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Showing papers on "Motor imagery published in 2018"


Journal ArticleDOI
TL;DR: Previous models of the similarities in the networks for Motor Imagery, Action Observation, and Movement Execution are quantified and amended, while highlighting key differences in their recruitment of motor cortex, parietal cortex, and subcortical structures.

379 citations


Journal ArticleDOI
Wang Ping1, Aimin Jiang1, Xiaofeng Liu1, Jing Shang1, Li Zhang1 
18 Oct 2018
TL;DR: A one dimension-aggregate approximation (1d-AX) is employed to achieve robust classification, and Inspired by classical common spatial pattern, channel weighting technique is further deployed to enhance the effectiveness of the proposed classification framework.
Abstract: Classification of motor imagery electroencephalograph signals is a fundamental problem in brain–computer interface (BCI) systems. We propose in this paper a classification framework based on long short-term memory (LSTM) networks. To achieve robust classification, a one dimension-aggregate approximation (1d-AX) is employed to extract effective signal representation for LSTM networks. Inspired by classical common spatial pattern, channel weighting technique is further deployed to enhance the effectiveness of the proposed classification framework. Public BCI competition data are used for the evaluation of the proposed feature extraction and classification network, whose performance is also compared with that of the state-of-the-arts approaches based on other deep networks.

224 citations


Journal ArticleDOI
TL;DR: It is shown that motor imagery recruits forward models to elicit sensory attenuation to the same extent as real movements, and that the imagery-induced attenuation follows the same spatiotemporal principles as does the attenuation elicited by overt movements.
Abstract: Research on motor imagery has identified many similarities between imagined and executed actions at the behavioral, physiological and neural levels, thus supporting their "functional equivalence". In contrast, little is known about their possible "computational equivalence"-specifically, whether the brain's internal forward models predict the sensory consequences of imagined movements as they do for overt movements. Here, we address this question by assessing whether imagined self-generated touch produces an attenuation of real tactile sensations. Previous studies have shown that self-touch feels less intense compared with touch of external origin because the forward models predict the tactile feedback based on a copy of the motor command. Our results demonstrate that imagined self-touch is attenuated just as real self-touch is and that the imagery-induced attenuation follows the same spatiotemporal principles as does the attenuation elicited by overt movements. We conclude that motor imagery recruits the forward models to predict the sensory consequences of imagined movements.

173 citations


Journal ArticleDOI
TL;DR: BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.
Abstract: Objective Brain-computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional near infrared spectroscopy (fNIRS). A crucial step of BCI is brain state classification from recorded signal features. Deep artificial neural networks (DNNs) recently reached unprecedented complex classification outcomes. These performances were achieved through increased computational power, efficient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting significant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art deep learning procedures. Approach We performed a guided left and right hand motor imagery task on 15 subjects with a fixed classification response time of 1 s and overall experiment length of 10 min. Left versus right classification accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classifiers. Main results At a group level we obtained significant increase in performance when considering multi-modal recordings and DNN classifier with synergistic effect. Significance BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.

133 citations


Journal ArticleDOI
TL;DR: This work analyzes the spatio-temporal and time–frequency characteristics of the electrical brain activity, associated with both the motor execution and imagery in a group of untrained subjects, by applying different methods of nonlinear dynamics and creates an algorithm allowing online detection of the observed patterns.
Abstract: Identification of brain activity associated with motor execution and, more importantly, with motor imagery is necessary for the development of brain–computer interfaces. Most of recent studies were performed with trained participants which demonstrated that the motor-related brain activity can be detected from the analysis of multichannel electroencephalograms (EEG). For untrained subjects, this task is less studied, but at the same time much more challenging. This task can be solved using the methods of nonlinear dynamics, allowing to extract specific features of the neuronal network of the brain (e.g., the degree of complexity of EEG signals and degree of interaction between different brain areas). In this work, we analyze the spatio-temporal and time–frequency characteristics of the electrical brain activity, associated with both the motor execution and imagery in a group of untrained subjects, by applying different methods of nonlinear dynamics. At the first stage, we apply multifractal formalism to the analysis of EEG signals to reveal the brain areas which demonstrate the most significant distinctions between real motor actions and imaginary movement. Then, using time–frequency wavelet-based analysis of the EEG activity, we analyze in detail the structure of considered brain areas. As a result, we distinguish characteristic oscillatory patterns which occur in different areas of brain and interact with each other when the motor execution (or imagination) takes place. Finally, we create an algorithm allowing online detection of the observed patterns and experimentally verify its efficiency.

