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Electroencephalogram (EEG) is a non-stationary random signal with strong background noise, which makes its feature extraction difficult and recognition rate low. This paper presents a feature extraction and classification model of motor imagery EEG signals based on wavelet threshold denoising. Firstly, this paper uses the improved wavelet threshold algorithm to obtain the denoised EEG signal, divides all EEG channel data into multiple partially overlapping frequency bands, and uses the common spatial pattern (CSP) method to construct multiple spatial filters to extract the characteristics of EEG signals. Secondly, EEG signal classification and recognition are realized by the support vector machine algorithm optimized by a genetic algorithm. Finally, the dataset of the third brain-computer interface (BCI) competition and the dataset of the fourth BCI competition is selected to verify the classification effect of the algorithm. The highest accuracy of this method for two BCI competition datasets is 92.86% and 87.16%, respectively, which is obviously superior to the traditional algorithm model. The accuracy of EEG feature classification is improved. It shows that an overlapping sub-band filter banks common spatial pattern-genetic algorithms optimization-support vector machines (OSFBCSP-GAO-SVM) model is an effective model for feature extraction and classification of motor imagination EEG signals. [ABSTRACT FROM AUTHOR]

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The brain-computer interface (BCI) connects the brain and the external world through an information transmission channel by interpreting the physiological information of the brain during thinking activities. The effective classification of electroencephalogram (EEG) signals is the key to improving the performance of the system. To improve the classification accuracy of EEG signals in the BCI system, the transfer learning algorithm and the improved Common Spatial Pattern (CSP) algorithm are combined to construct a data classification model. Finally, the effectiveness of the proposed algorithm is verified. The results show that in actual and imagined movements, the accuracy of the left- and right-hand movements at different speeds is higher than when the speeds are the same. The proposed Adaptive Composite Common Spatial Pattern (ACCSP) and Self Adaptive Common Spatial Pattern (SACSP) algorithms have good classification effects on 5 subjects, with an average classification accuracy rate of 83.58 percent, which is an increase of 6.96 percent compared with traditional algorithms. When the training sample size is 10, the classification accuracy of the ACCSP algorithm is higher than that of the traditional CSP algorithm. The improved CSP algorithm combined with transfer learning embodies a good classification effect in both ACCSP and SACSP. Especially, the performance of SACSP mode is better. Combining the improved CSP algorithm proposed with the CSP-based transfer learning algorithm can improve the classification accuracy of the BCI classifier. [ABSTRACT FROM AUTHOR]

Brain computer interaction (BCI) based on EEG can help patients with limb dyskinesia to carry out daily life and rehabilitation training. However, due to the low signal-to-noise ratio and large individual differences, EEG feature extraction and classification have the problems of low accuracy and efficiency. To solve this problem, this paper proposes a recognition method of motor imagery EEG signal based on deep convolution network. This method firstly aims at the problem of low quality of EEG signal characteristic data, and uses short-time Fourier transform (STFT) and continuous Morlet wavelet transform (CMWT) to preprocess the collected experimental data sets based on time series characteristics. So as to obtain EEG signals that are distinct and have time-frequency characteristics. And based on the improved CNN network model to efficiently recognize EEG signals, to achieve high-quality EEG feature extraction and classification. Further improve the quality of EEG signal feature acquisition, and ensure the high accuracy and precision of EEG signal recognition. Finally, the proposed method is validated based on the BCI competiton dataset and laboratory measured data. Experimental results show that the accuracy of this method for EEG signal recognition is 0.9324, the precision is 0.9653, and the AUC is 0.9464. It shows good practicality and applicability. [ABSTRACT FROM AUTHOR]

Motion imaging (MI) refers to the psychological realization of motions without movement or muscle activity; the basis of neural rehabilitation as a brain-computer interface (BCI) technique has been extensively studied. The combination of motor imaging and brain-computer interface technology can take advantage of patients' willingness to take the initiative to assist them in rehabilitation. Studies have shown that MI combined with BCI rehabilitation training is better than traditional rehabilitation training. Transient visual evoked potentials and motor imaging constructed a hybrid BCI system. Three healthy subjects were tested. EEG signals were superimposed preprocessing according to visual stimulus superimposed frequency and motor guidance frequency respectively. Transient visual evoked EEG segmentation is used as a control signal of choice, the use of wavelet decomposition helps to extract features, and then use BP neural network recognition for classification and identification. Visual guidance, motion-oriented event-related synchronization, or desynchronization feature signals as rehabilitation exercise control signals, are using time-domain sliding energy analysis to extract features, and then using BP neural network recognition for classification and identification. EEG signals collected in the experiment were superimposed signals of transient visual evoked and motorized EEG. There were 300 transient electroencephalogram (EEG) and 100 segments Imagine EEG segmentation. According to the results of the test, the average recognition rate of visual evoked EEG reached 95.42%; the average recognition rate of motor imaginary EEG was 73.08%, but there was a large individual difference in motor imaging EEG signals except 1 Name of the test rate of 85%, the remaining two subjects were less than 70% recognition rate. There is a large individual difference between motion imaging and signal feature recognition, and it takes a long time to train. Therefore, it is necessary to study further the selection of control signals for rehabilitation training. As the threshold feedback signal, controlling the amplitude feedback of rehabilitation training can promote the motivation of participants' motivation to stimulate and enhance the rehabilitation treatment effect. [ABSTRACT FROM AUTHOR]

