Weibo Yi

EEG feature comparison and classification of simple and compound limb motor imagery

BackgroundMotor imagery can elicit brain oscillations in Rolandic mu rhythm and central beta rhythm, both originating in the sensorimotor cortex. In contrast with simple limb motor imagery, less work was reported about compound limb motor imagery which involves several parts of limbs. The goal of this study was to investigate the differences of the EEG patterns between simple limb motor imagery and compound limb motor imagery, and discuss the separability of multiple types of mental tasks.MethodsTen subjects participated in the experiment involving three tasks of simple limb motor imagery (left hand, right hand, feet), three tasks of compound limb motor imagery (both hands, left hand combined with right foot, right hand combined with left foot) and rest state. Event-related spectral perturbation (ERSP), power spectral entropy (PSE) and spatial distribution coefficient were adopted to analyze these seven EEG patterns. Then three algorithms of modified multi-class common spatial patterns (CSP) were used for feature extraction and classification was implemented by support vector machine (SVM).ResultsThe induced event-related desynchronization (ERD) affects more components within both alpha and beta bands resulting in more broad ERD bands at electrode positions C3, Cz and C4 during left/right hand combined with contralateral foot imagery, whose PSE values are significant higher than that of simple limb motor imagery. From the topographical distribution, simultaneous imagination of upper limb and contralateral lower limb certainly contributes to the activation of more areas on cerebral cortex. Classification result shows that multi-class stationary Tikhonov regularized CSP (Multi-sTRCSP) outperforms other two multi-class CSP methods, with the highest accuracy of 84% and mean accuracy of 70%.ConclusionsThe work implies that there exist the separable differences between simple limb motor imagery and compound limb motor imagery, which can be utilized to build a multimodal classification paradigm in motor imagery based brain-computer interface (BCI) systems.

Event-Related Beta EEG Changes During Active, Passive Movement and Functional Electrical Stimulation of the Lower Limb

A number of electroencephalographic (EEG) studies have reported on event-related desynchronization/synchronization (ERD/ERS) during active movements, passive movements, and the movements induced by functional electrical stimulation (FES). However, the quantitative differences in ERD values and affected frequency bands associated with the lower limb have not been discussed. The goal of this paper was to quantitatively compare the ERD patterns during active movement, passive movement and FES-induced movement of the lower limb. 64-channel EEG signals were recorded to investigate the brain oscillatory patterns during active movement, passive movement and FES-induced movement of the lower limb in twelve healthy subjects. And passive movement and FES-induced movement were also performed in a hemiplegic stroke patient. For healthy subjects, FES-induced movement presented significantly higher characteristic frequency of central beta ERD while there was no significant difference in ERD values compared with active or passive movement. Meanwhile, beta ERD values of FES-induced movement were significantly correlated with those of active movement, and spatial distribution of beta ERD pattern for FES-induced movement was more correlated with that for active movement. In addition, the stroke patient presented central ERD patterns during FES-induced movement, while no ERD with similar frequencies could be found during passive movement. This work implies that the EEG oscillatory pattern under FES-induced movement tends more towards active movement instead of passive movement. The quantification of ERD patterns could be expected as a potential technique to evaluate the brain response during FES-induced movement.

Evaluation of EEG Oscillatory Patterns and Cognitive Process during Simple and Compound Limb Motor Imagery