77 citations


Journal ArticleDOI
11 Jan 2018-PLOS ONE
TL;DR: An understanding of effective connectivity between motor and cognitive areas during motor execution and imagery as well as the basis for future connectivity studies for patients with stroke is provided.
Abstract: Background Recent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor execution and motor imagery, but the relationship between motor and cognitive areas has not yet been completely understood. The objectives of our study were to analyze the effective connectivity between motor and cognitive networks in order to define network dynamics during motor execution and motor imagery in healthy individuals. Second, we analyzed the differences in effective connectivity between correct and incorrect responses during motor execution and imagery using dynamic causal modeling (DCM) of electroencephalography (EEG) data. Method Twenty healthy subjects performed a sequence of finger tapping trials using either motor execution or motor imagery, and the performances were recorded. Changes in effective connectivity between the primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), and dorsolateral prefrontal cortex (DLPFC) were estimated using dynamic causal modeling. Bayesian model averaging with family-level inference and fixed-effects analysis was applied to determine the most likely connectivity model for these regions. Results Motor execution and imagery showed inputs to distinct brain regions, the premotor cortex and the supplementary motor area, respectively. During motor execution, the coupling strength of a feedforward network from the DLPFC to the PMC was greater than that during motor imagery. During motor imagery, the coupling strengths of a feedforward network from the PMC to the SMA and of a feedback network from M1 to the PMC were higher than that during motor execution. In imagined movement, although there were connectivity differences between correct and incorrect task responses, each motor imagery task that included correct and incorrect responses showed similar network connectivity characteristics. Correct motor imagery responses showed connectivity from the PMC to the DLPFC, while the incorrect responses had characteristic connectivity from the SMA to the DLPFC. Conclusions These findings provide an understanding of effective connectivity between motor and cognitive areas during motor execution and imagery as well as the basis for future connectivity studies for patients with stroke.

74 citations


Journal ArticleDOI
TL;DR: A newly developed standard for presenting results acquired during MIBCI experiments is proposed, designed to facilitate communication and comparison of essential information regarding the effects observed, based on the findings of descriptive analysis and meta-analysis.
Abstract: Brain-Computer Interfaces (BCI) constitute an alternative channel of communication between humans and environment. There are a number of different technologies which enable the recording of brain activity. One of these is electroencephalography (EEG). The most common EEG methods include interfaces whose operation is based on changes in the activity of Sensorimotor Rhythms (SMR) during imagery movement, so-called Motor Imagery BCI (MIBCI). The present article is a review of 131 articles published from 1997 to 2017 discussing various procedures of data processing in MIBCI. The experiments described in these publications have been compared in terms of the methods used for data registration and analysis. Some of the studies (76 reports) were subjected to meta-analysis which showed corrected average classification accuracy achieved in these studies at the level of 51.96%, a high degree of heterogeneity of results (Q = 1806577.61; df= 486; p < 0.001; I2 = 99.97%), as well as significant effects of number of channels, number of mental images, and method of spatial filtering. On the other hand the meta-regression failed to provide evidence that there was an increase in the effectiveness of the solutions proposed in the articles published in recent years. The authors have proposed a newly developed standard for presenting results acquired during MIBCI experiments, which is designed to facilitate communication and comparison of essential information regarding the effects observed. Also, based on the findings of descriptive analysis and meta-analysis, the authors formulated recommendations regarding practices applied in research on signal processing in MIBCIs.

