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.
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.
Endometriosis coexisting with a dermoid cyst of the ovary is extraordinarily rare, although both these benign conditions are said to be common in women in the reproductive age group. There are only two previous case reports,which is evident from our literature review from January 1960 through January 2010. Acute abdomen is one of the greatest diagnostic challenges and easily ignored by the clinicians to exclude the possibility of gynecologic illness. A 35-year-old woman was referred by the doctor in Family clinic. She experienced a three-day period of severe right lower abdominal pain and intermittent vomiting. Ultrasonography identified a bilocular, cystic, hypoechoic, and hyperechoic tumor, 7 cm × 6 cm × 6 cm in the right adnexal region. Laparoscopic cystectomy was performed under the impression of ovarian cyst with torsion or hemorrhage. The frozen section was benign and appendiceal status was adequate. Histopathologic examination described an ovarian cyst composed of endometrial-type lining with stromacells (endometriosis) and benign teratoma tissue with plenty of skin appendages and sebaceous glands. We report this unusual and interesting ovarian mass to remind physicians that the usage of the Endobag after cystectomy, the benefits on minimizing operative time, spilled opportunity, and postoperative complications. Laparoscopic techniques for large ovarian masses might be considered. The experience of the surgeon is also very important to prevent misdiagnosis or complication. Further follow up is mandatory for this simultaneous finding of ovarian endometriosis with coincidental dermoid cyst as a separate pathology in single ovary of such a nature. It also presents a challenge to the clinicians and to the pathologists.
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.
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.
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.
This paper proposes a low-cost field-programmable gate-array (FPGA)-based brain-computer interface (BCI) multimedia control system, different from the BCI system, which uses bulky and expensive electroencephalography (EEG) measurement equipment, personal computer, and commercial real-time signal-processing software. The proposed system combines a customized stimulation panel, a brainwave-acquisition circuit, and an FPGA-based real-time signal processor and allows users to use their brainwave to communicate with or control multimedia devices by themselves. This study also designs a light-emitting diode stimulation panel instead of cathode ray tube or liquid-crystal display used in existing studies, to induce a stronger steady-state visual evoked potential (SSVEP), a kind of EEG, used as the input signal of the proposed BCI system. Implementing a prototype of the SSVEP-based BCI multimedia control system verifies the effectiveness of the proposed system. Experimental results show that the subjects SSVEP can successfully control the multimedia device through the proposed BCI system with high identification accuracy.
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.
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.
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.
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.
Current methods used to treat critical limb ischaemia (CLI) are hampered by a lack of effective strategies, therefore, therapeutic vasculogenesis may open up a new field for the treatment of CLI. In this study we investigated the ability of the DPP-4 inhibitor, sitagliptin, originally used as a hypoglycaemic agent, to induce vasculogenesis in vivo.
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.
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