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Objectives: To identify potential treatment targets for spinal cord injury (SCI)-related neuropathic pain (NP) by analysing the differences in electroencephalogram (EEG) and brain network connections among SCI patients with NP or numbness. Participants and methods: The EEG signals during rest, as well as left- and righthand and feet motor imagination (MI), were recorded. The power spectral density (PSD) of the (4-8 Hz), a (8-12 Hz), and ß (13-30 Hz) bands was calculated by applying Continuous Wavelet Transform (CWT) and Modified S-transform (MST) to the data. We used 21 electrodes as network nodes and performed statistical measurements of the phase synchronisation between two brain regions using a phase-locking value, which captures nonlinear phase synchronisation. Results: The specificity of the MST algorithm was higher than that of the CWT. Widespread non-lateralised event-related synchronization was observed in both groups during the left- and right-hand MI. The PWP (patients with pain) group had lower and a bands PSD values in multiple channels of regions including the frontal, premotor, motor, and temporal regions compared with the PWN (patients with numbness) group (all p < 0.05), but higher ß band PSD values in multiple channels of regions including the frontal, premotor, motor, and parietal region compared with the PWN group (all p < 0.05). During left-hand and feet MI, in the lower frequency bands (and a bands), the brain network connections of the PWP group were significantly weaker than the PWN group except for the frontal region. Conversely, in the higher frequency bands (ß band), the brain network connections of the PWP group were significantly stronger in all regions than the PWN group. Conclusion: The differences in the power of EEG and network connectivity in the frontal, premotor, motor, and temporal regions are potential biological and functional characteristics that can be used to distinguish NP from numbness. The differences in brain network connections between the two groups suggest that the distinct mechanisms for pain and numbness. [ABSTRACT FROM AUTHOR]

Objective：The electroencephalogram (EEG) signals were collected for analysis to define the differences in dynamic functional connectivity of the brain network of related nodes in the primary motor area (M1) and premotor area (PMA) during motor imagination and motor execution. The relationship between muscle synergy and isolated movement was also explored. Method：Ten stroke patients with right hemiplegia and nineteen healthy adults participated in this study. All participants performed motor imagination (MI) and motor execution (ME) tasks according to visual instructions. We recorded and analyzed the EEG signals at 12 sites located in M1 and PMA areas. The chosen EEG signals were filtered and analyzed based on the modified S- transform (MST) . All data were normalized to avoid individual differences. Then we analyzed the data with Pearson correlation to identify the dynamic functional connectivity (FC) and the differences with Fisher’s exact test for node degrees. Result：All the distribution trend of correlation degree of chosen node about left or right MI and ME of stroke patients was similar to that of healthy participants. Compared with the motion execution, the function connection strength and density of each node were elevated at MI, which was also consistent with healthy participants. When healthy adults underwent left hand MI, the degree of the C4 node in the M1 area was significantly higher than that of C3 on the opposite side (P<0.05), while at right hand MI, the sum of the node degrees of FC3 and FC1 in the left PMA area was significantly higher than that of the lateral symmetric channel FC4 and FC2 (P<0.05) . When the right upper limb isolated movement was performed, the node degree of C3 decreased significantly (P<0.05) . Conclusion：The major region of function connectivity of the right hand MI was in the left PMA area, and the node degree at MI was higher than ME. The functional connectivity of each node at the left hand MI was dispersed. The main channels activated by the muscle synergy are different from the isolated movement. [ABSTRACT FROM AUTHOR]

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Physiological circuits differ across increasing isometric force levels during unilateral contraction. Therefore, we first explored the possibility of predicting the force level based on electroencephalogram (EEG) activity recorded during a single trial of unilateral 5% or 40% of maximal isometric voluntary contraction (MVC) in right-hand grip imagination. Nine healthy subjects were involved in this study. The subjects were required to randomly perform 20 trials for each force level while imagining a right-hand grip. We proposed the use of common spatial patterns (CSPs) and coherence between EEG signals as features in a support vector machine for force level prediction. The results showed that the force levels could be predicted through single-trial EEGs while imagining the grip (mean accuracy = 81.4 ± 13.29%). Additionally, we tested the possibility of online control of a ball game using the above paradigm through unilateral grip imagination at different force levels (i.e., 5% of MVC imagination and 40% of MVC imagination for right-hand movement control). Subjects played the ball games effectively by controlling direction with our novel BCI system (n = 9, mean accuracy = 76.67 ± 9.35%). Data analysis validated the use of our BCI system in the online control of a ball game. This information may provide additional commands for the control of robots by users through combinations with other traditional brain–computer interfaces, e.g., different limb imaginations. [ABSTRACT FROM AUTHOR]