70 citations


Journal ArticleDOI
TL;DR: System's performance showed that it has a potential to be used for hand rehabilitation of stroke patients, and was designed using a bank of temporal filters, the common spatial pattern algorithm for feature extraction and particle swarm optimisation for feature selection.
Abstract: Motor imagery-based brain-computer interfaces (BCI) have shown potential for the rehabilitation of stroke patients; however, low performance has restricted their application in clinical environments. Therefore, this work presents the implementation of a BCI system, coupled to a robotic hand orthosis and driven by hand motor imagery of healthy subjects and the paralysed hand of stroke patients. A novel processing stage was designed using a bank of temporal filters, the common spatial pattern algorithm for feature extraction and particle swarm optimisation for feature selection. Offline tests were performed for testing the proposed processing stage, and results were compared with those computed with common spatial patterns. Afterwards, online tests with healthy subjects were performed in which the orthosis was activated by the system. Stroke patients’ average performance was 74.1 ± 11%. For 4 out of 6 patients, the proposed method showed a statistically significant higher performance than the common spatial pattern method. Healthy subjects’ average offline and online performances were of 76.2 ± 7.6% and 70 ± 6.7, respectively. For 3 out of 8 healthy subjects, the proposed method showed a statistically significant higher performance than the common spatial pattern method. System’s performance showed that it has a potential to be used for hand rehabilitation of stroke patients.

63 citations


Proceedings ArticleDOI
01 Jan 2018
TL;DR: This paper obtained time-frequency representations of EEGs in cases of left and right motor imageries using continuous wavelet transform (CWT) and shows that the proposed method provides the improved accuracy compared to the traditional machine learning based classification methods.
Abstract: Motor imagery classification using electroencephalogram (EEG) is increasingly becoming popular in brain-computer interface (BCI) field. In this paper, we present a novel convolution neural networks (CNN) approach for classifying motor imagery electroencephalogram (EEG). For this, we obtained time-frequency representations of EEGs in cases of left and right motor imageries using continuous wavelet transform (CWT). Through experiments using well known public benchmark dataset, it shows that the proposed method provides the improved accuracy compared to the traditional machine learning based classification methods. It confirms the usefulness of the CNN scheme for BCI research field.

61 citations


Journal ArticleDOI
TL;DR: The results show that the presence of visual imagery and specifically related alpha power changes are useful to broaden the range of BCI control strategies.
Abstract: Currently the most common imagery task used in Brain-Computer Interfaces (BCIs) is motor imagery, asking a user to imagine moving a part of the body. This study investigates the possibility to build BCIs based on another kind of mental imagery, namely “visual imagery”. We study to what extent can we distinguish alternative mental processes of observing visual stimuli and imagining it to obtain EEG-based BCIs. Per trial, we instructed each of 26 users who participated in the study to observe a visual cue of one of two predefined images (a flower or a hammer) and then imagine the same cue, followed by rest. We investigated if we can differentiate between the different subtrial types from the EEG alone, as well as detect which image was shown in the trial. We obtained the following classifier performances: (i) visual imagery vs. visual observation task (71% of classification accuracy), (ii) visual observation task towards different visual stimuli (classifying one observation cue versus another observation cue with an accuracy of 61%) and (iii) resting vs. observation/imagery (77% of accuracy between imagery task versus resting state, and the accuracy of 75% between observation task versus resting state). Our results show that the presence of visual imagery and specifically related alpha power changes are useful to broaden the range of BCI control strategies.

61 citations


Journal ArticleDOI
TL;DR: In this article, the effects of simultaneous and alternate AOMI combinations on the learning of a dart throwing task were examined, where participants were randomly allocated to one of five training groups: action observation (AO), motor imagery (MI), simultaneous action observation and motor imagery(S-AOMI), alternate action observation, and a control group, with the exception of the AO and control groups significantly improved performance following the intervention.

