Classification of Action Potentials With High Variability Using Convolutional Neural Network for Motor Unit Tracking.

The reliable classification of motor unit action potentials (MUAPs) provides the possibility of tracking motor unit (MU) activities. However, the variation of MUAP profiles caused by multiple factors in realistic conditions challenges the accurate classification of MUAPs. The goal of this study was to propose an effective method based on the convolutional neural network (CNN) to classify MUAPs with high levels of variation for MU tracking. MUAP variation was added artificially in the synthetic electromyogram (EMG) signals and was induced by changing the forearm postures in the experimental study. The proposed overlapped-segment-wise EMG decomposition method and the spike-triggered averaging method were combined to obtain the MUAP waveform samples of individual MUs in the experimental study, and the MUAP profile classification performance was tested. Since the ground-truth of MU discharge activities was known for the synthetic EMG, the MU tracking performance was further verified by mimicking the tracking procedure of MU discharge activities and the spike consistency with the true spike trains was tested in the simulation study. The conventional MUAP similarity index (SI)-based method was also performed as comparison. For both the experimental and the synthetic EMG signals, the CNN-based method significantly improved the MUAP tracking performance compared with the conventional SI-based method manifested as a higher classification accuracy (93.3%±5.4% vs 56.2%±13.9%) in the experimental study or higher spike consistency (71.1%±10.2% vs 29.2%±11.0%) in the simulation study with a smaller variation. These results demonstrated the efficiency and robustness of the proposed method to distinguish MUAPs with large variations accurately. Further development of the proposed method can promote the study on the physiological and pathological changes of the neuromuscular system where tracking MU activities is needed.

Enhancement of motor imagery training efficiency by an online adaptive training paradigm integrated with error related potential.

Objective. Motor imagery (MI) is a process of autonomously modulating the motor area to rehearse action mentally without actual execution. Based on the neuroplasticity of the cerebral cortex, MI can promote the functional rehabilitation of the injured cerebral cortex motor area. However, it usually takes several days to a few months to train individuals to acquire the necessary MI ability to control rehabilitation equipment in current studies, which greatly limits the clinical application of rehabilitation training systems based on the MI brain-computer interface (BCI).Approach. A novel MI training paradigm combined with the error related potential (ErrP) is proposed, and online adaptive training of the MI classifier was performed using ErrP. ErrP is used to correct the output of the MI classification to obtain a higher accuracy of kinesthetic feedback based on the imagination intention of subjects while generating simulated labels for MI online adaptive training. In this way, we improved the MI training efficiency. Thirteen subjects were randomly divided into an experimental group using the proposed paradigm and a control group using the traditional MI training paradigm to participate in six MI training experiments.Main results. The proposed paradigm enabled the experimental group to obtain a higher event-related desynchronization modulation level in the contralateral brain region compared with the control group and 69.76% online classification accuracy of MI after three MI training experiments. The online classification accuracy reached 72.76% and the whole system recognized the MI intention of the subjects with an online accuracy of 82.61% after six experiments.Significance. Compared with the conventional unimodal MI training strategy, the proposed approach enables subjects to use the MI-BCI based system directly and achieve a better performance after only three training experiments with training left and right hands simultaneously. This greatly improves the usability of the MI-BCI-based rehabilitation system and makes it more convenient for clinical use.

Evaluation of Synergy-Based Hand Gesture Recognition Method Against Force Variation for Robust Myoelectric Control.

The non-stationary characteristics of surface electromyography (sEMG) and possible adverse variations in real-world conditions make it still an open challenge to realize robust myoelectric control (MEC) for multifunctional prostheses. Variable muscle contraction level is one of the handicaps that may degrade the performance of MEC. In this study, we proposed a force-invariant intent recognition method based on muscle synergy analysis (MSA) in the setting of three self-defined force levels (low, medium, and high). Specifically, a fast matrix factorization algorithm based on alternating non-negativity constrained least squares (NMF/ANLS) was chosen to extract task-specific synergies associated with each of six hand gestures in the training stage; while for the testing samples, we used the non-negative least square (NNLS) method to estimate neural commands for movement classification. The performance of proposed method was compared with conventional pattern recognition (PR) method consisting of LDA (linear discrimination analysis) classifier and representative features in three offline evaluation scenarios. Statistical tests on ten able-bodied subjects revealed no significant difference in intra-force-level (p = 0.353) and multi-force-level (p = 0.695) accuracy; But the synergy-based method performed significantly better than conventional PR-based method under inter-force-level conditions (p < 0.05). Similar results were observed for nine amputee subjects though there was a drop in the classification accuracy. This study was the first to concurrently demonstrate the robustness and predictive power of task-specific synergies under variant force levels and explore their potential for reliable intent recognition against force variation. Although the online performance is yet to be demonstrated, the proposed method is characterized by simple training procedure and acceptable computational efficiency, which would potentially provide an alternative approach for the development of clinically viable prostheses and rehabilitation robots driven by sEMG.

