Effects of bihemispheric transcranial direct current stimulation on motor recovery in subacute stroke patients: a double-blind, randomized sham-controlled trial.

BACKGROUND: Bihemispheric transcranial direct current stimulation (tDCS) of the primary motor cortex (M1) can simultaneously modulate bilateral corticospinal excitability and interhemispheric interaction. However, how tDCS affects subacute stroke recovery remains unclear. We investigated the effects of bihemispheric tDCS on motor recovery in subacute stroke patients. METHODS: We enrolled subacute inpatients who had first-ever ischemic stroke at subcortical regions and moderate-to-severe baseline Fugl-Meyer Assessment of Upper Extremity (FMA-UE) score 2-56. Participants between 14 and 28 days after stroke were double-blind, randomly assigned (1:1) to receive real (n = 13) or sham (n = 14) bihemispheric tDCS (with ipsilesional M1 anode and contralesional M1 cathode, 20 min, 2 mA) during task practice twice daily for 20 sessions in two weeks. Residual integrity of the ipsilesional corticospinal tract was stratified between groups. The primary efficacy outcome was the change in FMA-UE score from baseline (responder as an increase ≥ 10). The secondary measures included changes in the Action Research Arm Test (ARAT), FMA-Lower Extremity (FMA-LE) and explorative resting-state MRI functional connectivity (FC) of target regions after intervention and three months post-stroke. RESULTS: Twenty-seven participants completed the study without significant adverse effects. Nineteen patients (70%) had no recordable baseline motor-evoked potentials (MEP-negative) from the paretic forearm. Compared with the sham group, the real tDCS group showed enhanced improvement of FMA-UE after intervention (p < 0.01, effect size η2 = 0.211; responder rate: 77% vs. 36%, p = 0.031), which sustained three months post-stroke (p < 0.01), but not ARAT. Interestingly, in the MEP-negative subgroup analysis, the FMA-UE improvement remained but delayed. Additionally, the FMA-LE improvement after real tDCS was not significantly greater until three months post-stroke (p < 0.01). We found that the individual FMA-UE improvements after real tDCS were associated with bilateral intrahemispheric, rather than interhemispheric, FC strengths in the targeted cortices, while the improvements after sham tDCS were associated with predominantly ipsilesional FC changes after adjustment for age and sex (p < 0.01). CONCLUSIONS: Bihemispheric tDCS during task-oriented training may facilitate motor recovery in subacute stroke patients, even with compromised corticospinal tract integrity. Further studies are warranted for tDCS efficacy and network-specific neuromodulation. TRIAL REGISTRATION: This study is registered with ClinicalTrials.gov: (ID: NCT02731508).

Evaluating the performance of machine learning models for automatic diagnosis of patients with schizophrenia based on a single site dataset of 440 participants.

BACKGROUND: Support vector machines (SVMs) based on brain-wise functional connectivity (FC) have been widely adopted for single-subject prediction of patients with schizophrenia, but most of them had small sample size. This study aimed to evaluate the performance of SVMs based on a large single-site dataset and investigate the effects of demographic homogeneity and training sample size on classification accuracy. METHODS: The resting functional Magnetic Resonance Imaging (fMRI) dataset comprised 220 patients with schizophrenia and 220 healthy controls. Brain-wise FCs was calculated for each participant and linear SVMs were developed for automatic classification of patients and controls. First, we evaluated the SVMs based on all participants and homogeneous subsamples of men, women, younger (18-30 years), and older (31-50 years) participants by 10-fold nested cross-validation. Then, we hold out a fixed test set of 40 participants (20 patients and 20 controls) and evaluated the SVMs based on incremental training sample sizes (N = 40, 80, …, 400). RESULTS: We found that the SVMs based on all participants had accuracy of 85.05%. The SVMs based on male, female, young, and older participants yielded accuracy of 84.66, 81.56, 80.50, and 86.13%, respectively. Although the SVMs based on older subsamples had better performance than those based on all participants, they generalized poorly to younger participants (77.24%). For incremental training sizes, the classification accuracy increased stepwise from 72.6 to 83.3%, with >80% accuracy achieved with sample size >240. CONCLUSIONS: The findings indicate that SVMs based on a large dataset yield high classification accuracy and establish models using a large sample size with heterogeneous properties are recommended for single subject prediction of schizophrenia.

Phase-Approaching Stimulation Sequence for SSVEP-Based BCI: A Practical Use in VR/AR HMD.