Journal ArticleDOI
13 Nov 2018
TL;DR: An asynchronous control paradigm based on sequential motor imagery (sMI) is proposed to enrich the control commands of a motor imagery -based brain-computer interface to test the feasibility and the effectiveness of this paradigm in wheelchair navigation control.
Abstract: In this paper, an asynchronous control paradigm based on sequential motor imagery (sMI) is proposed to enrich the control commands of a motor imagery -based brain-computer interface. We test the feasibility and report the performance of this paradigm in wheelchair navigation control. By sequentially imaging left- and right-hand movements, the subjects can complete four sMI tasks in an asynchronous mode that are then encoded to control six steering functions of a wheelchair, including moving forward, turning left, turning right, accelerating, decelerating, and stopping. Two experiments, a simulated experiment, and an online wheelchair navigation experiment, were conducted to evaluate the performance of the proposed approach in seven subjects. In summary, the subjects completed 99 of 105 experimental trials along a predefined route. The success rate was 94.2% indicating the practicality and the effectiveness of the proposed asynchronous control paradigm in wheelchair navigation control.

Journal ArticleDOI
TL;DR: Results showed that, in some subjects, it is possible to replace up to 50% of frames with artificial data, which reduces training time from 720 to 360 s, and the method can be used to replace EEG frames that contain artifact, which reduced the impact of rejecting data with artifact.
Abstract: EEG-based Brain-Computer Interfaces (BCIs) are becoming a new tool for neurorehabilitation. BCIs are used to help stroke patients to improve the functional capability of the impaired limbs, and to communicate and assess the level of consciousness in Disorder of Consciousness (DoC) patients. BCIs based on a motor imagery paradigm typically require a training period to adapt the system to each user’s brain, and the BCI then creates and uses a classifier created with the acquired EEG. The quality of this classifier relies on amount of data used for training. More data can improve the classifier, but also increases the training time, which can be especially problematic for some patients. Training time might be reduced by creating new artificial frames by applying Empirical Mode Decomposition (EMD) on the EEG frames and mixing their Intrinsic Mode Function (IMFs). The purpose of this study is to explore the use of artificial EEG frames as replacements for some real ones by comparing classifiers trained with some artificial frames to classifiers trained with only real data. Results showed that, in some subjects, it is possible to replace up to 50% of frames with artificial data, which reduces training time from 720 to 360 seconds. In the remaining subjects, at least 12.5% of the real EEG frames could be replaced, reducing the training time by 90 seconds. Moreover, the method can be used to replace EEG frames that contain artifact, which reduces the impact of rejecting data with artifact. The method was also tested on an out of sample scenario with the best subjects from a public database, who yielded very good results using a frame collection with 87.5% artificial frames. These initial results with healthy users need to be further explored with patients’ data, along with research into alternative IMF mixing strategies and using other BCI paradigms.

Journal ArticleDOI
TL;DR: In this article, two kinds of deep learning schemes based on convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) were proposed for MI-classification.
Abstract: Motor imagery (MI) is an important control paradigm in the field of brain‐computer interface (BCI), which enables the recognition of personal intention. So far, numerous methods have been designed to classify EEG signal features for MI task. However, deep neural networks have been seldom applied to analyze EEG signals. In this study, two novel kinds of deep learning schemes based on convolutional neural networks (CNN) and Long Short‐Term Memory (LSTM) were proposed for MI‐classification. The frequency domain representations of EEG signals were obtained using short time Fourier transform (STFT) to train models. Classification results were compared between conventional algorithm, CNN, and LSTM models. Compared with two other methods, CNN algorithms had shown better performance. These conclusions verified that CNN method was promising for MI‐based BCIs.

Journal ArticleDOI
TL;DR: It is found that St‐NMES not only induced significantly larger desynchronization over sensorimotor areas (p<0.05) but also significantly enhanced MI brain connectivity patterns, and classification accuracy and stability were significantly higher with St‐ NMES.

Journal ArticleDOI
TL;DR: As corticospinal excitability was facilitated by the use of combined action observation and motor imagery, researchers should seek to establish the efficacy of implementing combined action observations and imagery interventions for improving motor skill performance and learning in applied sporting settings.

Journal ArticleDOI
TL;DR: Leveraging extension of the sense of ownership, agency, and self-location towards a non-body object has already been proven to help in producing stronger EEG correlates of MI, and these principles were used to facilitate the MI-BCI training process for the first time.