Motor imagery (MI), sharing similar neural representations to motor execution, is regarded as a window to investigate the cognitive motor processes. However, in comparison to simple limb motor imagery, significantly less work has been reported on brain oscillatory patterns induced by compound limb motor imagery which involves several parts of limbs. This study aims to investigate differences of the electroencephalogram (EEG) patterns as well as cognitive process between simple limb motor imagery and compound limb motor imagery. Ten subjects participated in the experiment involving three tasks of simple limb motor imagery (left hand, right hand, feet) and three tasks of compound limb motor imagery (both hands, left hand combined with right foot, right hand combined with left foot). Simultaneous imagination of different limbs contributes to the activation of larger cortical areas as well as two estimated sources located at corresponding motor areas within beta rhythm. Compared with simple limb motor imagery, compound limb motor imagery presents a network with more effective interactions overlying larger brain regions, additionally shows significantly larger causal flow over sensorimotor areas and larger causal density over both sensorimotor areas and neighboring regions. On the other hand, compound limb motor imagery also shows significantly larger 10–11 Hz alpha desynchronization at occipital areas and central theta synchronization. Furthermore, the phase-locking value (PLV) between central and occipital areas of left/right hand combined with contralateral foot imagery is significantly larger than that of simple limb motor imagery. All these findings imply that there exist apparent intrinsic distinctions of neural mechanism between simple and compound limb motor imagery, which presents a more complex effective connectivity network and may involve a more complex cognitive process during information processing.

Enhancing performance of a motor imagery based brain–computer interface by incorporating electrical stimulation-induced SSSEP

OBJECTIVE We proposed a novel simultaneous hybrid brain-computer interface (BCI) by incorporating electrical stimulation into a motor imagery (MI) based BCI system. The goal of this study was to enhance the overall performance of an MI-based BCI. In addition, the brain oscillatory pattern in the hybrid task was also investigated. APPROACH 64-channel electroencephalographic (EEG) data were recorded during MI, selective attention (SA) and hybrid tasks in fourteen healthy subjects. In the hybrid task, subjects performed MI with electrical stimulation which was applied to bilateral median nerve on wrists simultaneously. MAIN RESULTS The hybrid task clearly presented additional steady-state somatosensory evoked potential (SSSEP) induced by electrical stimulation with MI-induced event-related desynchronization (ERD). By combining ERD and SSSEP features, the performance in the hybrid task was significantly better than in both MI and SA tasks, achieving a ~14% improvement in total relative to the MI task alone and reaching ~89% in mean classification accuracy. On the contrary, there was no significant enhancement obtained in performance while separate ERD feature was utilized in the hybrid task. In terms of the hybrid task, the performance using combined feature was significantly better than using separate ERD or SSSEP feature. SIGNIFICANCE The results in this work validate the feasibility of our proposed approach to form a novel MI-SSSEP hybrid BCI outperforming a conventional MI-based BCI through combing MI with electrical stimulation.

Evaluation and comparison of effective connectivity during simple and compound limb motor imagery

Motor imagery (MI) has been demonstrated beneficial in motor rehabilitation in patients with movement disorders. In contrast with simple limb motor imagery, less work was reported about the effective connectivity networks of compound limb motor imagery which involves several parts of limbs. This work aimed to investigate the differences of information flow patterns between simple limb motor imagery and compound limb motor imagery. Ten subjects participated in the experiment involving three tasks of simple limb motor imagery (left hand, right hand, feet) and three tasks of compound limb motor imagery (both hands, left hand combined with right foot, right hand combined with left foot). The causal interactions among different neural regions were evaluated by Short-time Directed Transfer Function (SDTF). Quite different from the networks of simple limb motor imagery, more effective interactions overlying larger brain regions were observed during compound limb motor imagery. These results imply that there exist significant differences in the patterns of EEG activity flow between simple limb motor imagery and compound limb motor imagery, which present more complex networks and could be utilized in motor rehabilitation for more benefit in patients with movement disorders.

