Evaluation of time-domain features for motor imagery movements using FCM and SVM

Brain–Machine Interface is a direct communication pathway between brain and an external electronic device. BMIs aim to translate brain activities into control commands. To design a system that translates brain waves and its activities to desired commands, motor imagery tasks classification is the core part. Classification accuracy not only depends on how capable the classifier is but also it is about the input data. Feature extraction is to highlight the properties of signal that make it distinct from the signal of the other mental tasks. Performance of BMIs directly depends on the effectiveness of the feature extraction and classification algorithms. If a feature provides large interclass difference for different classes, the applied classifier exhibits a better performance. In order to attain less computational complexity, five timedomain procedure, namely: Mean Absolute Value, Maximum peak value, Simple Square Integral, Willison Amplitude, and Waveform Length are used for feature extraction of EEG signals. Two classifiers are applied to assess the performance of each feature-subject. SVM with polynomial kernel is one of the applied nonlinear classifier and supervised FCM is the other one. The performance of each feature for input data are evaluated with both classifiers and classification accuracy is the considered common comparison parameter

Evaluating the effectiveness of time-domain features for motor imagery movements using SVM

Motor imagery electroencephalogram signals are the only bio-signals that enable locked-in patients, who have lost control over every motor output, to communicate with and control their surroundings. Brain Machine Interface is collaboration between a human and machines, which translates brain waves to desired, understandable commands for a machine. Classification of motor imagery tasks for BMIs is the crucial part. Classification accuracy not only depends on how accurate and robust the classifier is; it is also about data. For well separated data, classifiers such as kernel SVM can handle classification and deliver acceptable results. If a feature provides large interclass difference for different classes, immunity to random noise and chaotic behavior of EEG signal is rationally conformed, which means the applied feature is suitable for classifying EEG signals. In this work, in order to have less computational complexity, time-domain algorithms are employed to motor imagery signals. Extracted features are: Mean Absolute Value, Maximum peak value, Simple Square Integral, Willison Amplitude, and Waveform Length. Support Vector Machine with polynomial kernel is applied for classification of four different classes of data. The obtained results show that these features have acceptable, distinct values for different these four motor imagery tasks. Maximum classification accuracy belongs to contribution of Willison amplitude as feature and SVM as classifier, with 95.1 percentages accuracy. Where, the lowest is the contribution of Waveform Length and SVM with 31.67 percentages classification accuracy

Evaluation of treated wastewater irrigation on the productivity of wheat

The major objective of this research work is to propose the initial environmental impact assessment concerning the utilization of treated wastewater for two different varieties of wheat production. The study analyzed the soil chemical composition before and after irrigation at two different depths (0-30 cm & 30-60 cm). Water chemical composition is also analyzed for controlled water, treated water of Abu Dhabi and Al Ain. Wheat plant chemical composition present in the head, root, and shoot for both the varieties is analyzed. The levels of Ca, Mg, Na and CI in soil have increased after irrigation with controlled water. The presence of cations and anions in the soil are slightly higher in the treated water of Abu Dhabi. Ca, Na, CI and SO4 are found to be significantly higher after irrigation with treated wastewater of Al Ain.  The plant chemical composition of head, root and shoot ND fiber, AD fiber, Crude protein and Macro elements have shown no significant differences across the three types of water and two varieties of wheat production. The correlations between RBS limits and the three types of water considered in the study are negative. The results revealed that the differences in chemical composition between RBS limits, controlled, treated wastewater of Abu Dhabi and Al Ain are statistically significant with particular reference to trace and heavy metals. Concerning water chemical composition, the study concludes that the correlation between controlled water and treated wastewater of Al Ain is strong when compared to Abu Dhabi

Developing the attenuation relation for damage spectrum in X-braced steel structures with neural network

Evaluating structural damage, caused by earthquakes, is very important in seismic risk management. Zoning maps of structural damage are directly used in evaluating damage of different zones as well as planning to retrofit structures. Attenuation relation is applied in preparing the acceleration zoning of regions. Similarly, damage attenuation relations which are used in analyzing probabilistic hazard and preparing damage zoning are obtained by structural damage spectrum. This spectrum is nonlinear and designed by considering nonlinear parameters of a series of one-degree-of-freedom structures and time history dynamic analysis. After gathering and modifying 778 records of the earthquakes happened in Iran, the damage spectrum was prepared for X-braced steel structures with different specifications (yield force, hysteresis curves, and ductility capacity). Damage attenuation relation was developed for the structures through regression analysis and the obtained results were compared with those of artificial neural network method. Damage of three samples with different specifications was calculated by the developed attenuation relation. The obtained results were compared with those of time history dynamic analysis. The developed relations were used for analyzing the probabilistic damage risk and preparing the damage zoning maps for city of Qazvin, as a seismic region in Iran

