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.
In order to realize brain-computer interface (BCI), optimal features of single trail motor imagery electroencephalogram (EEG) were extracted and classified. Mu rhythm of EEG was obtained by preprocessing, and the features were optimized by spatial filtering, which are estimated from a set of data by method of common spatial pattern. Classification decision can be made by Fisher criterion, and classification performance can be evaluated by cross validation and receiver operating characteristic (ROC) curve. Optimal feature dimension determination projected by spatial filter was discussed deeply in cross-validation way. The experimental results show that the high discriminate accuracy can be guaranteed, meanwhile the program running speed is improved. Motor imagery intention classification based on optimized EEG feature provides difference of states and simplifies the recognition processing, which offers a new method for the research of intention recognition.
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.
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.
Motor imaging therapy is of great significance to the rehabilitation of patients with stroke or motor dysfunction, but there are few studies on lower limb motor imagination. When electrical stimulation is applied to the posterior tibial nerve of the ankle, the steady-state somatosensory evoked potentials (SSSEP) can be induced at the electrical stimulation frequency. In order to better realize the classification of lower extremity motor imagination, improve the classification effect, and enrich the instruction set of lower extremity motor imagination, this paper designs two experimental paradigms: Motor imaging (MI) paradigm and Hybrid paradigm. The Hybrid paradigm contains electrical stimulation assistance. Ten healthy college students were recruited to complete the unilateral movement imagination task of left and right foot in two paradigms. Through time-frequency analysis and classification accuracy analysis, it is found that compared with MI paradigm, Hybrid paradigm could get obvious SSSEP and ERD features. The average classification accuracy of subjects in the Hybrid paradigm was 78.61%, which was obviously higher than the MI paradigm. It proves that electrical stimulation has a positive role in promoting the classification training of lower limb motor imagination.
Neurological damage caused by stroke is one of the main causes of motor dysfunction in patients, which brings great spiritual and economic burdens for society and families. Motor imagery is an important assisting method for the rehabilitation of patients after stroke, which is easy to learn with low cost and has great significance in improving the motor function and the quality of patient's life. This paper mainly summarizes the positive effects of motor imagery on post-stroke rehabilitation, outlines the physiological performance and theoretical model of motor imagery, the influencing factors of motor imagery, the scoring criteria of motor imagery and analyzes the shortcomings such as the few kinds of experimental subject, the subjective evaluation method and the low resolution of the experimental equipment in the process of rehabilitation of motor function in post-stroke patients. It is hopeful that patients with stroke will be more scientifically and effectively using motor imagery therapy.
Motor imagery (MI), motion intention of the specific body without actual movements, has attracted wide attention in fields as neuroscience. Classification algorithms for motor imagery electroencephalogram (MI-EEG) signals are able to distinguish different MI tasks based on the physiological information contained by the EEG signals, especially the features extracted from them. In recent years, there have been some new advances in classification algorithms for MI-EEG signals in terms of classifiers versus machine learning strategies. In terms of classifiers, traditional machine learning classifiers have been improved by some researchers, deep learning and Riemannian geometry classifiers have been widely applied as well. In terms of machine learning strategies, ensemble learning, adaptive learning, and transfer learning strategies have been utilized to improve classification accuracies or reach other targets. This paper reviewed the progress of classification algorithms for MI-EEG signals, summarized and evaluated the existing classifiers and machine learning strategies, to provide new ideas for developing classification algorithms with higher performance.
Most of electroencephalogram (EEG) acquired by multi-channels is difficult to be applied to the single-channel brain-computer interface (BCI) in the EEG analysis method based on left and right hand motor imagery. The present research applied an improved independent component analysis (ICA) method to realize pretreatment of the EEG effectively. Firstly, data drift was removed through linear drift correction. Secondly, the number of virtual channels were increased by applying delayed window data and some EEG artifacts which are namely electrooculogram (EOG) and electrocardiogram (ECG) were removed by ICA. Finally, the average instantaneous energy characteristics were calculated and classified through the instantaneous amplitude which was solved by applying Hilbert-Huang transform (HHT). The experiment proves that the method completes the EEG pretreatment and improves classification ratio of single-channel EEG, and lays a foundation of single-channel and portable BCI.
As the most common active brain-computer interaction paradigm, motor imagery brain-computer interface (MI-BCI) suffers from the bottleneck problems of small instruction set and low accuracy, and its information transmission rate (ITR) and practical application are severely limited. In this study, we designed 6-class imagination actions, collected electroencephalogram (EEG) signals from 19 subjects, and studied the effect of collaborative brain-computer interface (cBCI) collaboration strategy on MI-BCI classification performance, the effects of changes in different group sizes and fusion strategies on group multi-classification performance are compared. The results showed that the most suitable group size was 4 people, and the best fusion strategy was decision fusion. In this condition, the classification accuracy of the group reached 77%, which was higher than that of the feature fusion strategy under the same group size (77.31% vs. 56.34%), and was significantly higher than that of the average single user (77.31% vs. 44.90%). The research in this paper proves that the cBCI collaboration strategy can effectively improve the MI-BCI classification performance, which lays the foundation for MI-cBCI research and its future application.
Event-related desynchronization (ERD) is the basic feature of electroencephalogram (EEG), and the brain-computer interface based on motor imagery (MI-BCI) with the foundation of the analysis of ERD is of great significance in motor function recovery. The valid ERD characteristics extracted from EEG are the key to the performance of the BCI, so the study of which kind of stimulation mode can prompt subjects to generate more obvious characteristics of ERD is crucial. Four different stimulation modes are designed in this paper, and the effects of motion imagery tasks under static text stimulation, grip video stimulation, serial motion video stimulation of fingers as well as serial motion video stimulation of fingers with sound on the characteristics of ERD are analyzed. Combining the analysis of time-frequency spectrum, the power spectral density curve, ERD value and brain topographic map, it is shown that the ERD under serial motion video stimulation of fingers and serial motion video stimulation of fingers with sound modes is much stronger and has wider range of activation, and the BCI based on the analysis of ERD will have a better effect on practical application. As a result, the recognition and acceptance of the users of BCI system are improved in some extent.