Steady-state visual evoked potential (SSVEP) has been used to implement brain-computer interface (BCI) due to its advantages of high information transfer rate (ITR) and high accuracy. In recent years, owing to the developments of head-mounted device (HMD), the HMD has become a popular device to implement SSVEP-based BCI. However, an HMD with fixed frame rate only can flash at its subharmonic frequencies which limits the available number of stimulation frequencies for SSVEP-based BCI. In order to increase the number of available commands for SSVEP-based BCI, we proposed a phase-approaching (PA) method to generate visual stimulation sequences at user-specified frequency on an HMD. The flickering sequence generated by our PA method (PAS sequence) tries to approximate user-specified stimulation frequency by means of minimizing the difference of accumulated phases between our PAS sequence and the ideal wave of user-specified frequency. The generated sequence of PA method determines the brightness state for each frame to approach the accumulated phase of the ideal wave. The SSVEPs evoked from stimulators, driven by PAS sequences, were analyzed using canonical correlation analysis (CCA) to identify user's gazed target. In this study, a six-command SSVEP-based BCI was designed to operate a flying drone. The ITR and detection accuracy are 36.84 bits/min and 93.30%, respectively.

Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation.

The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set.

Empirical Mode Decomposition and Monogenic Signal-Based Approach for Quantification of Myocardial Infarction From MR Images.

Quantification of myocardial infarction on late Gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) images into heterogeneous infarct periphery (or gray zone) and infarct core plays an important role in cardiac diagnosis, especially in identifying patients at high risk of cardiovascular mortality. However, quantification task is challenging due to noise corrupted in cardiac MR images, the contrast variation, and limited resolution of images. In this study, we propose a novel approach for automatic myocardial infarction quantification, termed DEMPOT, which consists of three key parts: Decomposition of image into intrinsic modes, monogenic phase performing on combined dominant modes, and multilevel Otsu thresholding on the phase. In particular, inspired by the Hilbert-Huang transform, we perform the multidimensional ensemble empirical mode decomposition and 2-D generalization of the Hilbert transform known as the Riesz transform on the MR image to obtain the monogenic phase that is robust to noise and contrast variation. Then, a two-stage algorithm using multilevel Otsu thresholding is accomplished on the monogenic phase to automatically quantify the myocardium into healthy, gray zone, and infarct core regions. Experiments on LGE-CMR images with myocardial infarction from 82 patients show the superior performance of the proposed approach in terms of reproducibility, robustness, and effectiveness.

Analyses of EEG Oscillatory Activities during Slow and Fast Repetitive Movements using Holo-Hilbert Spectral Analysis.

Neural oscillatory activities existing in multiple fre-quency bands usually represent different levels of neurophysiolog-ical meanings, from micro-scale to macro-scale organizations. In this study, we adopted Holo-Hilbert spectral analysis (HHSA) to study the amplitude-modulated (AM) and frequency-modulated (FM) components in sensorimotor Mu rhythm, induced by slow- and fast-rate repetitive movements. The HHSA-based approach is a two-layer empirical mode decomposition (EMD) architecture, which firstly decomposes the EEG signal into a series of frequency-modulated intrinsic mode functions (IMF) and then decomposes each frequency-modulated IMF into a set of amplitude-modulated IMFs. With the HHSA, the FM and AM components were incor-porated with their instantaneous power to achieve full-informa-tional spectral analysis. We observed that the instantaneous power induced by slow-rate movements was significantly higher than that induced by fast-rate movements (p < 0.01, Wilcoxon signed rank test). The alpha-band AM frequencies induced by slow-rate movements were higher than those induced by fast-rate move-ments, while no statistical difference was found in beta-band AM frequencies. In addition, to study the functional coupling between the primary sensorimotor area and other brain regions, spectral coherence was applied and statistical difference was found in frontal area in slow-rate versus fast-rate movements. The discrep-ancy between slow- and fast-rate movements might be owing to the change of motor functional modes from default mode network (DMN) to automatic timing with the increase of movement rates. The use of HHSA for oscillatory activity analysis can be an effi-cient tool to provide informative interaction among different fre-quency bands.

Study of Repetitive Movements Induced Oscillatory Activities in Healthy Subjects and Chronic Stroke Patients.