Journal ArticleDOI
TL;DR: This work develops a multiclass BCI decoding algorithm that uses electroencephalography source imaging, a technique that maps scalp potentials to cortical activations, to compensate for low spatial resolution of EEG.
Abstract: Brain-Computer Interface (BCI) is a rapidly developing technology that aims to support individuals suffering from various disabilities and, ultimately, improve everyday quality of life. Sensorimotor rhythm-based BCIs have demonstrated remarkable results in controlling virtual or physical external devices but they still face a number of challenges and limitations. Main challenges include multiple degrees-of-freedom control, accuracy, and robustness. In this work, we develop a multiclass BCI decoding algorithm that uses electroencephalography (EEG) source imaging, a technique that maps scalp potentials to cortical activations, to compensate for low spatial resolution of EEG. Spatial features were extracted using Common Spatial Pattern (CSP) filters in the cortical source space from a number of selected Regions of Interest (ROIs). Classification was performed through an ensemble model, based on individual ROI classification models. The evaluation was performed on the BCI Competition IV dataset 2a, which features 4 motor imagery classes from 9 participants. Our results revealed a mean accuracy increase of 5.6% with respect to the conventional application method of CSP on sensors. Neuroanatomical constraints and prior neurophysiological knowledge play an important role in developing source space-based BCI algorithms. Feature selection and classifier characteristics of our implementation will be explored to raise performance to current state-of-the-art.

Journal ArticleDOI
01 Feb 2018
TL;DR: BCIs, without the subject-and session-specific calibration and with lesser number of channels employed, play a vital role while promoting a generic and efficient framework for plug and play use.
Abstract: Inter-subject and inter-session variabilities pose a significant challenge in electroencephalogram (EEG)-based brain–computer interface (BCI) systems. Furthermore, high dimensional EEG montages introduce huge computational burden due to excessive number of channels involved. Two experimental, i.e., inter-session and inter-subject, variabilities of EEG dynamics during motor imagery (MI) tasks are investigated in this paper. In particular, the effect on the performance of the BCIs due to day-to-day variability in EEG dynamics during the alterations in cognitive stages is explored. In addition, the inter-subject BCIs feasibility between cortically synchronized and desynchronized subject pairs on pairwise performance associativity is further examined. Moreover, the consequences of integrating spatial brain dynamics of varying the number of channels - from specific regions of the brain - are also discussed in case of both the contexts. The proposed approach is validated on real BCI data set containing EEG data from four classes of MI tasks, i.e., left/right hand, both feet, and tongue, subjected prior to a preprocessing of three different spatial filtering techniques. Experimental results have shown that a maximum classification accuracy of around 58% was achieved for the inter-subject experimental case, whereas a 31% deviation was noticed in the classification accuracies across two sessions during the inter-session experimental case. In conclusion, BCIs, without the subject-and session-specific calibration and with lesser number of channels employed, play a vital role while promoting a generic and efficient framework for plug and play use.

Proceedings ArticleDOI
Jie Zhou1, Ming Meng1, Yunyuan Gao1, Yuliang Ma1, Qizhong Zhang1 
09 Jun 2018
TL;DR: A novel method based on wavelet envelope analysis and long-term short-term memory (LSTM) classifier which consider the amplitude modulation characteristics and time series information of MI-EEG to classify EEG signals into multiple classes is proposed.
Abstract: Motor imagery (MI) based brain-computer interface (BCI) facilitates a medium to translate the human motion intentions using Motor imagery electroencephalogram (EEG) into control signals. A major challenge in BCI research is the identification of non-stationary brain electrical signals to categorize human motion intentions. We propose a novel method based on wavelet envelope analysis and long-term short-term memory (LSTM) classifier which consider the amplitude modulation characteristics and time series information of MI-EEG to classify EEG signals into multiple classes. First, the Hilbert transform (HT) and discrete wavelet transform (DWT) are combined to extract significant features which contains the underlying information of both amplitude modulation and frequency modulation of the EEG signals. Then, the wavelet envelope features are input into an LSTM classifier with input gates, forget gates, and output gates for classification. Finally, the experiment was conducted on the 2003 BCI competition data set III with 5-fold cross-validation, and experimental results show that the proposed method helps achieve higher classification accuracy.

