Regarding to the channel selection problem during the classification of electroencephalogram (EEG) signals, we proposed a novel method, Relief-SBS, in this paper. Firstly, the proposed method performed EEG channel selection by combining the principles of Relief and sequential backward selection (SBS) algorithms. And then correlation coefficient was used for classification of EEG signals. The selected channels that achieved optimal classification accuracy were considered as optimal channels. The data recorded from motor imagery task experiments were analyzed, and the results showed that the channels selected with our proposed method achieved excellent classification accuracy, and also outperformed other feature selection methods. In addition, the distribution of the optimal channels was proved to be consistent with the neurophysiological knowledge. This demonstrates the effectiveness of our method. It can be well concluded that our proposed method, Relief-SBS, provides a new way for channel selection.
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.
Brain-computer interface (BCI) can be summarized as a system that uses online brain information to realize communication between brain and computer. BCI has experienced nearly half a century of development, although it now has a high degree of awareness in the public, but the application of BCI in the actual scene is still very limited. This collection invited some BCI teams in China to report their efforts to promote BCI from laboratory to real scene. This paper summarizes the main contents of the invited papers, and looks forward to the future of BCI.
Electroencephalogram (EEG) classification for brain-computer interface (BCI) is a new way of realizing human-computer interreaction. In this paper the application of semi-supervised sparse representation classifier algorithms based on help training to EEG classification for BCI is reported. Firstly, the correlation information of the unlabeled data is obtained by sparse representation classifier and some data with high correlation selected. Secondly, the boundary information of the selected data is produced by discriminative classifier, which is the Fisher linear classifier. The final unlabeled data with high confidence are selected by a criterion containing the information of distance and direction. We applied this novel method to the three benchmark datasets, which were BCIⅠ, BCIⅡ_Ⅳ and USPS. The classification rate were 97%,82% and 84.7%, respectively. Moreover the fastest arithmetic rate was just about 0.2 s. The classification rate and efficiency results of the novel method are both better than those of S3VM and SVM, proving that the proposed method is effective.
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.
Individual differences of P300 potentials lead to that a large amount of training data must be collected to construct pattern recognition models in P300-based brain-computer interface system, which may cause subjects’ fatigue and degrade the system performance. TrAdaBoost is a method that transfers the knowledge from source area to target area, which improves learning effect in the target area. Our research purposed a TrAdaBoost-based linear discriminant analysis and a TrAdaBoost-based support vector machine to recognize the P300 potentials across multiple subjects. This method first trains two kinds of classifiers separately by using the data deriving from a small amount of data from same subject and a large amount of data from different subjects. Then it combines all the classifiers with different weights. Compared with traditional training methods that use only a small amount of data from same subject or mixed different subjects’ data to directly train, our algorithm improved the accuracies by 19.56% and 22.25% respectively, and improved the information transfer rate of 14.69 bits/min and 15.76 bits/min respectively. The results indicate that the TrAdaBoost-based method has the potential to enhance the generalization ability of brain-computer interface on the individual differences.
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.
High-density channels are often used to acquire electroencephalogram (EEG) spatial information in different cortical regions of the brain in brain-computer interface (BCI) systems. However, applying excessive channels is inconvenient for signal acquisition, and it may bring artifacts. To avoid these defects, the common spatial pattern (CSP) algorithm was used for channel selection and a selection criteria based on norm-2 is proposed in this paper. The channels with the highest M scores were selected for the purpose of using fewer channels to acquire similar rate with high density channels. The DatasetⅢa from BCI competition 2005 were used for comparing the classification accuracies of three motor imagery between whole channels and the selected channels with the present proposed method. The experimental results showed that the classification accuracies of three subjects using the 20 channels selected with the present method were all higher than the classification accuracies using all 60 channels, which convinced that our method could be more effective and useful.
Brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) is a new-type human-computer interaction technique. To explore the separability of fNIRS signals in different motor imageries on the single limb, the study measured the fNIRS signals of 15 subjects (amateur football fans) during three different motor imageries of the right foot (passing, stopping and shooting). And the correlation coefficient of the HbO signal during different motor imageries was extracted as features for the input of a three-classification model based on support vector machines. The results found that the classification accuracy of the three motor imageries of the right foot was 78.89%±6.161%. The classification accuracy of the two-classification of motor imageries of the right foot, that is, passing and stopping, passing and shooting, and stopping and shooting was 85.17%±4.768%, 82.33%±6.011%, and 89.33%±6.713%, respectively. The results demonstrate that the fNIRS of different motor imageries of the single limb is separable, which is expected to add new control commands to fNIRS-BCI and also provide a new option for rehabilitation training and control peripherals for unilateral stroke patients. Besides, the study also confirms that the correlation coefficient can be used as an effective feature to classify different motor imageries.
Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups ofⅣ_ⅢandⅣ_Ⅰ. The experimental results proved that the method proposed in this paper was feasible.