Enhancing Performance of SSVEP-Based Visual Acuity via Spatial Filtering.

The purpose of this study was to enhance the performance of steady-state visual evoked potential (SSVEP)-based visual acuity assessment with spatial filtering methods. Using the vertical sinusoidal gratings at six spatial frequency steps as the visual stimuli for 11 subjects, SSVEPs were recorded from six occipital electrodes (O1, Oz, O2, PO3, POz, and PO4). Ten commonly used training-free spatial filtering methods, i.e., native combination (single-electrode), bipolar combination, Laplacian combination, average combination, common average reference (CAR), minimum energy combination (MEC), maximum contrast combination (MCC), canonical correlation analysis (CCA), multivariate synchronization index (MSI), and partial least squares (PLS), were compared for multielectrode signals combination in SSVEP visual acuity assessment by statistical analyses, e.g., Bland-Altman analysis and repeated-measures ANOVA. The SSVEP signal characteristics corresponding to each spatial filtering method were compared, determining the chosen spatial filtering methods of CCA and MSI with a higher performance than the native combination for further signal processing. After the visual acuity threshold estimation criterion, the agreement between the subjective Freiburg Visual Acuity and Contrast Test (FrACT) and SSVEP visual acuity for the native combination (0.253 logMAR), CCA (0.202 logMAR), and MSI (0.208 logMAR) was all good, and the difference between FrACT and SSVEP visual acuity was also all acceptable for the native combination (-0.095 logMAR), CCA (0.039 logMAR), and MSI (-0.080 logMAR), where CCA-based SSVEP visual acuity had the best performance and the native combination had the worst. The study proved that the performance of SSVEP-based visual acuity can be enhanced by spatial filtering methods of CCA and MSI and also recommended CCA as the spatial filtering method for multielectrode signals combination in SSVEP visual acuity assessment.

Enhancement of capability for motor imagery using vestibular imbalance stimulation during brain computer interface.

Objective.Motor imagery (MI), based on the theory of mirror neurons and neuroplasticity, can promote motor cortical activation in neurorehabilitation. The strategy of MI based on brain-computer interface (BCI) has been used in rehabilitation training and daily assistance for patients with hemiplegia in recent years. However, it is difficult to maintain the consistency and timeliness of receiving external stimulation to neural activation in most subjects owing to the high variability of electroencephalogram (EEG) representation across trials/subjects. Moreover, in practical application, MI-BCI cannot highly activate the motor cortex and provide stable interaction owing to the weakness of the EEG feature and lack of an effective mode of activation.Approach.In this study, a novel hybrid BCI paradigm based on MI and vestibular stimulation motor imagery (VSMI) was proposed to enhance the capability of feature response for MI. Twelve subjects participated in a group of controlled experiments containing VSMI and MI. Three indicators, namely, activation degree, timeliness, and classification accuracy, were adopted to evaluate the performance of the task.Main results.Vestibular stimulation could significantly strengthen the suppression ofαandßbands of contralateral brain regions during MI, that is, enhance the activation degree of the motor cortex (p< 0.01). Compared with MI, the timeliness of EEG feature-response achieved obvious improvements in VSMI experiments. Moreover, the averaged classification accuracy of VSMI and MI was 80.56% and 69.38%, respectively.Significance.The experimental results indicate that specific vestibular activity contributes to the oscillations of the motor cortex and has a positive effect on spontaneous imagery, which provides a novel MI paradigm and enables the preliminary exploration of sensorimotor integration of MI.

Real-time, precise, rapid and objective visual acuity assessment by self-adaptive step SSVEPs.

Objective. This study aimed to explore an online, real-time, and precise method to assess steady-state visual evoked potential (SSVEP)-based visual acuity more rapidly and objectively with self-adaptive spatial frequency steps.Approach. Taking the vertical sinusoidal reversal gratings with different spatial frequencies and temporal frequencies as the visual stimuli, according to the psychometric function for visual acuity assessment, a self-adaptive procedure, the best parameter estimation by sequential testing algorithm, was used to calculate the spatial frequency sequence based on all the previous spatial frequencies and their significance of the SSVEP response. Simultaneously, the canonical correlation analysis (CCA) method with a signal-to-noise ratio (SNR) significance detection criterion was used to judge the significance of the SSVEP response.Main results.After 18 iterative trails, the spatial frequency to be presented converged to a value, which was exactly defined as the SSVEP visual acuity threshold. Our results indicated that this SSVEP acuity had a good agreement and correlation with subjective Freiburg Visual Acuity and Contrast Test acuity, and the test-retest repeatability was also good.Significance. The self-adaptive step SSVEP procedure combined with the CCA method and SNR significance detection criterion appears to be an alternative method in the real-time SSVEP acuity test to obtain objective visual acuity more rapidly and precisely.