Cortical excitability effects of stimulation intensity change speed during NMES

Rehabilitation method of motor dysfunction is a challenging issue of neural rehabilitation. Neuromuscular electrical stimulation (NMES) has been frequently used in rehabilitation therapy to improve neural recovery such as stroke and spinal cord injury. Stimulus, acting on sensorimotor neural system components, resulted in the increased cortical excitability which accompanied with motor performance improvement. Stimulus information conveyed by sensory system included below four elementary attributes: modality, location, intensity, and timing. But, few works has been reported about effect of the stimulation intensity change speed (SICS). In this paper, we studied the effects of SICS by event-related desynchronization (ERD) or event-related synchronization (ERS) and EEG source analysis by exact low resolution brain electric tomography (eLORETA). The results suggested that brain function areas were sensitive to SICS. Using fast SICS could evoked more significant cortical excitability than the slow one. We demonstrated the availability of an efficient NMES method, additionally implied the rehabilitation potential of cortical excitability enhancement in sensorimotor cortex for motor dysfunction.Rehabilitation method of motor dysfunction is a challenging issue of neural rehabilitation. Neuromuscular electrical stimulation (NMES) has been frequently used in rehabilitation therapy to improve neural recovery such as stroke and spinal cord injury. Stimulus, acting on sensorimotor neural system components, resulted in the increased cortical excitability which accompanied with motor performance improvement. Stimulus information conveyed by sensory system included below four elementary attributes: modality, location, intensity, and timing. But, few works has been reported about effect of the stimulation intensity change speed (SICS). In this paper, we studied the effects of SICS by event-related desynchronization (ERD) or event-related synchronization (ERS) and EEG source analysis by exact low resolution brain electric tomography (eLORETA). The results suggested that brain function areas were sensitive to SICS. Using fast SICS could evoked more significant cortical excitability than the slow one. We demonstrated the availability of an efficient NMES method, additionally implied the rehabilitation potential of cortical excitability enhancement in sensorimotor cortex for motor dysfunction.

Research on multi-dimensional N-back task induced EEG variations

In order to test the effectiveness of multi-dimensional N-back task for inducing deeper brain fatigue, we conducted a series of N*L-back experiments: 1*1-back, 1*2-back, 2*1-back and 2*2-back tasks. We analyzed and compared the behavioral results, EEG variations and mutual information among these four different tasks. There was no significant difference in average EEG power and power spectrum entropy (PSE) among the tasks. However, the behavioral result of N*2-back task showed significant difference compared to traditional one dimensional N-back task. Connectivity changes were observed with the addition of one more matching task in N-back. We suggest that multi-dimensional N-back task consume more brain resources and activate different brain areas. These results provide a basis for multi-dimensional N-back tasks that can be used to induce deeper mental fatigue or exert more workload.

EMD-based Feature Extraction from Motor Imaginary EEG of Complex Movements

In this study, we proposed and evaluated the use of the empirical mode decomposition (EMD) technique to extract feature information of the event-related (de) synchronization (ERD/ERS) phenomenon during complex motor imagination of combined body and limb action. The EEG data were separated into intrinsic mode functions (IMFs) using the EMD method and determined the characteristic IMFs by power spectral density (PSD) analysis. Thereafter, the analytic signals of the characteristic IMFs can be obtained by the Hilbert transformation, then extracting the ERD/ERS feature of each single-trial. To verify the effectiveness of this method, ten subjects were tested for distinguishing three kinds of complex motor imagery. The classification performance suggests that the proposed EMD based approach is effective for ERD/ERS feature extraction and is worth for the further application in a brain-computer interface.

Super resolution reconstruction based on total variation regularization

Super resolution reconstruction is an important branch of image processing that extracting high resolution images containing more details from an image sequence of low resolution, by image processing such as motion estimation, de-blurring and de-noising. Currently super resolution is an economical and practical algorithm that can be used to improve image resolution in remote monitoring, remote sensing and medical imaging. In this thesis, in order to obtain high resolution image from an image sequence of low resolution and improve the image quality, visual effects, total variation algorithm is used to estimate the motion of low resolution images caused by the restriction of environmental conditions and the physical limitations of imaging equipment. This algorithm contains a lot of processing technologies, such as, motion estimate, motion compensation, image fusion, de-nosing. Experiment result shows that the entropy of the high resolution image was improved and the D and Dindex are improved with the increasing of frames, so clearly high resolution image can be obtained from source image by using this algorithm. The super resolution algorithm mentioned in the thesis with high practical application value, can be applied to long-range remote sensing and face image restoration.