Journal ArticleDOI
TL;DR: The significant training effect obtained in shorter training time relative to previously proposed methods suggests the superiority of AOMI training and physiologically‐congruent proprioceptive feedback to enhance the MI‐ERD power.

Journal ArticleDOI
01 Jan 2018
TL;DR: The study showed that stroke patients can control a MI BCI system with high accuracy relative to healthy persons, and suggested that motor function could improve even if classification accuracy did not, and suggest other new questions to explore in future work.
Abstract: Motor imagery (MI) based brain-computer interfaces (BCI) extract commands in real-time and can be used to control a cursor, a robot or functional electrical stimulation (FES) devices. The control of FES devices is especially interesting for stroke rehabilitation, when a patient can use motor imagery to stimulate specific muscles in real-time. However, damage to motor areas resulting from stroke or other causes might impair control of a motor imagery BCI for rehabilitation. The current work presents a comparative evaluation of the MI-based BCI control accuracy between stroke patients and healthy subjects. Five patients who had a stroke that affected the motor system participated in the current study, and were trained across 10-24 sessions lasting about 1 h each with the recoveriX system. The participants' EEG data were classified while they imagined left or right hand movements, and real-time feedback was provided on a monitor. If the correct imagination was detected, the FES was also activated to move the left or right hand. The grand average mean accuracy was 87.4% for all patients and sessions. All patients were able to achieve at least one session with a maximum accuracy above 96%. Both the mean accuracy and the maximum accuracy were surprisingly high and above results seen with healthy controls in prior studies. Importantly, the study showed that stroke patients can control a MI BCI system with high accuracy relative to healthy persons. This may occur because these patients are highly motivated to participate in a study to improve their motor functions. Participants often reported early in the training of motor improvements and this caused additional motivation. However, it also reflects the efficacy of combining motor imagination, seeing continuous bar feedback, and real hand movement that also activates the tactile and proprioceptive systems. Results also suggested that motor function could improve even if classification accuracy did not, and suggest other new questions to explore in future work. Future studies will also be done with a first-person view 3D avatar to provide improved feedback and thereby increase each patients' sense of engagement.

Proceedings ArticleDOI
01 Oct 2018
TL;DR: This study analyzed the decoding of five different hand executions and imageries from EEG signals, for a robot hand control, using the common spatial patterns and the regularized linear discriminant analysis for the data analysis.
Abstract: The development of brain-computer interface (BCI) systems that are based on electroencephalography (EEG), and driven by spontaneous movement intentions, is useful for rehabilitation and external device control. In this study, we analyzed the decoding of five different hand executions and imageries from EEG signals, for a robot hand control. Five healthy subjects participated in this experiment. They executed and imagined five sustained hand motions. In this motor execution (ME) and motor imagery (MI) experiment, we proposed a subject-specific time interval selection method, and we used common spatial patterns (CSP) and the regularized linear discriminant analysis (RLDA) for the data analysis. As a result, we classified the five different hand motions offline and obtained average classification accuracies of 56.83% for ME, and 51.01% for MI, respectively. Both results were higher than the obtained accuracies from a comparison method that used a standard fixed time interval method. This result is encouraging, and the proposed method could potentially be used in future applications, such as a BCI-driven robot hand control.

Journal ArticleDOI
TL;DR: The data echo the importance of high kinesthetic vividness for MI training outcome and consider IPL as a key area during MI and through MI training.
Abstract: Motor imagery (MI) is the mental simulation of action frequently used by professionals in different fields. However, with respect to performance, well-controlled functional imaging studies on MI training are sparse. We investigated changes in fMRI representation going along with performance changes of a finger sequence (error and velocity) after MI training in 48 healthy young volunteers. Before training, we tested the vividness of kinesthetic and visual imagery. During tests, participants were instructed to move or to imagine moving the fingers of the right hand in a specific order. During MI training, participants repeatedly imagined the sequence for 15 min. Imaging analysis was performed using a full-factorial design to assess brain changes due to imagery training. We also used regression analyses to identify those who profited from training (performance outcome and gain) with initial imagery scores (vividness) and fMRI activation magnitude during MI at pre-test (MIpre ). After training, error rate decreased and velocity increased. We combined both parameters into a common performance index. FMRI activation in the left inferior parietal lobe (IPL) was associated with MI and increased over time. In addition, fMRI activation in the right IPL during MIpre was associated with high initial kinesthetic vividness. High kinesthetic imagery vividness predicted a high performance after training. In contrast, occipital activation, associated with visual imagery strategies, showed a negative predictive value for performance. Our data echo the importance of high kinesthetic vividness for MI training outcome and consider IPL as a key area during MI and through MI training.

