Shuang Qiu

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.

Study on fatigue feature from forearm SEMG signal based on wavelet analysis

The aim of this paper is to estimate muscle fatigue by using wavelet analysis method in SEMG signal analysis. A signal acquisition system is designed and forearm muscle fatigue experiments under static and dynamic contractions are performed. The wavelet analysis method is proposed to group the wavelet coefficients of SEMG signal into high frequency-band (100Hz–350Hz) and low frequency-band (13–22Hz). The amplitude of SEMG signal is determined by calculating the root mean square, the amplitude of high frequency is correlated to the force level and the amplitude of low frequency band which is correlated to the muscle fatigue shows an upward trend. Then correlation coefficients between RMS of low frequency band and MF, RMS of low frequency band and MDF in static contraction as well the first time-varying parameter in dynamic contraction are calculated. Results demonstrate that the wavelet analysis method is an effective analysis tool in muscle fatigue evaluation and it lays a foundation for studying at the muscle fatigue in a variety of muscle contraction modes.2

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.

A Stimulus Artifact Removal Technique for SEMG Signal Processing During Functional Electrical Stimulation

Goal: The purpose of this study was to design a method for extracting the volitional EMG from recorded surface electromyography (EMG), contaminated by functional electrical stimulation (FES) artifact. Methods: Considering that the FES artifact emerges periodically with rather large amplitude in nonstationary EMG, we designed an adaptive-matched filter (AMF) via genetic algorithm (GA) optimization. Both the simulated and real data from seven subjects were processed, using the GA-AMF filter and comb filter, respectively. To test the filtering effect on the EMG, contaminated with FES artifact of different current intensities, the contaminated EMG was simulated by combining the simulation artifact and clean EMG with various FES artifacts to clean EMG ratios. Results: The results show that, in simulation test, compared to the EMG filtered by comb filter, the simulated EMG ( p <; 0.05), filtered by using GA-AMF, had significantly higher correlation coefficient, higher signal to noise ratio, and lower normalized root mean square error, whereas the real EMG (p <; 0.05), filtered by using GA-AMF had higher power reduction than that filtered by using comb filter. The results indicate that GA-AMF can effectively remove FES artifact from the EMG of the stimulated muscle and its adjacent muscle, and the GA-AMF filter performed better than did the comb filter. Conclusion: All these results demonstrate that the GA-AMF filter is capable of extracting volitional EMG from the stimulated muscle and adjacent muscles. Significance: GA-AMF could provide technical support for improving EMG feedback control of FES rehabilitation system.

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.

Intelligent algorithm tuning PID method of function electrical stimulation using knee joint angle.

Functional electrical stimulation (FES) could restore motor functions for individuals with spinal cord injury (SCI). By applying electric current pulses, FES system could produce muscle contractions, generate joint torques, and thus, achieve joint movements automatically. Since the muscle system is highly nonlinear and time-varying, feedback control is quite necessary for precision control of the preset action. In the present study, we applied two methods (Proportional Integral Derivative (PID) controller based on Back Propagation (BP) neural network and that based on Genetic Algorithm (GA)), to control the knee joint angle for the FES system, while the traditional Ziegler-Nichols method was used in the control group for comparison. They were tested using a muscle model of the quadriceps. The results showed that intelligent algorithm tuning PID controller displayed superior performance than classic Ziegler-Nichols method with constant parameters. More particularly, PID controller tuned by BP neural network was superior on controlling precision to make the feedback signal track the desired trajectory whose error was less than 1.2°±0.16°, while GA-PID controller, seeking the optimal parameters from multipoint simultaneity, resulted in shortened delay in the response. Both strategies showed promise in application of intelligent algorithm tuning PID methods in FES system.

Microcirculation Perfusion Monitor on the Back of the Health Volunteers

Objective. To observe the dermal microcirculation blood perfusion characterization of meridians channels (acupoints). Methods. 20 healthy human subjects were monitored using Pericam Perfusion Speckle Imager (PSI) for the changes in dermal microcirculation blood perfusion on governor meridian and other respective dermal regions as a control. Result. The microcirculation blood perfusion on Governor Meridian is higher than its control area. Conclusion. The dermal microcirculation blood perfusion on certain parts of Governor Meridian of healthy human subjects showed specifics.

Sonomyography Analysis on Thickness of Skeletal Muscle During Dynamic Contraction Induced by Neuromuscular Electrical Stimulation: A Pilot Study

Goal: Neuromuscular electrical stimulation (NMES) that stimulates skeletal muscles to induce contractions has been widely applied to restore functions of paralyzed muscles. However, the architectural changes of stimulated muscles induced by NMES are still not well understood. The present study applies sonomyography (SMG) to evaluate muscle architecture under NMES-induced and voluntary movements. The quadriceps muscles of seven healthy subjects were tested for eight cycles during an extension exercise of the knee joint with/without NMES, and SMG and the knee joint angle were recorded during the process of knee extension. A least squares support vector machine (LS-SVM) LS-SVM model was developed and trained using the data sets of six cycles collected under NMES, while the remaining data was used to test. Muscle thickness changes were extracted from ultrasound images and compared between NMES-induced and voluntary contractions, and LS-SVM was used to model a relationship between dynamical knee joint angles and SMG signals. Muscle thickness showed to be significantly correlated with joint angle

STIMULUS ARTIFACT REMOVAL OF SEMG SIGNALS DETECTED DURING FUNCTIONAL ELECTRICAL STIMULATION

(P< 0.05)