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]

Brain-computer interfaces (BCIs) can serve as a means for stroke rehabilitation, but low BCI performance can decrease agency (users’ perceived control), frustrate users and thereby hamper rehabilitation. In such rehabilitative tasks BCIs can implement fabricated input (preprogrammed positive feedback) that improve agency and frustration. Two substudies with healthy subjects and stroke patients investigated this potential through completion of a game and a simple task with: 1) 16 healthy subjects using motor imagery-based online BCI and 2) 13 stroke patients using a surrogate BCI system based on eye-blink detection through an eye-tracker to have a highly reliable input signal. Substudy 1 measured perceived control and frustration in four conditions: 1) unaltered BCI control, 2) 30% guaranteed positive feedback from fabricated input 3) 50% guaranteed negative feedback, and 4) 50% guaranteed negative feedback and 30% guaranteed positive feedback. In substudy 2, stroke patients had 50% control over outcomes and four conditions added from 0% to 50% positive feedback. In both substudies, positive feedback improved participants’ perceived control and reduced frustration with increasing improvements when the amount of positive fabricated input increased. The stroke patients did not react as much to the fabricated input as the healthy participants. Fabricated input can be concealed in both online and surrogate BCIs which can be used to improve perceived control and frustration in a game-based interaction and simple task. This suggests that BCI designers can exercise artistic freedom to create engaging motor imagery-based interactions of narrative-based games or simpler gamified interactions to facilitate improved training efforts.

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

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]

Objectives: To identify potential treatment targets for spinal cord injury (SCI)-related neuropathic pain (NP) by analysing the differences in electroencephalogram (EEG) and brain network connections among SCI patients with NP or numbness., Participants and Methods: The EEG signals during rest, as well as left- and right-hand and feet motor imagination (MI), were recorded. The power spectral density (PSD) of the θ (4-8 Hz), α (8-12 Hz), and β (13-30 Hz) bands was calculated by applying Continuous Wavelet Transform (CWT) and Modified S-transform (MST) to the data. We used 21 electrodes as network nodes and performed statistical measurements of the phase synchronisation between two brain regions using a phase-locking value, which captures nonlinear phase synchronisation., Results: The specificity of the MST algorithm was higher than that of the CWT. Widespread non-lateralised event-related synchronization was observed in both groups during the left- and right-hand MI. The PWP (patients with pain) group had lower θ and α bands PSD values in multiple channels of regions including the frontal, premotor, motor, and temporal regions compared with the PWN (patients with numbness) group (all p  < 0.05), but higher β band PSD values in multiple channels of regions including the frontal, premotor, motor, and parietal region compared with the PWN group (all p  < 0.05). During left-hand and feet MI, in the lower frequency bands (θ and α bands), the brain network connections of the PWP group were significantly weaker than the PWN group except for the frontal region. Conversely, in the higher frequency bands (β band), the brain network connections of the PWP group were significantly stronger in all regions than the PWN group., Conclusion: The differences in the power of EEG and network connectivity in the frontal, premotor, motor, and temporal regions are potential biological and functional characteristics that can be used to distinguish NP from numbness. The differences in brain network connections between the two groups suggest that the distinct mechanisms for pain and numbness., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Zhang, Xin, Wang, Yue, Guo, Wang, Zhang and Xu.)

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]

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.

Long-term depression (LTD) and long-term potentiation (LTP)-like plasticity are models of synaptic plasticity which have been associated with memory and learning. The induction of LTD and LTP-like plasticity, using different stimulation protocols, has been proposed as a means of addressing abnormalities in cortical excitability associated with conditions such as focal hand dystonia and stroke. The aim of this study was to investigate whether the excitability of the cortical projections to the tibialis anterior (TA) muscle could be decreased when dorsiflexion of the ankle joint was imagined and paired with peripheral electrical stimulation (ES) of the nerve supplying the antagonist soleus muscle. The effect of stimulus timing was evaluated by comparing paired stimulation timed to reach the cortex before, at and after the onset of imagined movement. Fourteen healthy subjects participated in six experimental sessions held on non-consecutive days. The timing of stimulation delivery was determined offline based on the contingent negative variation (CNV) of electroencephalography brain data obtained during imagined dorsiflexion. Afferent stimulation was provided via a single pulse ES to the peripheral nerve paired, based on the CNV, with motor imagination of ankle dorsiflexion. A significant decrease (P = 0.001) in the excitability of the cortical projection of TA was observed when the afferent volley from the ES of the tibial nerve (TN) reached the cortex at the onset of motor imagination based on the CNV. When TN stimulation was delivered before (P = 0.62), or after (P = 0.23) imagined movement onset there was no significant effect. Nor was a significant effect found when ES of the TN was applied independent of imagined movement (P = 0.45). Therefore, the excitability of the cortical projection to a muscle can be inhibited when ES of the nerve supplying the antagonist muscle is precisely paired with the onset of imagined movement. [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 investigated the possibility of identifying the speed of an imagined movement from EEG recordings in amyotrophic lateral sclerosis (ALS) patients. EEG signals were acquired from four ALS patients during imagination of wrist extensions at two speeds (fast and slow), each repeated up to 100 times in random order. The movement-related cortical potentials (MRCPs) and averaged sensorimotor rhythm associated with the two tasks were obtained from the EEG recordings. Moreover, offline single-trial EEG classification was performed with discrete wavelet transform for feature extraction and support vector machine for classification. The speed of the task was encoded in the time delay of peak negativity in the MRCPs, which was shorter for faster than for slower movements. The average single-trial misclassification rate between speeds was 30.4 ± 3.5 % when the best scalp location and time interval were selected for each individual. The scalp location and time interval leading to the lowest misclassification rate varied among patients. The results indicate that the imagination of movements at different speeds is a viable strategy for controlling a brain-computer interface system by ALS patients.