Repetitive movements at a constant rate require the integration of internal time counting and motor neural networks. Previous studies have proved that humans can follow short durations automatically (automatic timing) but require more cognitive efforts to track or estimate long durations. In this study, we studied sensorimotor oscillatory activities in healthy subjects and chronic stroke patients when subjects were performing repetitive finger movements. We found the movement-modulated changes in alpha and beta oscillatory activities were decreased with the increase of movement rates in finger lifting of healthy subjects and the non-paretic hands in stroke patients, whereas no difference was found in the paretic-hand movements at different movement rates in stroke patients. The significant difference in oscillatory activities between movements of non-paretic hands and paretic hands could imply the requirement of higher cognitive efforts to perform fast repetitive movements in paretic hands. The sensorimotor oscillatory response in fast repetitive movements could be a possible indicator to probe the recovery of motor function in stroke patients.

Evaluate the Feasibility of Using Frontal SSVEP to Implement an SSVEP-Based BCI in Young, Elderly and ALS Groups.

This paper studies the amplitude-frequency characteristic of frontal steady-state visual evoked potential (SSVEP) and its feasibility as a control signal for brain computer interface (BCI). SSVEPs induced by different stimulation frequencies, from 13 ~ 31 Hz in 2 Hz steps, were measured in eight young subjects, eight elders and seven ALS patients. Each subject was requested to participate in a calibration study and an application study. The calibration study was designed to find the amplitude-frequency characteristics of SSVEPs recorded from Oz and Fpz positions, while the application study was designed to test the feasibility of using frontal SSVEP to control a two-command SSVEP-based BCI. The SSVEP amplitude was detected by an epoch-average process which enables artifact-contaminated epochs can be removed. The seven ALS patients were severely impaired, and four patients, who were incapable of completing our BCI task, were excluded from calculation of BCI performance. The averaged accuracies, command transfer intervals and information transfer rates in operating frontal SSVEP-based BCI were 96.1%, 3.43 s/command, and 14.42 bits/min in young subjects; 91.8%, 6.22 s/command, and 6.16 bits/min in elders; 81.2%, 12.14 s/command, and 1.51 bits/min in ALS patients, respectively. The frontal SSVEP could be an alternative choice to design SSVEP-based BCI.

Change in the cortical complexity of spinocerebellar ataxia type 3 appears earlier than clinical symptoms.

Patients with spinocerebellar ataxia type 3 (SCA3) have exhibited cerebral cortical involvement and various mental deficits in previous studies. Clinically, conventional measurements, such as the Mini-Mental State Examination (MMSE) and electroencephalography (EEG), are insensitive to cerebral cortical involvement and mental deficits associated with SCA3, particularly at the early stage of the disease. We applied a three-dimensional fractal dimension (3D-FD) method, which can be used to quantify the shape complexity of cortical folding, in assessing cortical degeneration. We evaluated 48 genetically confirmed SCA3 patients by employing clinical scales and magnetic resonance imaging and using 50 healthy participants as a control group. According to the Scale for the Assessment and Rating of Ataxia (SARA), the SCA3 patients were diagnosed with cortical dysfunction in the cerebellar cortex; however, no significant difference in the cerebral cortex was observed according to the patients' MMSE ratings. Using the 3D-FD method, we determined that cortical involvement was more extensive than involvement of traditional olivopontocerebellar regions and the corticocerebellar system. Moreover, the significant correlation between decreased 3D-FD values and disease duration may indicate atrophy of the cerebellar cortex and cerebral cortex in SCA3 patients. The change of the cerebral complexity in the SCA3 patients can be detected throughout the disease duration, especially it becomes substantial at the late stage of the disease. Furthermore, we determined that atrophy of the cerebral cortex may occur earlier than changes in MMSE scores and EEG signals.

A noninvasive brain computer interface using visually-induced near-infrared spectroscopy responses.

Visually-induced near-infrared spectroscopy (NIRS) response was utilized to design a brain computer interface (BCI) system. Four circular checkerboards driven by distinct flickering sequences were displayed on a LCD screen as visual stimuli to induce subjects' NIRS responses. Each flickering sequence was a concatenated sequence of alternative flickering segments and resting segments. The flickering segment was designed with fixed duration of 3s whereas the resting segment was chosen randomly within 15-20s to create the mutual independencies among different flickering sequences. Six subjects were recruited in this study and subjects were requested to gaze at the four visual stimuli one-after-one in a random order. Since visual responses in human brain are time-locked to the onsets of visual stimuli and the flicker sequences of distinct visual stimuli were designed mutually independent, the NIRS responses induced by user's gazed targets can be discerned from non-gazed targets by applying a simple averaging process. The accuracies for the six subjects were higher than 90% after 10 or more epochs being averaged.