Effect of heat treatment on optical properties of crosslinkable Azo Chromophore doped in poly amic acid

In this work, we have studied the optical properties of a crosslinkable poly amic acid containing Disperse Red 1. The thin films were cured at 130, 160 and 195 °C. The structural and optical properties of the doped films were investigated by using UV-VIS spectra, and Prism Coupling techniques. The composite crosslinks during poling rendering it totally insoluble. A r33 of 1.5 pm/v was obtained after poling

Health-related quality of life and medication adherence in elderly patients with epilepsy

© 2019 Polish Neurological Society. Objective. Considering the high prevalence of epilepsy in the elderly and the importance of maximising their quality of life (QoL), this study aimed to investigate the relationship between medication adherence and QoL, and the mediating effects of medication adherence on the association between serum antiepileptic drug (AED) level and seizure severity with QoL in elderly epileptics. Methods. In a longitudinal study, 766 elderly patients with epilepsy who were prescribed a minimum of one antiepileptic drug were selected by convenience sampling method. A Medication Adherence Report Scale (MARS-5) questionnaire was completed at the baseline. Seizure severity and QoL were assessed after six months using the Liverpool Seizure Severity Scale (LSSS) and the QoL in Epilepsy (QOLIE-31) questionnaires respectively. Serum level of AED was also measured at six-month follow-up. Results. Medication adherence was significantly correlated with both seizure severity (β = -0.33, p < 0.0001) and serum AED level (β = 0.29, p < 0.0001) after adjusting for demographic and clinical characteristics. Neither QoL nor its sub-classes were correlated with seizure severity. In addition, a significant correlation was not observed between serum AED level and QoL. However, medication adherence was significantly correlated with QoL (β = 0.30, p < 0.0001). The mediating effects of medication adherence on the association between serum AED level (Z = 3.39, p < 0.001) and seizure severity (Z = -3.47, p < 0.001) with QoL were supported by the Sobel test. Conclusion. This study demonstrates that medication adherence has a beneficial impact on QoL in elderly epileptics. Therefore, adherence to treatment should be monitored to improve their QoL

A Novel Method (T-Junction with a Tilted Slat) for Controlling Breakup Volume Ratio of Droplets in Micro and Nanofluidic T-Junctions

We propose a novel method for producing unequal sized droplets using a titled slat in the center of a T-junctions. In the available methods for generating unequal-sized droplets such as T-junction with valve and T-junction with a heater, the minimum breakup volume ratio that is accessible is approximately 0.3 while the system of this paper can generate droplets with the volume ratio 0.05. Therefore, the manufacturing cost of the system decreases considerably because it does not need to the consecutive breakup systems for generation of small droplets. The employed method was investigated through a numerical simulation using the volume of fluid (VOF) algorithm. The simulation results are reported for micro and nano-scaled T-junctions in various tilted slat sizes, capillary numbers (a dimensionless group describes the ratio of the inertial forces to the surface tension forces) and slat angles. Our method decreases (increases) considerably the breakup time (speed of the breakup process). For example in the case Ca=0.1 and volume ratio 0.4, dimensionless breakup time of our method and the method of T-junction with valve are 0.25 and 3.6, respectively. The results revealed that the breakup length of the nanoscale T-junction is smaller than microscale and increases by increasing the slat angle in both scales. The results demonstrated the breakup volume ratio decreases by increasing the tilted slat length. Also the breakup volume ratio minimizes in a specific slat angle. The results showed the breakup time is reduced by decreasing the slat angle. We also found that the pressure drop of the system is almost independent of the system geometry

Using Facial Gestures to Drive Narrative in VR

We developed an exploratory VR environment, where spatial features and narratives can be manipulated in real time by the facial and head gestures of the user. We are using the Faceteq prototype, exhibited in 2017, as the interactive interface. Faceteq consists of a wearable technology that can be adjusted on commercial HMDs for measuring facial expressions and biometric responses. Faceteq project was founded with the aim to provide a human-centred additional tool for affective human-computer interaction. The proposed demo will exhibit the hardware and the functionality of the demo in real time