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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.

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.  = 1.3 -1.55 µm) 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 at  = 1.55 µm illumination.Hybrid upconversion Er 3+ -doped NaYF 4 nanocrystal/LT-GaAs photoconductive switch was fabricated. Under a continuous wave (CW) λ = 1.55µm laser illumination (power density  12.9 mW/µm²), 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 (UCNPs). 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.

The development of field‐effect transistor‐based (FET‐based) non‐volatile optoelectronic memories is vital toward innovations necessary to improve computer systems. In this work, for the first time, the unique charge‐trapping and charge‐retention properties of solution‐processed colloidal nitrogen‐doped carbon quantum dots (CQDs) are harnessed to achieve functional optoelectronic memories programmable by UV illumination with a multilevel writing possibility. Of particular note, long‐lasting memory function can be achieved thanks to the vast charge trapping sites provided by the N‐doped CQDs and the resultant photo‐gating effect is exercised on the graphene FET. The achieved memory can be erased by a positive gate bias which provides sufficient carriers to remove trapped charges through recombination. This study highlights the possibility to engineer high‐performance all‐carbon non‐volatile FET‐based optoelectronic memories through manipulating and coupling the charge‐trapping properties of colloidal CQDs and graphene.