Adaptive SSVEP-based BCI system with frequency and pulse duty-cycle stimuli tuning design.

This study aims to design a steady state visual evoked potentials (SSVEP) based brain-computer interface (BCI) system with only three electrodes. It is known that low frequency flickering induces more intensive SSVEP, but might cause users feel uncomfortable and easily tired. Therefore, this paper proposes a novel middle/high frequency flickering stimulus. However, users show different SSVEP responses when gazing at the same stimuli. It is improper to design fixed frequency flickering stimuli for all users. This study firstly proposes a strategy to adjust the stimuli frequency for each user that could cause better SSVEP. Moreover, to further enhance the SSVEP, this study incorporates flickering duty-cycle for stimuli design, which has been discussed less for SSVEP-based BCI systems. The proposed system consists of two modes, flicker frequency/duty-cycle selection mode and application mode. The flicker frequency/duty-cycle selection mode obtains two best frequencies between 24 and 36 Hz with their related optimal duty-cycle. Then the system goes into the application mode to control the devices. A new fact that has been found is that the optimal flicker frequency and duty-cycle do not vary with time. It means once the optical flicker frequency and duty-cycle is determined the first time, flicker frequency/duty-cycle selection mode does not need to operate the next time. Furthermore, the phase coding technology is used to extend the one command/one frequency to multi command/one frequency. Experimental results show the proposed system has good performance with average accuracy 95% and average command transfer interval 4.4925 s per command.

Quantifying cerebellar atrophy in multiple system atrophy of the cerebellar type (MSA-C) using three-dimensional gyrification index analysis.

Multiple system atrophy of the cerebellar type (MSA-C) is a degenerative neurological disease of the central nervous system. This study employed a method named, "surface-based three-dimensional gyrification index" (3D-GI) to quantify morphological changes in normal cerebellum (including brainstem) and atrophied cerebellum, in patients with MSA-C. We assessed whether 3D-GI can exclude gender and age differences to quantify cerebellum and brainstem atrophy more accurately. Sixteen healthy subjects and 16 MSA-C patients participated in this study. We compared 3D-GI values and volumes in the cerebellum, based on T1-weighted MR images. We also compared the images of reconstructed 3D cerebellum gray matter (3D-CBGM) and cerebellum white matter (3D-CBWM) to detect the atrophied cerebellar region in MSA-C patients. The 3D-GI values were in a stable range with small variances, exhibiting no gender effect and no age-related shrinkage. Significantly lower 3D-GI values were exhibited in both CBGM and CBWM of the MSA-C patients compared with healthy subjects, even in the early phases of the disease. Decreases in 3D-GI values indicated the degeneration of the cerebellar folding structure, exactly reflecting the morphological changes in cerebellum. The 3D-GI method based on CBGM resulted in superior discriminative accuracy compared with the CBGM volumetric method. Using the two-dimensional 3D-GI values, the K-means classifier can evidently discriminate the MSA-C patients from healthy subjects.

Frequency recognition in an SSVEP-based brain computer interface using empirical mode decomposition and refined generalized zero-crossing.

This paper presents an empirical mode decomposition (EMD) and refined generalized zero crossing (rGZC) approach to achieve frequency recognition in steady-stated visual evoked potential (SSVEP)-based brain computer interfaces (BCIs). Six light emitting diode (LED) flickers with high flickering rates (30, 31, 32, 33, 34, and 35 Hz) functioned as visual stimulators to induce the subjects' SSVEPs. EEG signals recorded in the Oz channel were segmented into data epochs (0.75 s). Each epoch was then decomposed into a series of oscillation components, representing fine-to-coarse information of the signal, called intrinsic mode functions (IMFs). The instantaneous frequencies in each IMF were calculated by refined generalized zero-crossing (rGZC). IMFs with mean instantaneous frequencies (f(GZC)) within 29.5 Hz and 35.5 Hz (i.e., 29.5≤f(GZC)≤35.5 Hz) were designated as SSVEP-related IMFs. Due to the time-locked and phase-locked characteristics of SSVEP, the induced SSVEPs had the same frequency as the gazing visual stimulator. The LED flicker that contributed the majority of the frequency content in SSVEP-related IMFs was chosen as the gaze target. This study tests the proposed system in five male subjects (mean age=25.4±2.07 y/o). Each subject attempted to activate four virtual commands by inputting a sequence of cursor commands on an LCD screen. The average information transfer rate (ITR) and accuracy were 36.99 bits/min and 84.63%. This study demonstrates that EMD is capable of extracting SSVEP data in SSVEP-based BCI system.