Quantitative and objective diagnosis of color vision deficiencies based on steady-state visual evoked potentials.

PURPOSE: Traditional color vision tests depend on subjective judgments and are not suitable for infant children and subjects with cognitive dysfunction. We aimed to explore an objective and quantitative color vision testing method based on sweep steady-state visual evoked potentials (sweep SSVEPs) and compare the results with subjective Farnsworth-Munsell (FM) 100-hue test results. METHODS: A red-green SSVEP pattern reversal checkboard paradigm at different luminance ratios was used to induce visual evoked potentials (VEPs) from 15 color vision deficiencies (CVDs) and 11 normal color vision subjects. After electroencephalography signals were processed by canonical correlation analysis, an equiluminance turning curve corresponding to the activation of the L-cones and M-cones at different levels of color vision was established. Then, we obtained different equiluminance T and proposed the SSVEP color vision severity index (ICVD) to quantify color vision function and the severity of CVDs. In addition, the FM 100-hue test was used to obtain subjective data for the diagnosis of color vision. RESULTS: The value of ICVD can be an indicator of the level of color vision. Both the total error scores (TES) and confusion index (C-index) of the FM 100-hue test were significantly correlated with ICVD values (P < 0.001, respectively). ICVD also had a good classification effect in detecting normals, anomalous trichromats and dichromats. Moreover, equiluminance T had a good effect on classifying protans and deutans in subjects with CVDs. CONCLUSION: Color vision evaluation with sweep SSVEPs showed a good correlation with subjective psychophysical methods. SSVEPs can be an objective and quantitative method to test color vision and diagnose CVDs.

Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review.

The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in current research. One of the main assumptions of machine learning is that training and test data belong to the same feature space and are subject to the same probability distribution. However, this may be violated in EEG processing. Variations across sessions/subjects result in a deviation of the feature distribution of EEG signals in the same task, which reduces the accuracy of the decoding model for mental tasks. Recently, transfer learning (TL) has shown great potential in processing EEG signals across sessions/subjects. In this work, we reviewed 80 related published studies from 2010 to 2020 about TL application for EEG decoding. Herein, we report what kind of TL methods have been used (e.g., instance knowledge, feature representation knowledge, and model parameter knowledge), describe which types of EEG paradigms have been analyzed, and summarize the datasets that have been used to evaluate performance. Moreover, we discuss the state-of-the-art and future development of TL for EEG decoding. The results show that TL can significantly improve the performance of decoding models across subjects/sessions and can reduce the calibration time of brain-computer interface (BCI) systems. This review summarizes the current practical suggestions and performance outcomes in the hope that it will provide guidance and help for EEG research in the future.

Assessment of Human Visual Acuity Using Visual Evoked Potential: A Review.

Visual evoked potential (VEP) has been used as an alternative method to assess visual acuity objectively, especially in non-verbal infants and adults with low intellectual abilities or malingering. By sweeping the spatial frequency of visual stimuli and recording the corresponding VEP, VEP acuity can be defined by analyzing electroencephalography (EEG) signals. This paper presents a review on the VEP-based visual acuity assessment technique, including a brief overview of the technique, the effects of the parameters of visual stimuli, and signal acquisition and analysis of the VEP acuity test, and a summary of the current clinical applications of the technique. Finally, we discuss the current problems in this research domain and potential future work, which may enable this technique to be used more widely and quickly, deepening the VEP and even electrophysiology research on the detection and diagnosis of visual function.

Anti-fatigue Performance in SSVEP-Based Visual Acuity Assessment: A Comparison of Six Stimulus Paradigms.

PURPOSE: The occurrence of mental fatigue when users stare at stimuli is a critical problem in the implementation of steady-state visual evoked potential (SSVEP)-based visual acuity assessment, which may weaken the SSVEP amplitude and signal-to-noise ratio (SNR) and subsequently affect the results of visual acuity assessment. This study aimed to explore the anti-fatigue performance of six stimulus paradigms (reverse vertical sinusoidal gratings, reverse horizontal sinusoidal gratings, reverse vertical square-wave gratings, brief-onset vertical sinusoidal gratings, reversal checkerboards, and oscillating expansion-contraction concentric rings) in SSVEP acuity assessment. METHODS: Based on four indices of α + θ index, pupil diameter, National Aeronautics and Space Administration Task Load Index (NASA-TLX), and amplitude and SNR of SSVEPs, this study quantitatively evaluated mental fatigue in six SSVEP visual attention runs corresponding to six paradigms with 12 subjects. RESULTS: These indices of mental fatigue showed a good agreement. The results showed that the paradigm of motion expansion-contraction concentric rings had a superior anti-fatigue efficacy than the other five paradigms of conventional onset mode or pattern reversal mode during prolonged SSVEP experiment. The paradigm of brief-onset mode showed the lowest anti-fatigue efficacy, and the other paradigms of pattern reversal SSVEP paradigms showed a similar anti-fatigue efficacy, which was between motion expansion-contraction mode and onset mode. CONCLUSION: This study recommended the paradigm of oscillating expansion-contraction concentric rings as the stimulation paradigm in SSVEP visual acuity because of its superior anti-fatigue efficacy.