Book ChapterDOI
TL;DR: New directions are proposed which include exploring the physical setting and conditions in which imagery occurs and investigating if short term impairments to the motor system detract from motor imagery ability and the potential application of motor imagery for recovery.
Abstract: Motor imagery has been central to adzvances in sport performance and rehabilitation. Neuroscience has provided techniques for measurement which have aided our understanding, conceptualization and theorizing. Challenges remain in the appropriate measurement of motor imagery. Motor imagery continues to provide an impetus for new findings relating to our emotional network, embodied cognition, inhibitory processes and action representation. New directions are proposed which include exploring the physical setting and conditions in which imagery occurs and investigating if short term impairments to the motor system detract from motor imagery ability and the potential application of motor imagery for recovery.

Journal ArticleDOI
TL;DR: The findings suggest that in healthy individuals with higher motor imagery ability from a first-person perspective, KMI efficiently engages the shared cortical circuits corresponding with motor execution, including the sensorimotor cortex, with high compliance.
Abstract: In the field of psychology, it has been well established that there are two types of motor imagery such as kinesthetic motor imagery (KMI) and visual motor imagery (VMI), and the subjective evaluation for vividness of motor imagery each differs across individuals. This study aimed to examine how the motor imagery ability assessed by the psychological scores is associated with the physiological measure using electroencephalogram (EEG) sensorimotor rhythm during KMI task. First, 20 healthy young individuals evaluated subjectively how vividly they can perform each of KMI and VMI by using the Kinesthetic and Visual Imagery Questionnaire (KVIQ). We assessed their motor imagery abilities by summing each of KMI and VMI scores in KVIQ (KMItotal and VMItotal). Second, in physiological experiments, they repeated two strengths (10 and 40% of maximal effort) of isometric voluntary wrist-dorsiflexion. Right after each contraction, they also performed its KMI. The scalp EEGs over the sensorimotor cortex were recorded during the tasks. The EEG power is known to decrease in the alpha-and-beta band (7-35 Hz) from resting state to performing state of voluntary contraction (VC) or motor imagery. This phenomenon is referred to as event-related desynchronization (ERD). For each strength of the tasks, we calculated the maximal peak of ERD during VC, and that during its KMI, and measured the degree of similarity (ERDsim) between them. The results showed significant negative correlations between KMItotal and ERDsim for both strengths (p < 0.05) (i.e., the higher the KMItotal, the smaller the ERDsim). These findings suggest that in healthy individuals with higher motor imagery ability from a first-person perspective, KMI efficiently engages the shared cortical circuits corresponding with motor execution, including the sensorimotor cortex, with high compliance.

