This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4th International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.
ObjectiveTo investigate the feasibility and effectiveness of motor imagery based brain computer interface with wrist passive movement in chronic stroke patients with wrist extension impairment.MethodsFifteen chronic stroke patients with a mean age of (47.60±14.66) years were recruited from March 2017 to June 2018. At baseline, motor imagery ability was assessed first. Then motor imagery based brain computer interface with wrist passive movement was given as an intervention. Both range of motion of paretic wrist and Barthel index was assessed before and after the intervention.ResultsAmong the 15 chronic stroke patients admitted in the study, 12 finished the whole therapy, and 3 failed to pass the initial assessment. After the therapy, the 12 participants who completed the whole sessions of the treatment and follow up had improved ability of control electroencephalogram, in whom 9 regained the ability to actively extend the affected wrist, and the other 3 failed to actively extend their wrist (the rate of active extending wrist was 75%). The activity of daily life of all the participants did not change significantly before and after intervention, and no discomfort was found after daily treatment.ConclusionIn chronic stroke patients with wrist extension impairment, motor imagery based brain computer interface with wrist passive movement training is feasible and effective.
One of the key problems of brain-computer interfaces (BCI) is low signal-to-noise ratio (SNR) of electroencephalogram (EEG) signals. It affects recognition performance. To remove the artifact and noise, block under-determined blind source separation method based on the small number of channels is proposed in this paper. The non-stationary EEG signals are turned into block stationary signals by piecewise. The mixing matrix is estimated by the second-order under-determined blind mixing matrix identification. Then, the beamformer based on minimum mean square error separates the original sources of signals. Eventually, the reconstructed EEG for mixed signals removes the unwanted components of source signals to achieve suppressing artifact. The experiment results on the real motor imagery BCI indicated that the block under-determined blind source separation method could reconstruct signals and remove artifact effectively. The accuracy of motor imagery task of BCI has been greatly improved.
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 is often used in the fields of sports training and neurorehabilitation for its advantages of being highly targeted, easy to learn, and requiring no special equipment, and has become a major research paradigm in cognitive neuroscience. Transcranial direct current stimulation (tDCS), an emerging neuromodulation technique, modulates cortical excitability, which in turn affects functions such as locomotion. However, it is unclear whether tDCS has a positive effect on motor imagery task states. In this paper, 16 young healthy subjects were included, and the electroencephalogram (EEG) signals and near-infrared spectrum (NIRS) signals of the subjects were collected when they were performing motor imagery tasks before and after receiving tDCS, and the changes in multiscale sample entropy (MSE) and haemoglobin concentration were calculated and analyzed during the different tasks. The results found that MSE of task-related brain regions increased, oxygenated haemoglobin concentration increased, and total haemoglobin concentration rose after tDCS stimulation, indicating that tDCS increased the activation of task-related brain regions and had a positive effect on motor imagery. This study may provide some reference value for the clinical study of tDCS combined with motor imagery.
Transfer learning is provided with potential research value and application prospect in motor imagery electroencephalography (MI-EEG)-based brain-computer interface (BCI) rehabilitation system, and the source domain classification model and transfer strategy are the two important aspects that directly affect the performance and transfer efficiency of the target domain model. Therefore, we propose a parameter transfer learning method based on shallow visual geometry group network (PTL-sVGG). First, Pearson correlation coefficient is used to screen the subjects of the source domain, and the short-time Fourier transform is performed on the MI-EEG data of each selected subject to acquire the time-frequency spectrogram images (TFSI). Then, the architecture of VGG-16 is simplified and the block design is carried out, and the modified sVGG model is pre-trained with TFSI of source domain. Furthermore, a block-based frozen-fine-tuning transfer strategy is designed to quickly find and freeze the block with the greatest contribution to sVGG model, and the remaining blocks are fine-tuned by using TFSI of target subjects to obtain the target domain classification model. Extensive experiments are conducted based on public MI-EEG datasets, the average recognition rate and Kappa value of PTL-sVGG are 94.9% and 0.898, respectively. The results show that the subjects’ optimization is beneficial to improve the model performance in source domain, and the block-based transfer strategy can enhance the transfer efficiency, realizing the rapid and effective transfer of model parameters across subjects on the datasets with different number of channels. It is beneficial to reduce the calibration time of BCI system, which promote the application of BCI technology in rehabilitation engineering.
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.
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.