasibility and effectiveness of the multi-modal EEG-NIRS helmet, NIRS and EEG signals were collected from six healthy subjects during six mental tasks involving the right hand clenching force and speed motor imagery. These signals may reflect brain activity related to hand clenching force and speed motor imagery in a certain extent. The experiment showed that the EEG-NIRS helmet designed in the paper was feasible and effective. It not only could provide support for the multi-modal motor imagery brain-computer interface based on EEG-NIRS, but also was expected to provide support for multi-modal brain functional imaging based on EEG-NIRS.
With the breakthroughs of digitization, artificial intelligence and other technologies and the gradual expansion of application fields, more and more studies have been conducted on the application of digital intelligence technologies such as exoskeleton robots, brain-computer interface, and spinal cord neuromodulation to improve or compensate physical function after spinal cord injury (SCI) and improve self-care ability and quality of life of patients with SCI. The development of digital intelligent rehabilitation technology provides a new application platform for the functional reconstruction after SCI, and the digital and intelligentized rehabilitation technology has broad application prospects in the clinical rehabilitation treatment after SCI. This article elaborates on the current status of exoskeleton robots, brain-computer interface technology, and spinal cord neuromodulation technology for functional recovery after SCI.
In the study of the scalp electroencephalogram (EEG)-based brain-computer interface (BCI), individual differences and complex background noise are two main factors which affect the stability of BCI system. For different subjects, therefore, optimization of BCI system parameters is necessary, including the optimal designing of temporal and spatial filters parameters as well as the classifier parameters. In order to improve the accuracy of BCI system, this paper proposes a new BCI information processing method, which combines the optimization design of independent component analysis spatial filter (ICA-SF) with the multiple sub-band features of EEG signals. The four subjects' three-class motor imagery EEG (MI-EEG) data collected in different periods were analyzed with the proposed method. Experimental results revealed that, during the inner and outer cross-validation of single subject as well as the subject-to-subject validation, the proposed multiple sub-band method always had higher average classification accuracy compared to those with single-band method, and the maximum difference could achieve 6.08% and 5.15%, respectively.
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.
With the development of brain-computer interface (BCI) technology and its translational application in clinical medicine, BCI medicine has emerged, ushering in profound changes to the practice of medicine, while also bringing forth a series of ethical issues related to BCI medicine. BCI medicine is progressively emerging as a new disciplinary focus, yet to date, there has been limited literature discussing it. Therefore, this paper focuses on BCI medicine, firstly providing an overview of the main potential medical applications of BCI technology. It then defines the discipline, outlines its objectives, methodologies, potential efficacy, and associated translational medical research. Additionally, it discusses the ethics associated with BCI medicine, and introduces the standardized operational procedures for BCI medical applications and the methods for evaluating the efficacy of BCI medical applications. Finally, it anticipates the challenges and future directions of BCI medicine. In the future, BCI medicine may become a new academic discipline or major in higher education. In summary, this article is hoped to provide thoughts and references for the development of the discipline of BCI medicine.
Brain–computer interface (BCI) technology enable humans to interact with external devices by decoding their brain signals. Despite it has made some significant breakthroughs in recent years, there are still many obstacles in its applications and extensions. The current used BCI control signals are generally derived from the brain areas involved in primary sensory- or motor-related processing. However, these signals only reflect a limited range of limb movement intention. Therefore, additional sources of brain signals for controlling BCI systems need to be explored. Brain signals derived from the cognitive brain areas are more intuitive and effective. These signals can be used for expand the brain signal sources as a new approach. This paper reviewed the research status of cognitive BCI based on the single brain area and multiple hybrid brain areas, and summarized its applications in the rehabilitation medicine. It’s believed that cognitive BCI technologies would become a possible breakthrough for future BCI rehabilitation applications.
The bidirectional closed-loop motor imagery brain-computer interface (MI-BCI) is an emerging method for active rehabilitation training of motor dysfunction, extensively tested in both laboratory and clinical settings. However, no standardized method for evaluating its rehabilitation efficacy has been established, and relevant literature remains limited. To facilitate the clinical translation of bidirectional closed-loop MI-BCI, this article first introduced its fundamental principles, reviewed the rehabilitation training cycle and methods for evaluating rehabilitation efficacy, and summarized approaches for evaluating system usability, user satisfaction and usage. Finally, the challenges associated with evaluating the rehabilitation efficacy of bidirectional closed-loop MI-BCI were discussed, aiming to promote its broader adoption and standardization in clinical practice.