Left Hand Motor Imagery Research Articles

Time–Frequency Cross Mutual Information Analysis of the Brain Functional Networks Underlying Multiclass Motor Imagery

ABSTRACTTo study the physiologic mechanism of the brain during different motor imagery (MI) tasks, the authors employed a method of brain-network modeling based on time–frequency cross mutual information obtained from 4-class (left hand, right hand, feet, and tongue) MI tasks recorded as brain–computer interface (BCI) electroencephalography data. The authors explored the brain network revealed by these MI tasks using statistical analysis and the analysis of topologic characteristics, and observed significant differences in the reaction level, reaction time, and activated target during 4-class MI tasks. There was a great difference in the reaction level between the execution and resting states during different tasks: the reaction level of the left-hand MI task was the greatest, followed by that of the right-hand, feet, and tongue MI tasks. The reaction time required to perform the tasks also differed: during the left-hand and right-hand MI tasks, the brain networks of subjects reacted promptly and strongly, but there was a delay during the feet and tongue MI task. Statistical analysis and the analysis of network topology revealed the target regions of the brain network during different MI processes. In conclusion, our findings suggest a new way to explain the neural mechanism behind MI.

The impact of goal-oriented task design on neurofeedback learning for brain-computer interface control.

Neurofeedback training teaches individuals to modulate brain activity by providing real-time feedback and can be used for brain-computer interface control. The present study aimed to optimize training by maximizing engagement through goal-oriented task design. Participants were shown either a visual display or a robot, where each was manipulated using motor imagery (MI)-related electroencephalography signals. Those with the robot were instructed to quickly navigate grid spaces, as the potential for goal-oriented design to strengthen learning was central to our investigation. Both groups were hypothesized to show increased magnitude of these signals across 10 sessions, with the greatest gains being seen in those navigating the robot due to increased engagement. Participants demonstrated the predicted increase in magnitude, with no differentiation between hemispheres. Participants navigating the robot showed stronger left-hand MI increases than those with the computer display. This is likely due to success being reliant on maintaining strong MI-related signals. While older participants showed stronger signals in early sessions, this trend later reversed, suggesting greater natural proficiency but reduced flexibility. These results demonstrate capacity for modulating neurofeedback using MI over a series of training sessions, using tasks of varied design. Importantly, the more goal-oriented robot control task resulted in greater improvements.

A Stimulus-Independent Hybrid BCI Based on Motor Imagery and Somatosensory Attentional Orientation.

Distinctive EEG signals from the motor and somatosensory cortex are generated during mental tasks of motor imagery (MI) and somatosensory attentional orientation (SAO). In this paper, we hypothesize that a combination of these two signal modalities provides improvements in a brain-computer interface (BCI) performance with respect to using the two methods separately, and generate novel types of multi-class BCI systems. Thirty two subjects were randomly divided into a Control-Group and a Hybrid-Group. In the Control-Group, the subjects performed left and right hand motor imagery (i.e., L-MI and R-MI). In the Hybrid-Group, the subjects performed the four mental tasks (i.e., L-MI, R-MI, L-SAO, and R-SAO). The results indicate that combining two of the tasks in a hybrid manner (such as L-SAO and R-MI) resulted in a significantly greater classification accuracy than when using two MI tasks. The hybrid modality reached 86.1% classification accuracy on average, with a 7.70% increase with respect to MI ( ), and 7.21% to SAO ( ) alone. Moreover, all 16 subjects in the hybrid modality reached at least 70% accuracy, which is considered the threshold for BCI illiteracy. In addition to the two-class results, the classification accuracy was 68.1% and 54.1% for the three-class and four-class hybrid BCI. Combining the induced brain signals from motor and somatosensory cortex, the proposed stimulus-independent hybrid BCI has shown improved performance with respect to individual modalities, reducing the portion of BCI-illiterate subjects, and provided novel types of multi-class BCIs.