Bimanual movements are an integral part of everyday activities and are often included in rehabilitation therapies. Yet, electroencephalography (EEG)-based assistive and rehabilitative brain–computer interface (BCI) systems typically rely on motor imagination (MI) of one limb at the time. In this paper, we present a classifier which discriminates between uni-and bi-manual MI. Ten able-bodied participants took part in cue-based motor execution (ME) and MI tasks of the left (L), right (R) and both (B) hands. A 32-channel EEG was recorded. Three linear discriminant analysis classifiers, based on MI of L–B, B–R, and B–L hands were created, with features based on wide band common spatial patterns (CSP) 8–30 Hz, and band specifics common spatial patterns (CSPb). Event-related desynchronization (ERD) was significantly stronger during bimanual compared to unimanual ME on both hemispheres. Bimanual MI resulted in bilateral parietally shifted ERD of similar intensity to unimanual MI. The average classification accuracy for CSP and CSPb was comparable for the L–R task (73% ± 9% and 75% ± 10%, respectively) and for the L–B task (73% ± 11% and 70% ± 9%, respectively). However, for the R–B task (67% ± 3% and 72% ± 6%, respectively), it was significantly higher for CSPb ($p = 0.0351$). Six participants whose L–R classification accuracy exceeded 70% were included in an online task a week later, using the unmodified offline CSPb classifier, achieving 69% ± 3% and 66% ± 3% accuracy for L–R and R–B tasks, respectively. Combined uni- and bi-manual BCI could be used for restoration of motor function of highly disabled patents and for motor rehabilitation of patients with motor deficits. [ABSTRACT FROM AUTHOR]

Many classification methods by machine learning and convolutional neural networks (CNN) have been proposed to recognize MI-EEG recently. However, the indescribable properties and individual differences of the MI-EEG signals cause low classification accuracy. In this study, a new MI-EEG classification method was designed to improve classification accuracy by combining Shannon complex wavelets and convolutional neural networks. First, the original MI-EEG was preprocessed using EEGLAB by channel selection and bandpass filtering. Second, the Shannon complex wavelet was used as the time–frequency transform strategy to calculate the time–frequency​ matrix. Finally, an improved Resnet was used to classify the time–frequency​ matrix to complete the MI-EEG identification. BCI competition IV dataset 2b as a public motor imagination dataset was tested to prove the validation of this proposed method. The classification accuracy and kappa value were adopted to prove the superiority of the proposed method by comparing it with the state-of-the-art classification methods. Experimental results showed that the classification accuracy and kappa values are 0.852 and 0.704, respectively, and they are the highest in the state-of-the-art. The parameter influence of wavelet wavelength and interception time on classification accuracy was discussed and optimized. This method can effectively improve classification accuracy and has a wide range of applications in MI-EEG classification. • We proposed a novel MI-EEG classification method by combining Shannon Complex wavelet and convolutional neural networks. • Classification accuracy and Kappa value were improved by 2% and 0.04 on BCI Competition IV Dataset 2b. • We discussed and optimized the parameter influence on the classification accuracy. [ABSTRACT FROM AUTHOR]

Feature extraction and automatic classification of mental states is an interesting and open area of research in the field of brain–computer interfacing (BCI). A well-trained classifier would allow the BCI system to control an external assistive device in real world problems. Sometimes, standard existing classifiers fail to generalize the components of a non-stationary signal, like Electroencephalography (EEG) which may pose one or more problems during real-time usage of the BCI system. In this paper, we aim to tackle this issue by designing an interval type-2 fuzzy classifier which deals with the uncertainties of the EEG signal over various sessions. Our designed classifier is used to decode various movements concerning the wrist (extension and flexion) and finger (opening and closing of a fist). For this purpose, we have employed extreme energy ratio (EER) to construct the feature vector. The average classification accuracy achieved during offline training and online testing over eight subjects are 86.45% and 78.44%, respectively. On comparison with other related works, it is shown that our designed IT2FS classifier presents a better performance. [ABSTRACT FROM AUTHOR]

