The phase characteristics of the representative frequency components of the Electroencephalogram (EEG) can be a means of understanding the brain functions of human senses and perception. In this paper, we found out that visual evoked potential (VEP) is composed of the dominant multi-band component signals of the EEG through the experiment.
We analyzed the characteristics of VEP based on the theory that brain evoked potentials can be decomposed into phase synchronized signals. In order to decompose the EEG signal into across each frequency component signals, we extracted the signals in the time-frequency domain with high resolution using the empirical mode decomposition method. We applied the Hilbert transform (HT) to extract the signal and synthesized it into a frequency band signal representing VEP components. VEP could be decomposed into phase synchronized δ, θ, α, and β frequency signals. We investigated the features of visual brain function by analyzing the amplitude and latency of the decomposed signals in phase synchronized with the VEP and the phase-locking value (PLV) between brain regions.
In response to visual stimulation, PLV values were higher in the posterior lobe region than in the anterior lobe. In the occipital region, the PLV value of theta band was observed high.
The VEP signals decomposed into constituent frequency components through phase analysis can be used as a method of analyzing the relationship between activated signals and brain function related to visual stimuli.
The best frequency response band for the steady-state visual evoked potential (SSVEP) stimulus for humans is limited. This results in a reduced number of encoded targets.
To circumvent these limitations, we propose a motion-coupled, steady-state motion visual evoked potential (SSMVEP) method. We designed a stimulus paradigm that couples both sinusoidal and square wave motions. The paradigm performs a spiral motion with a higher frequency in the form of sinusoidal wave, and alters the size of the lower frequency via the square wave form.
The motion-coupled SSMVEP method could simultaneously induce stable motion frequency and coupling frequency, and there was no loss of frequency component.
The proposed method has been evaluated to have substantial potential for increasing the number of coding targets, which is an effective supplement to the existing studies.
In this study, the performance of OpenBCI, a low-cost bio-amplifier, is assessed when used for 3D motion reconstruction.
Eleven scalp electrode locations from three subjects were used, with sampling rate of 125 Hz, subsequently band-pass filtered from 0.5 to 40 Hz. After segmentation into epochs, information-rich frequency ranges were determined using filter bank common spatial filter. Simultaneously, the actual hand motions of subjects were captured using a Microsoft Kinect sensor. Multimodal data streams were synchronized using the lab streaming layer (LSL) application. A modified version of an existing multiple linear regression models was employed to learn the relationship between the electroencephalography (EEG) feature input and the recorded kinematic data. To assess system performance with limited data, 10-fold cross validation was used.
The most information-rich frequency bands for subjects were found to be in the ranges of 5 – 9 Hz and 33 – 37 Hz. Hand lateralization accuracy for the three subjects were 97.4, 78.7 and 96.9% respectively. 3D position reconstructed with an average correlation coefficient of 0.21, 0.47 and 0.38 respectively along three pre-defined axes, with the corresponding average correlation coefficients for velocity being 0.21, 0.36 and 0.25 respectively. The results compare favourably with a cross-section of existing results, while cost-per-electrode costs were 76% lower than the average per-electrode cost for similar systems and 44% lower than the cheapest previously-reported system.
This study has shown that low-cost bio-amplifiers such as the OpenBCI can be used for 3D motion reconstruction tasks.
A transfemoral prosthesis is required to assist amputees to perform the activity of daily living (ADL). The passive prosthesis has some drawbacks such as utilization of high metabolic energy. In contrast, the active prosthesis consumes less metabolic energy and offers better performance. However, the recent active prosthesis uses surface electromyography as its sensory system which has weak signals with microvolt-level intensity and requires a lot of computation to extract features. This paper focuses on recognizing different phases of sitting and standing of a transfemoral amputee using in-socket piezoelectric-based sensors. 15 piezoelectric film sensors were embedded in the inner socket wall adjacent to the most active regions of the agonist and antagonist knee extensor and flexor muscles, i. e. region with the highest level of muscle contractions of the quadriceps and hamstring. A male transfemoral amputee wore the instrumented socket and was instructed to perform several sitting and standing phases using an armless chair. Data was collected from the 15 embedded sensors and went through signal conditioning circuits. The overlapping analysis window technique was used to segment the data using different window lengths. Fifteen time-domain and frequency-domain features were extracted and new feature sets were obtained based on the feature performance. Eight of the common pattern recognition multiclass classifiers were evaluated and compared. Regression analysis was used to investigate the impact of the number of features and the window lengths on the classifiers’ accuracies, and Analysis of Variance (ANOVA) was used to test significant differences in the classifiers’ performances. The classification accuracy was calculated using k-fold cross-validation method, and 20% of the data set was held out for testing the optimal classifier. The results showed that the feature set (FS-5) consisting of the root mean square (RMS) and the number of peaks (NP) achieved the highest classification accuracy in five classifiers. Support vector machine (SVM) with cubic kernel proved to be the optimal classifier, and it achieved a classification accuracy of 98.33 % using the test data set. Obtaining high classification accuracy using only two time-domain features would significantly reduce the processing time of controlling a prosthesis and eliminate substantial delay. The proposed in-socket sensors used to detect sit-to-stand and stand-to-sit movements could be further integrated with an active knee joint actuation system to produce powered assistance during energy-demanding activities such as sit-to-stand and stair climbing. In future, the system could also be used to accurately predict the intended movement based on their residual limb’s muscle and mechanical behaviour as detected by the in-socket sensory system.