Classification of motor imagery EEG signals using SVM, k-NN and ANN

Presently, the brain mechanisms are still far from being fully understood, and a considerable amount of neuroscience research is still required to achieve this goal. Brain signals once decoded can be used to control devices and help people in locked-in state to live a better life. The present investigation deals with the processing and classification of left hand motor imagery and foot motor imagery EEG based brain signals. These signals have a very high dimensionality which possess problem for classifiers. In the present investigation, dimensionality reduction methods PCA and LDA have been implemented and state vector machine, k-nearest neighbour and artificial neural network (ANN) classifiers have been compared for their accuracy and speed of classification. It has been concluded that the combination of LDA and ANN can be treated as a strong candidate for processing and classification of Motor Imagery EEG based brain signals.

EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine.

Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of motion intention from a common resting state. In this paper, we sought a novel approach for multi-class classification in BCI applications. We collected electroencephalographic (EEG) signals registered by electrodes placed over the scalp during left hand motor imagery, right hand motor imagery, and resting state for ten healthy human subjects. We proposed using the Kolmogorov complexity (Kc) for feature extraction and a multi-class Adaboost classifier with extreme learning machine as base classifier for classification, in order to classify the three-class EEG samples. An average classification accuracy of 79.5% was obtained for ten subjects, which greatly outperformed commonly used approaches. Thus, it is concluded that the proposed method could improve the performance for classification of motor imagery tasks for multi-class samples. It could be applied in further studies to generate the control commands to initiate the movement of a robotic exoskeleton or orthosis, which finally facilitates the rehabilitation of disabled people.

Neuroanatomical correlates of brain–computer interface performance

Brain–computer interfaces (BCIs) offer a potential means to replace or restore lost motor function. However, BCI performance varies considerably between users, the reasons for which are poorly understood. Here we investigated the relationship between sensorimotor rhythm (SMR)-based BCI performance and brain structure. Participants were instructed to control a computer cursor using right- and left-hand motor imagery, which primarily modulated their left- and right-hemispheric SMR powers, respectively. Although most participants were able to control the BCI with success rates significantly above chance level even at the first encounter, they also showed substantial inter-individual variability in BCI success rate. Participants also underwent T1-weighted three-dimensional structural magnetic resonance imaging (MRI). The MRI data were subjected to voxel-based morphometry using BCI success rate as an independent variable. We found that BCI performance correlated with gray matter volume of the supplementary motor area, supplementary somatosensory area, and dorsal premotor cortex. We suggest that SMR-based BCI performance is associated with development of non-primary somatosensory and motor areas. Advancing our understanding of BCI performance in relation to its neuroanatomical correlates may lead to better customization of BCIs based on individual brain structure.

Decoding Answers to Four-Choice Questions Using Functional near Infrared Spectroscopy

This paper presents a functional near infrared (NIR) spectroscopy-based paradigm that can be used to decode answers to four-choice questions. Ten healthy subjects were asked to perform one of the four different brain activities, that is, right-hand motor imagery (RMI), left-hand motor imagery (LMI), mental arithmetic (MA) and mental counting (MC), to answer the given four-choice questions. In selecting the A, B, C or D choices, the subjects were asked to perform RMI, LMI, MA or MC, respectively. Signals from the primary motor and prefrontal cortices were acquired simultaneously using a continuous-wave functional NIR spectroscopy system. The four activities were classified using multiclass linear discriminant analysis to decode the answers to an average accuracy of 73.3% across the 10 subjects. The results demonstrate the potential of functional NIR spectroscopy to decode answers to four-choice questions using four different intentionally generated brain activities as control signals.

Classification of prefrontal and motor cortex signals for three-class fNIRS–BCI

Functional near-infrared spectroscopy (fNIRS) is an optical imaging method that can be used for a brain-computer interface (BCI). In the present study, we concurrently measure and discriminate fNIRS signals evoked by three different mental activities, that is, mental arithmetic (MA), right-hand motor imagery (RI), and left-hand motor imagery (LI). Ten healthy subjects were asked to perform the MA, RI, and LI during a 10s task period. Using a continuous-wave NIRS system, signals were acquired concurrently from the prefrontal and the primary motor cortices. Multiclass linear discriminant analysis was utilized to classify MA vs. RI vs. LI with an average classification accuracy of 75.6% across the ten subjects, for a 2–7s time window during the a 10s task period. These results demonstrate the feasibility of implementing a three-class fNIRS-BCI using three different intentionally-generated cognitive tasks as inputs.

