Sabotage Detection Using DL Models on EEG Data From a Cognitive-Motor Integration Task.

Objective clinical tools, including cognitive-motor integration (CMI) tasks, have the potential to improve concussion rehabilitation by helping to determine whether or not a concussion has occurred. In order to be useful, however, an individual must put forth their best effort. In this study, we have proposed a novel method to detect the difference in cortical activity between best effort (no-sabotage) and willful under-performance (sabotage) using a deep learning (DL) approach on the electroencephalogram (EEG) signals. The EEG signals from a wearable four-channel headband were acquired during a CMI task. Each participant completed sabotage and no-sabotage conditions in random order. A multi-channel convolutional neural network with long short term memory (CNN-LSTM) model with self-attention has been used to perform the time-series classification into sabotage and no-sabotage, by transforming the time-series into two-dimensional (2D) image-based scalogram representations. This approach allows the inspection of frequency-based, and temporal features of EEG, and the use of a multi-channel model facilitates in capturing correlation and causality between different EEG channels. By treating the 2D scalogram as an image, we show that the trained CNN-LSTM classifier based on automated visual analysis can achieve high levels of discrimination and an overall accuracy of 98.71% in case of intra-subject classification, as well as low false-positive rates. The average intra-subject accuracy obtained was 92.8%, and the average inter-subject accuracy was 86.15%. These results indicate that our proposed model performed well on the data of all subjects. We also compare the scalogram-based results with the results that we obtained by using raw time-series, showing that scalogram-based gave better performance. Our method can be applied in clinical applications such as baseline testing, assessing the current state of injury and recovery tracking and industrial applications like monitoring performance deterioration in workplaces.

Colloidal upconversion nanocrystals enable low-temperature-grown GaAs photoconductive switch operating atλ = 1.55µm.

Microwave photoconductive switches, allowing an optical control on the magnitude and phase of the microwave signals to be transmitted, are important components for many optoelectronic applications. In recent years, there are significant demands to develop photoconductive switches functional in the short-wave-infrared spectrum window (e.g.λ = 1.3-1.55µm) but most state-of-the-art semiconductors for photoconductive switches cannot achieve this goal. In this work, we propose a novel approach, by the use of solution-processed colloidal upconversion nanocrystals deposited directly onto low-temperature-grown gallium arsenide (LT-GaAs), to achieve microwave photoconductive switches functional atλ = 1.55µm illumination. Hybrid upconversion Er3+-doped NaYF4nanocrystal/LT-GaAs photoconductive switch was fabricated. Under a continuous waveλ = 1.55µm laser illumination (power density âˆ¼ 12.9 mWµm-2), thanks to the upconversion energy transfer from the nanocrystals, a more than 2-fold larger value in decibel was measured for the ON/OFF ratio on the hybrid nanocrystal/LT-GaAs device by comparison to the control device without upconversion nanoparticles. A maximum ON/OFF ratio reaching 20.6 dB was measured on the nanocrystal/LT-GaAs hybrid device at an input signal frequency of 20 MHz.

Effect of organic selenium supplementation on growth, Se uptake, and nutrient utilization in guinea pigs.

Forty weaned male guinea pigs (Cavia porcellus) of 152.6 +/- 7.96 g mean body weight were divided into four equal groups and fed a common basal diet comprised of 25% ground cowpea (Vigna unguiculata) hay, 30% ground maize (Zea mays) grain, 22% ground gram (Cicer arietinum) grain, 9.5% deoiled rice (Oryza sativa) bran, 6% soybean (Glycine max) meal, 6% fish meal, 1.5% mineral mixture (without Se), and ascorbic acid at 200 mg/kg to meet their nutrient requirements along with 0, 0.1, 0.2, and 0.3 ppm of organic selenium (Se) in groups I, II, III, and IV, respectively. Experimental feeding lasted for a period of 10 weeks, during which, daily feed intake and weekly body weights were recorded. Intake and digestibility of dry matter, organic matter, ether extract, crude fiber, and nitrogen-free extract as well as uptake of calcium and phosphorus were similar (P > 0.05) among the four groups. Feed:gain ratio was also similar (P > 0.05) in the four groups. However, digestibility of crude protein was significantly (P < 0.001) higher in group II supplemented with 0.1 ppm organic Se as compared to other three group. Intake and absorption of Se was significantly (P < 0.001) higher in all the Se supplemented groups as compared to control group. Average daily gain (ADG) was significantly (P < 0.05) higher in group II (3.16 g/day) and III (3.38 g/day) as compared to group I (2.88 g/day). However, ADG in group IV (supplemented 0.3 ppm organic Se) was significantly (P < 0.05) lower (2.83 g/day) than group II and III, but comparable (P > 0.05) to group I. Findings of the present experiment suggests that Se requirements of guinea pigs are > or =0.2 ppm, as supplementation of 0.1 ppm organic Se in the diet (having 0.1 ppm Se) not only enhanced their growth rate but also improved the protein utilization.