Local wave speed is a biomarker which provides an objective analysis of the cardiovascular function. The aim of this study was to determine the local wave speed in the internal carotid artery by a new non-invasive method that measures blood velocity waveform at only one site.
For this purpose, the cepstral analysis was employed to determine the arrival time of the reflection wave and the wave speed in the carotid artery. To validate our model, we applied it experimentally in vivo on young and old healthy subjects. The blood velocity waveform was measured by using phase-contrast magnetic resonance for 22 subjects.
Our experimental results correlated with reference values reported in previous studies conducted on the internal arterial carotid usually adopting the invasive method. They also correlated with those obtained by using the foot-to-foot method (R2=0.72). The wave speed obtained by the method developed in this study and that of the foot-to-foot method increased with age (p<0.001).
The method developed in this study can be applied in the other arteries and it can also be used with other techniques such as ultrasound imaging.
This study applied the posturography framework on five static standing tasks from the Berg Balance Scale (BBS). Thirteen participants were recruited and the trajectory data of the center of pressure (CoP) were collected. To analyze the postural performance, two approaches were taken: the scores from the BBS and statistical analysis. For the statistical analysis, Spearman’s method was applied to determine the correlation of CoP parameters. The results revealed the correlations between CoP parameters in the anterior-posterior (AP) and medial-lateral (ML) directions, and on the statokinesgram (SK) plane for all tasks. To obtain the in-depth detail between normal weight and overweight groups, the differences in the postural control mechanism were defined by correlations of CoP parameters. The Mann-Whitney U test was conducted to define the difference in postural control in terms of difference in weight gain and standing task factors, while Cohen’s d was used to investigate the influence of the difference in standing tasks and weight gain on postural control. The results showed that the correlations of CoP parameters could distinguish the balance impairment in the overweight condition from the normal postural control. Otherwise, the scores of BBS, the Mann-Whitney U test and Cohen’s d did not separate this slightly compensatory movement during equilibrium. Therefore, the correlations of CoP parameters could provide more information to analyze the balance function in each individual, especially in terms of slight compensation.
Cerebral blood flow (CBF) assessment is mainly performed by scintigraphy, computed tomography (CT) and magnetic resonance imaging (MRI). New approaches to assess the CBF through the passage of magnetic nanoparticles (MNPs) to blood-brain barrier (BBB) are convenient to help decrease the use of ionizing radiation and unleash the required MRI schedule in clinics. The development of nanomedicine and new biomedical devices, such as the magnetic particle imaging (MPI), enabled new approaches to study dynamic brain blood flow. In this paper, we employed MNPs and the alternating current biosusceptometry (ACB) to study the brain perfusion. We utilized the mannitol, before the MNPs, injection to modulate the BBB permeability and study its effects on the circulation time of the MNPs in the brain of rats. Also, we characterized a new ACB sensor to increase the systems’ applicability to study the MNPs’ accumulation, especially in the animals’ brain. Our data showed that the injection of mannitol increased the circulation time of MNPs in the brain. Also, the mannitol increased the accumulation of MNPs in the brain. This paper suggests the use of the ACB as a tool to study brain perfusion and accumulation of MNPs in studies of new nano agents focused on the brain diagnostics and treatment.
Our proposed research technique intends to provide an effective liver magnetic resonance imaging (MRI) and computed tomography (CT) scan image classification which would play a significant role in medical dataset especially in feature selection and classification. There are a number of existing research works classifying the liver tumor disease. Early detection of liver tumor will help the patients to get cured rapidly. Our proposed research focuses on the classification of medical images with respect to the classification technique artificial neural network (ANN) to classify an image as normal or abnormal. In the pre-processing step, the input image is selected from the database and adaptive median filtering is used for noise removal. For better enhancement, histogram equalization (HE) is done in the noise-removed images. In the pre-processed images, the texture feature such as gray-level co-occurrence matrix (GLCM) and statistical features are extracted. From the extensive feature set, optimal features are selected using the optimal kernel K-means (OKK-means) clustering algorithm along with the oppositional firefly algorithm (OFA). The proposed method obtained 97.5% accuracy in the classification when compared to the existing method.