Quantification of the power changes in BOLD signals using Welch spectrum method during different single-hand motor imageries

Motor imagery is an experimental paradigm implemented in cognitive neuroscience and cognitive psychology. To investigate the asymmetry of the strength of cortical functional activity due to different single-hand motor imageries, functional magnetic resonance imaging (fMRI) data from right handed normal subjects were recorded and analyzed during both left-hand and right-hand motor imagery processes. Then the average power of blood oxygenation level-dependent (BOLD) signals in temporal domain was calculated using the developed tool that combines Welch power spectrum and the integral of power spectrum approach of BOLD signal changes during motor imagery. Power change analysis results indicated that cortical activity exhibited a stronger power in the precentral gyrus and medial frontal gyrus with left-hand motor imagery tasks compared with that from right-hand motor imagery tasks. These observations suggest that right handed normal subjects mobilize more cortical nerve cells for left-hand motor imagery. Our findings also suggest that the approach based on power differences of BOLD signals is a suitable quantitative analysis tool for quantification of asymmetry of brain activity intensity during motor imagery tasks.

Analysis of Motor Imagery EEG Based on Hilbert-Huang Transform

Based on Hilbert-Huang Transform (HHT) theory, we present a method to analyze the electroencephalogram (EEG) signal of right and left hand motor imagery. Firstly, EMD method decomposed EEG signal into a group of intrinsic mode functions (IMFs). The first three IMFs were extracted to denoise. We adopt endpoint Mirror Extension method to relieve the influence on subsequent processing brought by endpoint effect. According to the Hilbert transform, we can obtain the time-frequency distribution. The energy of the first three components is selected as the input of SVM. The results show that EMD is an efficient method to analyze the EEG signal. The proposed method obtains an ideal recognition rate.

Time-frequency optimization for discrimination between imagination of right and left hand movements based on two bipolar electroencephalography channels

AbstractTo enforce a widespread use of efficient and easy to use brain-computer interfaces (BCIs), the inter-subject robustness should be increased and the number of electrodes should be reduced. These two key issues are addressed in this contribution, proposing a novel method to identify subject-specific time-frequency characteristics with a minimal number of electrodes. In this method, two alternative criteria, time-frequency discrimination factor (TFDF) andFscore, are proposed to evaluate the discriminative power of time-frequency regions. Distinct from classical measures (e.g., Fisher criterion,r2coefficient), the TFDF is based on the neurophysiologic phenomena, on which the motor imagery BCI paradigm relies, rather than only from statistics.Fscore is based on the popularFisher’sdiscriminant and purely data driven; however, it differs from traditional measures since it provides a simple and effective measure for quantifying the discriminative power of a multi-dimensional feature vector. The proposed method is tested on BCI competition IV datasets IIa and IIb for discriminating right and left hand motor imagery. Compared to state-of-the-art methods, our method based on both criteria led to comparable or even better classification results, while using fewer electrodes (i.e., only two bipolar channels, C3 and C4). This work indicates that time-frequency optimization can not only improve the classification performance but also contribute to reducing the number of electrodes required in motor imagery BCIs.