Intelligent complementary sliding-mode control for LUSMS-based X-Y-theta motion control stage.

An intelligent complementary sliding-mode control (ICSMC) system using a recurrent wavelet-based Elman neural network (RWENN) estimator is proposed in this study to control the mover position of a linear ultrasonic motors (LUSMs)-based X-Y-theta motion control stage for the tracking of various contours. By the addition of a complementary generalized error transformation, the complementary sliding-mode control (CSMC) can efficiently reduce the guaranteed ultimate bound of the tracking error by half compared with the slidingmode control (SMC) while using the saturation function. To estimate a lumped uncertainty on-line and replace the hitting control of the CSMC directly, the RWENN estimator is adopted in the proposed ICSMC system. In the RWENN, each hidden neuron employs a different wavelet function as an activation function to improve both the convergent precision and the convergent time compared with the conventional Elman neural network (ENN). The estimation laws of the RWENN are derived using the Lyapunov stability theorem to train the network parameters on-line. A robust compensator is also proposed to confront the uncertainties including approximation error, optimal parameter vectors, and higher-order terms in Taylor series. Finally, some experimental results of various contours tracking show that the tracking performance of the ICSMC system is significantly improved compared with the SMC and CSMC systems.

Dual-frequency steady-state visual evoked potential for brain computer interface.

This study presents a new steady-state visual evoked potential (SSVEP) for brain computer interface (BCI) systems. The goal of this study is to increase the number of selections using fewer stimulation frequencies. This study analyzes the SSVEPs induced by six groups of light-emitting diodes (LEDs). The proposed method produces more selections than the number of stimulation frequencies through a suitable combination of dual frequencies for stimulation. Further, the six groups of LEDs are generated by four frequencies. The symmetric harmonic phenomena in this study helps increase recognition efficiency. This study tests seven subjects to verify the feasibility of the proposed method.

An SSVEP-actuated brain computer interface using phase-tagged flickering sequences: a cursor system.

This study presents a new steady-state visual evoked potential (SSVEP)-based brain computer interface (BCI). SSVEPs, induced by phase-tagged flashes in eight light emitting diodes (LEDs), were used to control four cursor movements (up, right, down, and left) and four button functions (on, off, right-, and left-clicks) on a screen menu. EEG signals were measured by one EEG electrode placed at Oz position, referring to the international EEG 10-20 system. Since SSVEPs are time-locked and phase-locked to the onsets of SSVEP flashes, EEG signals were bandpass-filtered and segmented into epochs, and then averaged across a number of epochs to sharpen the recorded SSVEPs. Phase lags between the measured SSVEPs and a reference SSVEP were measured, and targets were recognized based on these phase lags. The current design used eight LEDs to flicker at 31.25 Hz with 45 degrees phase margin between any two adjacent SSVEP flickers. The SSVEP responses were filtered within 29.25-33.25 Hz and then averaged over 60 epochs. Owing to the utilization of high-frequency flickers, the induced SSVEPs were away from low-frequency noises, 60 Hz electricity noise, and eye movement artifacts. As a consequence, we achieved a simple architecture that did not require eye movement monitoring or other artifact detection and removal. The high-frequency design also achieved a flicker fusion effect for better visualization. Seven subjects were recruited in this study to sequentially input a command sequence, consisting of a sequence of eight cursor functions, repeated three times. The accuracy and information transfer rate (mean +/- SD) over the seven subjects were 93.14 +/- 5.73% and 28.29 +/- 12.19 bits/min, respectively. The proposed system can provide a reliable channel for severely disabled patients to communicate with external environments.

Fractal dimension analysis for quantifying cerebellar morphological change of multiple system atrophy of the cerebellar type (MSA-C).