Journal ArticleDOI
TL;DR: A novel decoding scheme based on the combination of Lomb-Scargle periodogram (LSP) and deep belief network (DBN) was proposed to recognize the incomplete motor imagery EEG and the results demonstrate that the LSP can estimate relative robust PSD features and the proposed scheme can significantly improve the decoding performance.
Abstract: High accuracy decoding of electroencephalogram (EEG) signal is still a major challenge that can hardly be solved in the design of an effective motor imagery-based brain-computer interface (BCI), especially when the signal contains various extreme artifacts and outliers arose from data loss. The conventional process to avoid such cases is to directly reject the entire severely contaminated EEG segments, which leads to a drawback that the BCI has no decoding results during that certain period. In this study, a novel decoding scheme based on the combination of Lomb-Scargle periodogram (LSP) and deep belief network (DBN) was proposed to recognize the incomplete motor imagery EEG. Particularly, instead of discarding the entire segment, two forms of data removal were adopted to eliminate the EEG portions with extreme artifacts and data loss. The LSP was utilized to steadily extract the power spectral density (PSD) features from the incomplete EEG constructed by the remaining portions. A DBN structure based on the restricted Boltzmann machine (RBM) was exploited and optimized to perform the classification task. Various comparative experiments were conducted and evaluated on simulated signal and real incomplete motor imagery EEG, including the comparison of three PSD extraction methods (fast Fourier transform, Welch and LSP) and two classifiers (DBN and support vector machine, SVM). The results demonstrate that the LSP can estimate relative robust PSD features and the proposed scheme can significantly improve the decoding performance for the incomplete motor imagery EEG. This scheme can provide an alternative decoding solution for the motor imagery EEG contaminated by extreme artifacts and data loss. It can be beneficial to promote the stability, smoothness and maintain consecutive outputs without interruption for a BCI system that is suitable for the online and long-term application.

Journal ArticleDOI
TL;DR: The general aim of this investigative research is to develop a brain-computer based interface for the movement imagination of the left fist, right fist, both fists and both feet in order to control an intelligent wheelchair.
Abstract: The activities pertaining to body control performed by human beings utilize neuromuscular tracts Tasks' performance such as moving the arms or walking demand the planning of the task to be performed People diagnosed with clinical conditions such as Amyotrophic Lateral Sclerosis, Spine lesions or Cerebrovascular Accident, for instance, have their neuromuscular tracts damaged One of the alternatives to bypass that problem is the development of technologies which can partially replace the loss functioning of people with severe motor impairment The imagination of the movement is considered as a cognitive state which corresponds to the mental simulation of a given motor action The general aim of this investigative research is to develop a brain-computer based interface for the movement imagination of the left fist, right fist, both fists and both feet in order to control an intelligent wheelchair The electroencephalography signals were acquired through the database eegmmidb - EEG Motor Movement/Imagery Dataset Electroencephalography signals samples of 106 individuals were utilized in order to validate the computational model The proposed model obtained an efficiency of 74,96% in the correct classification of the events related to movement imagination The developed techniques are promising The model intends to contribute as a complementation of an improvement towards the mobility of people suffering from severe motor impairment

Journal ArticleDOI
TL;DR: The results strongly support the hypothesis that noninvasive EEG-based BCI can provide robust 3-D control through endogenous neural modulation in broader populations with limited training.
Abstract: Objective: While noninvasive electroenceph-alography (EEG) based brain-computer interfacing (BCI) has been successfully demonstrated in two-dimensional (2-D) control tasks, little work has been published regarding its extension to practical three-dimensional (3-D) control. Methods: In this study, we developed a new BCI approach for 3-D control by combining a novel form of endogenous visuospatial attentional modulation, defined as overt spatial attention (OSA), and motor imagery (MI). Results: OSA modulation was shown to provide comparable control to conventional MI modulation in both 1-D and 2-D tasks. Furthermore, this paper provides evidence for the functional independence of traditional MI and OSA, as well as an investigation into the simultaneous use of both. Using this newly proposed BCI paradigm, 16 participants successfully completed a 3-D eight-target control task. Nine of these subjects further demonstrated robust 3-D control in a 12-target task, significantly outperforming the information transfer rate achieved in the 1-D and 2-D control tasks (29.7 ± 1.6 b/min). Conclusion: These results strongly support the hypothesis that noninvasive EEG-based BCI can provide robust 3-D control through endogenous neural modulation in broader populations with limited training. Significance: Through the combination of the two strategies (MI and OSA), a substantial portion of the recruited subjects were capable of robustly controlling a virtual cursor in 3-D space. The proposed novel approach could broaden the dimensionality of BCI control and shorten the training time.

Journal ArticleDOI
TL;DR: It is suggested that increased neural efficiency after training in mental rotation of hands manifests as a decrease in visual imagery in conjunction with increased recruitment of motor‐related regions, consistent with the obtained behavioral effects depicting motor imagery mediating expertise in mental rotate of hands.