Objective and quantitative assessment of interocular suppression in strabismic amblyopia based on steady-state motion visual evoked potentials.

In this study, we aimed to explore an objective, sensitive and quantitative measurement of interocular suppression in strabismic amblyopia. We compared 11 strabismic subjects with 12 normal vision subjects to explore the different response characterizations in normal eyes, nondominant and dominant eyes of strabismic subjects by using steady-state motion visual evoked potentials (SSMVEPs). Stimulation at different temporal frequencies was presented to two eyes by using an interocular dichoptic technique. Furthermore, canonical correlation analysis (CCA), signal-to-noise ratio (SNR) and some statistical methods, such as the paired t-test, analysis of variance (ANOVA) and correlation analysis, were used to analyze electroencephalography (EEG) signals. We proposed two indices-divergence J and suppression imbalance (SI) to describe the deficits in interocular suppression-and one index - mask attenuation coefficient (MAC)- to describe the influence of a dichoptic mask from the dominant eyes to nondominant eyes of strabismic subjects. A significant difference was found between nondominant and dominant eyes of strabismic subjects in SSMVEP response and SNR value while no apparent difference was observed between the two eyes in subjects with normal vision. There was a strong linear correlation between divergence J, SI and visual acuity difference of two eyes both in strabismic amblyopia and normal vision. A linear correlation was also found between visual acuity difference and MAC in patients with strabismic amblyopia. Our findings suggest that SSMVEPs can be an objective and quantitative method for measuring the interocular suppression in strabismus and assessing the deficits of strabismic amblyopia.

Objective and quantitative assessment of visual acuity and contrast sensitivity based on steady-state motion visual evoked potentials using concentric-ring paradigm.

PURPOSE: The traditional assessment of visual acuity and contrast sensitivity depends more on subjective judgments. Steady-state motion visual evoked potentials (SSMVEPs) can provide an objective and quantitative method to evaluate visual functions such as visual acuity and contrast sensitivity. Here, we explored the possibility of objective SSMVEP visual acuity and contrast sensitivity testing, and compared its performance with that of psychophysical methods. METHODS: In this study, we designed a specific concentric ring with oscillating expansion and contraction SSMVEP paradigm to assess visual acuity and contrast sensitivity. By changing the parameters of the paradigm, the SSMVEP paradigm with different contrasts and spatial frequencies corresponding to different visual acuity and contrast sensitivity was designed. Moreover, we proposed a threshold determination criterion to define the corresponding objective SSMVEP visual acuity and contrast sensitivity. RESULTS: We tested visual acuity and contrast sensitivity of sixteen healthy adults utilizing this paradigm with an electroencephalography system. Our data suggested that there was no significant difference between objective visual acuity and contrast sensitivity measurements based on the SSMVEPs and subjective psychophysical ones. CONCLUSION: Our study proved that SSMVEPs can be an objective and quantitative method to measure visual acuity and contrast sensitivity.

A Novel Underactuated Robotic Finger with Variable Stiffness Joints.

Existing robotic hands mostly consist of rigid finger mechanism with constant joint stiffness, leading to poor handling performance and even unexpected safety issues. This paper proposed a novel underactuated robotic finger with variable stiffness joints based on human finger anatomy and electrostatic adhesion(ESA) principle. The proposed finger is unique in the 3D printable one-piece body structure consisting of three similar joints, actuated by only one linear actuator to mimic the flexion/extension movement of the human finger. It is characterized by simple actuation, light weight, low cost and compliant grasp. We constructed a portable finger prototype to investigate the variable stiffness performance. It turns out that the joint stiffness shows a growing trend as the applied voltage increases, which verifies the effectiveness of this design. The proposed novel finger indicates potential applications in service robots and prosthetic hands.

A Novel Variable Stiffness Compliant Finger Exoskeleton for Rehabilitation Based on Electromagnet Control.

In this study, we present the design of a variable stiffness finger exoskeleton for hand rehabilitation, to meet the requirements of different human users and versatile rehabilitation task. This paper describes the design principle and fabrication of the variable stiffness finger exoskeleton which combines a variable stiffness beam and a 3D printed compliant finger exoskeleton. Experimental studies have shown that by using the electromagnetic force, exoskeleton stiffness variation is achievable. Therefore, the proposed variable stiffness finger exoskeleton is capable of adapting the versatile tasks and providing a soft, wearable device for hand rehabilitation of different human users.