Combining Motor Imagery With Selective Sensation Toward a Hybrid-Modality BCI

A hybrid modality brain-computer interface (BCI) is proposed in this paper, which combines motor imagery with selective sensation to enhance the discrimination between left and right mental tasks, e.g., the classification between left/ right stimulation sensation and right/ left motor imagery. In this paradigm, wearable vibrotactile rings are used to stimulate both the skin on both wrists. Subjects are required to perform the mental tasks according to the randomly presented cues (i.e., left hand motor imagery, right hand motor imagery, left stimulation sensation or right stimulation sensation). Two-way ANOVA statistical analysis showed a significant group effect (F (2,20) = 7.17, p = 0.0045), and the Benferroni-corrected multiple comparison test (with α = 0.05) showed that the hybrid modality group is 11.13% higher on average than the motor imagery group, and 10.45% higher than the selective sensation group. The hybrid modality experiment exhibits potentially wider spread usage within ten subjects crossed 70% accuracy, followed by four subjects in motor imagery and five subjects in selective sensation. Six subjects showed statistically significant improvement ( Benferroni-corrected) in hybrid modality in comparison with both motor imagery and selective sensation. Furthermore, among subjects having difficulties in both motor imagery and selective sensation, the hybrid modality improves their performance to 90% accuracy. The proposed hybrid modality BCI has demonstrated clear benefits for those poorly performing BCI users. Not only does the requirement of motor and sensory anticipation in this hybrid modality provide basic function of BCI for communication and control, it also has the potential for enhancing the rehabilitation during motor recovery.

Envelope Extraction Algorithms and the Application in Motor Imagery

The envelope information is an important feature in EEG signal processing. This paper analyses the application of Hilbert transform and sliding window Infomax algorithm in envelope detection of EEG signals from different respects, and compares the classification results in left and right hand motor imagery. From the results, we see that, the signal envelope extracted by Hilbert transform is more accurate than sliding window Infomax algorithm, but the latter is superior in signal denoising, real-time ability and the richness of characteristics, sliding window Infomax algorithm has greater application potential.

An EEG Based Control System for Intelligent Wheelchair

This paper presents a brain computer interface to control an intelligent wheelchair based on EEG signals. EEG signals are collected and analysed by using Emotiv. After signal processing, the events about motor imagery are generated and the commands are designed and transmitted to intelligent wheelchair. Finally, the system realizes the motion control of the intelligent wheelchair through subject's motor imagery of left hand, right hand and legs. Besides, the events about motor imagery are expressed in the form of virtual movement as the feedback of system. The Experiment results show that the control system is feasible and has better stability. It establishes a basis of practical application for EEG control intelligent wheelchair.

Pre-Stimulus Sensorimotor Rhythms Influence Brain–Computer Interface Classification Performance

The influence of pre-stimulus ongoing brain activity on post-stimulus task performance has recently been analyzed in several studies. While pre-stimulus activity in the parieto-occipital area has been exhaustively investigated with congruent results, less is known about the sensorimotor areas, for which studies reported inconsistent findings. In this work, the topic is addressed in a brain-computer interface (BCI) setting based on modulations of sensorimotor rhythms (SMR). The goal is to assess whether and how pre-stimulus SMR activity influences the successive task execution quality and consequently the classification performance. Grand average data of 23 participants performing right and left hand motor imagery were analyzed. Trials were separated into two groups depending on the SMR amplitude in the 1000 ms interval preceding the cue, and classification by common spatial patterns (CSPs) preprocessing and linear discriminant analysis (LDA) was carried out in the post-stimulus time interval, i.e., during the task execution. The correlation between trial group and classification performance was assessed by an analysis of variance. As a result of this analysis, trials with higher SMR amplitude in the 1000 ms interval preceding the cue yielded significantly better classification performance than trials with lower amplitude. A further investigation of brain activity patterns revealed that this increase in accuracy is mainly due to the persistence of a higher SMR amplitude over the ipsilateral hemisphere. Our findings support the idea that exploiting information about the ongoing SMR might be the key to boosting performance in future SMR-BCI experiments and motor related tasks in general.

Improved Classification Methods for Brain Computer Interface System

Brain computer interface (BCI) aims at providing a new communication way without brain's normal output through nerve and muscle. The electroencephalography (EEG) has been widely used for BCI system because it is a non-invasive approach. For the EEG signals of left and right hand motor imagery, the event-related desynchronization (ERD) and event-related synchronization (ERS) are used as classification features in this paper. The raw data are transformed by nonlinear methods and classified by Fisher classifier. Compared with the linear methods, the classification accuracy can get an obvious increase to 86.25%. Two different nonlinear transform were arised and one of them is under the consideration of the relativity of two channels of EEG signals. With these nonlinear transform, the performance are also stable with the balance of two misclassifications.