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]

Abstract: It is well known that motor evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) of the motor cortex are facilitated by voluntary muscle contraction. We evaluated the effects of imagination of movements on MEP latencies of agonist and antagonist muscles in the hand using TMS. Twenty-two healthy volunteers were studied. TMS delivered at rest and while imagining tonic abduction of the right thumb. MEPs were recorded in response to magnetic stimulation over the scalp and cervical spine (C7–T1), and central motor conduction times (CMCT) were calculated. MEPs were recorded from right abductor pollicis brevis muscle (APB) and adductor pollicis muscle (AP) simultaneously. Imagination of abduction resulted in a shortened latency of MEPs in the APB muscle, and a prolonged latency in the AP muscle. But the imagination caused no significant change in the latency of MEPs elicited by stimulation over the cervical spine. The changes of the CMCT may account for these latency changes with imagination of movement. These findings indicate that imagination of thumb abduction facilitates motoneurons of agonist muscle and has an inhibitory effect on those of antagonist muscle (reciprocal inhibition). [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]

We investigated the cerebral networks involved in execution and mental imagery of sequential movements of the left foot, both performed at slow and fast speed. Twelve volunteers were scanned with a 3T MRI during execution and imagination of a sequence of ankle movements. Overt movement execution and motor imagery shared a common network including the premotor, parietal and cingulate cortices, the striatum and the cerebellum. Motor imagery recruited specifically the prefrontal cortex, whereas motor execution recruited specifically the sensorimotor cortex. We also found that slow movements specifically recruited frontopolar and right dorsomedian prefrontal areas bilaterally, during both execution and mental imagery, whereas fast movements strongly activated the sensorimotor cerebral cortex. Finally, we noted that anterior vermis, lobules VI/VII and VIII of the cerebellum were specifically activated during fast movements, both in imagination and execution. We show that the selection of the neural networks underlying voluntary movement of the foot is depending on the speed strategy and is sensitive to execution versus imagery. Moreover, to the light of surprising recent findings in monkeys showing that the vermis should no longer be considered as entirely isolated from the cerebral cortex (Coffman et al., 2011 [2]), we suggest that the anterior vermis contributes to computational aspects of fast commands, whereas more lateral cerebellar superior lobe and lobule VIII would regulate patterning and sequencing of submovements in conjunction with movement rate. We also suggest that execution of overt slow movements, which strongly involves prefrontal executive cortex as during motor mental imagery, is associated with conscious mental representation of the ongoing movements., (Copyright © 2012 Elsevier Masson SAS. All rights reserved.)

The study investigated the possibility of identifying the speed of an imagined movement from EEG recordings in amyotrophic lateral sclerosis (ALS) patients. EEG signals were acquired from four ALS patients during imagination of wrist extensions at two speeds (fast and slow), each repeated up to 100 times in random order. The movement-related cortical potentials (MRCPs) and averaged sensorimotor rhythm associated with the two tasks were obtained from the EEG recordings. Moreover, offline single-trial EEG classification was performed with discrete wavelet transform for feature extraction and support vector machine for classification. The speed of the task was encoded in the time delay of peak negativity in the MRCPs, which was shorter for faster than for slower movements. The average single-trial misclassification rate between speeds was 30.4 +/- 3.5% when the best scalp location and time interval were selected for each individual. The scalp location and time interval leading to the lowest misclassification rate varied among patients. The results indicate that the imagination of movements at different speeds is a viable strategy for controlling a brain-computer interface system by ALS patients.