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.
Motor imagery (MI) is an important paradigm of driving brain computer interface (BCI). However, MI is not easy to control or acquire, and the performance of MI-BCI depends heavily on the performance of the subjects’ MI. Therefore, the correct execution of MI mental activities, ability evaluation and improvement methods play important and even critical roles in the improvement and application of MI-BCI system’s performance. However, in the research and development of MI-BCI, the existing researches mainly focus on the decoding algorithm of MI, but do not pay enough attention to the above three aspects of MI mental activities. In this paper, these problems of MI-BCI are discussed in detail, and it is pointed out that the subjects tend to use visual motor imagery as kinesthetic motor imagery. In the future, we need to develop some objective, quantitatively visualized MI ability evaluation methods, and develop some effective and less time-consumption training methods to improve MI ability. It is also necessary to solve the differences and commonness of MI problems between and within individuals and MI-BCI illiteracy to a certain extent.
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.
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.
To improve the performance of brain-controlled intelligent car based on motor imagery (MI), a method based on neurofeedback (NF) with electroencephalogram (EEG) for controlling intelligent car is proposed. A mental strategy of MI in which the energy column diagram of EEG features related to the mental activity is presented to subjects with visual feedback in real time to train them to quickly master the skills of MI and regulate their EEG activity, and combination of multi-features fusion of MI and multi-classifiers decision were used to control the intelligent car online. The average, maximum and minimum accuracy of identifying instructions achieved by the trained group (trained by the designed feedback system before the experiment) were 85.71%, 90.47% and 76.19%, respectively and the corresponding accuracy achieved by the control group (untrained) were 73.32%, 80.95% and 66.67%, respectively. For the trained group, the average, longest and shortest time consuming were 92 s, 101 s, and 85 s, respectively, while for the control group the corresponding time were 115.7 s, 120 s, and 110 s, respectively. According to the results described above, it is expected that this study may provide a new idea for the follow-up development of brain-controlled intelligent robot by the neurofeedback with EEG related to MI.
In the research of non-invasive brain-computer interface (BCI), independent component analysis (ICA) has been considered as a promising method of electroencephalogram (EEG) preprocessing and feature enhancement. However, there have been few investigations and implements about online ICA-BCI system up till now. This paper reports the investigation of the ICA-based motor imagery BCI (MIBCI) system, combining the characteristics of unsupervised learning of ICA and event-related desynchronization (ERD) related to motor imagery. We constructed a simple and practical method of ICA spatial filter calculation and discriminate criterion of three-type motor imageries in the study. To validate the online performance of proposed algorithms, an ICA-MIBCI experimental system was fully established based on NeuroScan EEG amplifier and VC++ platform. Four subjects participated in the experiment of MIBCI testing and two of them took part in the online experiment. The average classification accuracies of the three-type motor imageries reached 89.78% and 89.89% in the offline and online testing, respectively. The experimental results showed that the proposed algorithm produced high classification accuracy and required less time consumption, which would have a prospect of cross platform application.
Mental rotation cognitive tasks based on motor imagery (MI) have excellent predictability for individual’s motor imagery ability. In order to explore the relationship between motor imagery and behavioral data, in this study, we asked 10 right-handed male subjects to participate in the experiments of mental rotation tasks based on corresponding body parts pictures, and we therefore obtained the behavioral effects according to their reaction time (RT) and accuracy (ACC). Later on, we performed Pearson correlation analysis between the behavioral data and the scores of the Movement Imagery Questionnaire-Revised(MIQ-R). For each subject, the results showed significant angular and body location effect in the process of mental rotation. For all subjects, the results showed that there were correlations between the behavioral data and the scores of MIQ-R. Subjects who needed the longer reaction time represented lower motor imagery abilities in the same test, and vice versa. This research laid the foundation for the further study on brain electrophysiology in the process of mental rotation based on MI.
The research on brain functional mechanism and cognitive status based on brain network has the vital significance. According to a time–frequency method, partial directed coherence (PDC), for measuring directional interactions over time and frequency from scalp-recorded electroencephalogram (EEG) signals, this paper proposed dynamic PDC (dPDC) method to model the brain network for motor imagery. The parameters attributes (out-degree, in-degree, clustering coefficient and eccentricity) of effective network for 9 subjects were calculated based on dataset from BCI competitions IV in 2008, and then the interaction between different locations for the network character and significance of motor imagery was analyzed. The clustering coefficients for both groups were higher than those of the random network and the path length was close to that of random network. These experimental results show that the effective network has a small world property. The analysis of the network parameter attributes for the left and right hands verified that there was a significant difference on ROI2 (P = 0.007) and ROI3 (P = 0.002) regions for out-degree. The information flows of effective network based dPDC algorithm among different brain regions illustrated the active regions for motor imagery mainly located in fronto-central regions (ROI2 and ROI3) and parieto-occipital regions (ROI5 and ROI6). Therefore, the effective network based dPDC algorithm can be effective to reflect the change of imagery motor, and can be used as a practical index to research neural mechanisms.
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 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.