EEG Feature Extraction and Pattern Classification Based on Motor Imagery in Brain-Computer Interface

Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet transform method was used to decompose the average power of C3 electrode and C4 electrode in left-right hands imagery movement during some periods of time. The reconstructed signal of approximation coefficient A6 on the sixth level was selected to build up a feature signal. Secondly, the performances by Fisher Linear Discriminant Analysis with two different threshold calculation ways and Support Vector Machine methods were compared. The final classification results showed that false classification rate by Support Vector Machine was lower and gained an ideal classification results.

Evaluation of the effective connectivity of supplementary motor areas during motor imagery using Granger causality mapping

Brain activation during motor imagery has been studied extensively for years, but only a few of these studies focused on investigating the effective connectivity in the brain. The existence of interactions or closed loop circuits between the SMA and other brain regions during motor imagery still remains unclear. In the present study, selecting the SMA as the region of interest, we used the Granger causality mapping (GCM) method to explore the effective connectivity in the brain during motor imagery. Our results demonstrated that more brain regions showed effective connections to the SMA during the right-hand motor imagery than during the left-hand motor imagery, but the strength of the casual influence during the left-hand motor imagery was stronger than that of the right-hand motor imagery. We further found forward and backward effective connectivity between the SMA and three regions, including the bilateral dorsal premotor area (PMd), the contralateral primary and secondary somatosensory cortex (S1), and the primary motor cortex (M1). these results might indicate how the brain regions were inter-activated during motor imagery.

Automated classification of fMRI data employing trial-based imagery tasks

Automated interpretation and classification of functional MRI (fMRI) data is an emerging research field that enables the characterization of underlying cognitive processes with minimal human intervention. In this work, we present a method for the automated classification of human thoughts reflected on a trial-based paradigm using fMRI with a significantly shortened data acquisition time (less than one minute). Based on our preliminary experience with various cognitive imagery tasks, six characteristic thoughts were chosen as target tasks for the present work: right-hand motor imagery, left-hand motor imagery, right foot motor imagery, mental calculation, internal speech/word generation, and visual imagery. These six tasks were performed by five healthy volunteers and functional images were obtained using a T(*)(2)-weighted echo planar imaging (EPI) sequence. Feature vectors from activation maps, necessary for the classification of neural activity, were automatically extracted from the regions that were consistently and exclusively activated for a given task during the training process. Extracted feature vectors were classified using the support vector machine (SVM) algorithm. Parameter optimization, using a k-fold cross validation scheme, allowed the successful recognition of the six different categories of administered thought tasks with an accuracy of 74.5% (mean)+/-14.3% (standard deviation) across all five subjects. Our proposed study for the automated classification of fMRI data may be utilized in further investigations to monitor/identify human thought processes and their potential link to hardware/computer control.

Motor imagery and action observation: Modulation of sensorimotor brain rhythms during mental control of a brain–computer interface

This study investigates the impact of a continuously presented visual feedback in the form of a grasping hand on the modulation of sensorimotor EEG rhythms during online control of a brain-computer interface (BCI). Two groups of participants were trained to use left or right hand motor imagery to control a specific output signal on a computer monitor: the experimental group controlled a moving hand performing an object-related grasp ('realistic feedback'), whereas the control group controlled a moving bar ('abstract feedback'). Continuous feedback was realized by using the outcome of a real-time classifier which was based on EEG signals recorded from left and right central sites. The classification results show no difference between the two feedback groups. For both groups, ERD/ERS analysis revealed a significant larger ERD during feedback presentation compared to an initial motor imagery screening session without feedback. Increased ERD during online BCI control was particularly found for the lower alpha (8-10 Hz) and for the beta bands (16-20, 20-24 Hz). The present study demonstrates that visual BCI feedback clearly modulates sensorimotor EEG rhythms. When the feedback provides equivalent information on both the continuous and final outcomes of mental actions, the presentation form (abstract versus realistic) does not influence the performance in a BCI, at least in initial training sessions. The present results are of practical interest for classifier development and BCI use in the field of motor restoration.