Objective Motor imagination (MI) and functional electrical stimulation (FES) can activate the sensory-motor cortex through efferent and afferent pathways respectively. Motor imagination can be used as a control strategy to activate FES through a brain–computer interface as the part of a rehabilitation therapy. It is believed that precise timing between the onset of MI and FES is important for strengthening the cortico-spinal pathways but it is not known whether prolonged MI during FES influences cortical response. Methods Electroencephalogram was measured in ten able-bodied participants using MI strategy to control FES through a BCI system. Event related synchronisation/desynchronisation (ERS/ERD) over the sensory-motor cortex was analysed and compared in three paradigms: MI before FES, MI before and during FES and FES alone activated automatically. Results MI practiced both before and during FES produced strongest ERD. When MI only preceded FES it resulted in a weaker beta ERD during FES than when FES was activated automatically. Following termination of FES, beta ERD returns to the baseline level within 0.5 s while alpha ERD took longer than 1 s. Conclusions When MI and FES are combined for rehabilitation purposes it is recommended that MI is practiced throughout FES activation period. Significance The study is relevant for neurorehabilitation of movement. [ABSTRACT FROM AUTHOR]

The study investigates the accuracy in discriminating rate of torque development (RTD) and target torque (TT) (task parameters) from electroencephalography (EEG) signals generated during imaginary motor tasks. Signals were acquired from nine healthy subjects during four imaginary isometric plantar-flexions of the right foot involving two RTDs (ballistic and moderate) and two TTs (30 and 60% of the maximal voluntary contraction torque), each repeated 60 times in random order. The single-trial EEG traces were classified with a pattern recognition approach based on wavelet coefficients as features and support vector machine (SVM) as classifier. Average misclassification rates were (mean ± SD) 16 ± 9% and 26 ± 13% for discrimination of the two TTs under ballistic and moderate RTDs, respectively. RTDs could be discriminated with misclassification rates of 16 ± 11% and 19 ± 10% under high and low TT, respectively. These results indicate that differences in both TT and RTD can be detected from single-trial EEG traces during imaginary tasks. [ABSTRACT FROM AUTHOR]

Abstract: Objective: The study explored the possibility of identifying movement type and speed from EEG recordings. Methods: EEG signals were acquired from 9 healthy volunteers during imagination of four tasks of the right wrist that involved two speeds (fast and slow) and two types of movement (wrist extension and rotation), each repeated 60 times in random order. Average movement-related cortical potentials (MRCPs) were compared among the four tasks. Moreover, single-trial classification was performed using the rebound rate of MRCP and the power in the mu and beta bands as features. Results: The rebound rate of the average MRCPs was greater for faster than for slower movements but did not depend on the type of movement. Accordingly, pairs of tasks executed at different speeds led to lower misclassification rate than pairs of tasks executed at the same speed. The average misclassification rate between task pairs was 21±2% for the best channel and task pair. Conclusion: The task parameter speed can be discriminated in single-trial EEG traces with greater accuracy than the type of movement when tasks are executed at the same joint. Significance: The speed of movement execution may be included among the variables that characterize imagined tasks for brain–computer interface applications. [Copyright &y& Elsevier]

Brain-computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI system. The aims of this study were to develop a cheap 3D-printed wrist exoskeleton that can be controlled by a cheap open source BCI (OpenViBE), and to determine if training with such a setup could induce neural plasticity. Eleven healthy volunteers imagined wrist extensions, which were detected from single-trial electroencephalography (EEG), and in response to this, the wrist exoskeleton replicated the intended movement. Motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation were measured before, immediately after, and 30 min after BCI training with the exoskeleton. The BCI system had a true positive rate of 86 ± 12% with 1.20 ± 0.57 false detections per minute. Compared to the measurement before the BCI training, the MEPs increased by 35 ± 60% immediately after and 67 ± 60% 30 min after the BCI training. There was no association between the BCI performance and the induction of plasticity. In conclusion, it is possible to detect imaginary movements using an open-source BCI setup and control a cheap 3D-printed exoskeleton that when combined with the BCI can induce neural plasticity. These findings may promote the availability of BCI technology for rehabilitation clinics and home-use. However, the usability must be improved, and further tests are needed with stroke patients. [ABSTRACT FROM AUTHOR]