Multiple system atrophy of the cerebellar type (MSA-C) is a degenerative neurological disease of the central nervous system. This study aims to demonstrate that the morphological changes of cerebellar structure, specifically, the cerebellum white matter (CBWM) and cerebellum gray matter (CBGM) from T1-weighted magnetic resonance (MR) images, can be quantified by three-dimensional (3D) fractal dimension (FD) analysis, which is a measure of complexity. Twenty-three MSA-C patients and twenty-one normal subjects participated in this study. The results of this study show that MSA-C patients presented significantly lower FD values compared to the control group, and that morphological change in the CBWM dominates the cerebellar degeneration. In addition, the FD analysis method is superior to conventional volumetric methods in quantifying the structural changes of WM and GM because it exhibits smaller variances and less gender effect. Since a decrease of cerebellar FD value indicates degeneration of the cerebellar structure, this study further suggests that the morphological changes of cerebellar structures (CBGM and CBWM) can be characterized by FD analysis.

Single-trial analysis of cortical oscillatory activities during voluntary movements using empirical mode decomposition (EMD)-based spatiotemporal approach.

This study presents a method based on empirical mode decomposition (EMD) and a spatial template-based matching approach to extract sensorimotor oscillatory activities from multi-channel magnetoencephalographic (MEG) measurements during right index finger lifting. The longitudinal gradiometer of the sensor unit which presents most prominent SEF was selected on which each single-trial recording was decomposed into a set of intrinsic mode functions (IMFs). The correlation between each IMF of the selected channel and raw data on other channels were created and represented as a spatial map. The sensorimotor-related IMFs with corresponding correlational spatial map exhibiting large values on primary sensorimotor area (SMI) were selected via spatial-template matching process. Trial-specific alpha and beta bands were determined in sensorimotor-related oscillatory activities using a two-spectrum comparison between the spectra obtained from baseline period (-4 to -3 s) and movement-onset period (-0.5 to 0.5 s). Sensorimotor-related oscillatory activities were filtered within the trial-specific frequency bands to resolve task-related oscillatory activities. Results demonstrated that the optimal phase and amplitude information were preserved not only for alpha suppression (event-related desynchronization) and beta rebound (event-related synchronization) but also for profound analysis of subtle dynamics across trials. The retention of high SNR in the extracted oscillatory activities allow various methods of source estimation that can be applied to study the intricate brain dynamics of motor control mechanisms. The present study enables the possibility of investigating cortical pathophysiology of movement disorder on a trial-by-trial basis which also permits an effective alternative for participants or patients who can not endure lengthy procedures or are incapable of sustaining long experiments.

Robust dynamic sliding-mode control using adaptive RENN for magnetic levitation system.

In this paper, a robust dynamic sliding mode control system (RDSMC) using a recurrent Elman neural network (RENN) is proposed to control the position of a levitated object of a magnetic levitation system considering the uncertainties. First, a dynamic model of the magnetic levitation system is derived. Then, a proportional-integral-derivative (PID)-type sliding-mode control system (SMC) is adopted for tracking of the reference trajectories. Moreover, a new PID-type dynamic sliding-mode control system (DSMC) is proposed to reduce the chattering phenomenon. However, due to the hardware being limited and the uncertainty bound being unknown of the switching function for the DSMC, an RDSMC is proposed to improve the control performance and further increase the robustness of the magnetic levitation system. In the RDSMC, an RENN estimator is used to estimate an unknown nonlinear function of lumped uncertainty online and replace the switching function in the hitting control of the DSMC directly. The adaptive learning algorithms that trained the parameters of the RENN online are derived using Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties including approximation error, optimal parameter vectors, and higher order terms in Taylor series. Finally, some experimental results of tracking the various periodic trajectories demonstrate the validity of the proposed RDSMC for practical applications.

Implementation of pipelined FastICA on FPGA for real-time blind source separation.

Fast independent component analysis (FastICA) algorithm separates the independent sources from their mixtures by measuring non-Gaussian. FastICA is a common offline method to identify artifact and interference from their mixtures such as electroencephalogram (EEG), magnetoencephalography (MEG), and electrocardiogram (ECG). Therefore, it is valuable to implement FastICA for real-time signal processing. In this paper, the FastICA algorithm is implemented in a field-programmable gate array (FPGA), with the ability of real-time sequential mixed signals processing by the proposed pipelined FastICA architecture. Moreover, in order to increase the numbers precision, the hardware floating-point (FP) arithmetic units had been carried out in the hardware FastICA. In addition, the proposed pipeline FastICA provides the high sampling rate (192 kHz) capability by hand coding the hardware FastICA in hardware description language (HDL). To verify the features of the proposed hardware FastICA, simulations are first performed, then real-time signal processing experimental results are presented using the fabricated platform. Experimental results demonstrate the effectiveness of the presented hardware FastICA as expected.