Recent advancements in supercapacitor technology

Thanks to their remarkable mechanical, electrical, thermal, and barrier properties, graphene-based nanocomposites have been a hot area of research in the past decade. Because of their simple top-down synthesis, graphene oxide (GO) and reduced graphene oxide (rGO) have opened new possibilities for gas barrier, membrane separation, and stimuli-response characteristics in nanocomposites. Herein, we review the synthesis techniques most commonly used to produce these graphene derivatives, discuss how synthesis affects their key material properties, and highlight some examples of nanocomposites with unique and impressive properties. We specifically highlight their performances in separation applications, stimuli-responsive materials, anti-corrosion coatings, and energy storage. Finally, we discuss the outlook and remaining challenges in the field of practical industrial-scale production and use of graphene-derivative-based polymer nanocomposites.

Synthesis, properties, and applications of graphene oxide/reduced graphene oxide and their nanocomposites

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.

https://www.mdpi.com/1424-8220/19/6/1423/pdf

EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges.

A unified algorithm for elementary functions

Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

/pdf/current-status-challenges-and-possible-solutions-of-eeg-4el4wek5td.pdf

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Successful applications of brain-computer interface (BCI) approaches to motor imagery (MI) are still limited. In this paper, we propose a classification framework for MI electroencephalogram (EEG) signals that combines a convolutional neural network (CNN) architecture with a variational autoencoder (VAE) for classification. The decoder of the VAE generates a Gaussian distribution, so it can be used to fit the Gaussian distribution of EEG signals. A new representation of input was developed by combining the time, frequency, and channel information from the EEG signal, and the CNN-VAE method was designed and optimized accordingly for this form of input. In this network, the classification of the extracted CNN features is performed via the deep network VAE. Our framework, with an average kappa value of 0.564, outperforms the best classification method in the literature for BCI Competition IV dataset 2b with a 3% improvement. Furthermore, using our own dataset, the CNN-VAE framework also yields the best performance for both three-electrode and five-electrode EEGs and achieves the best average kappa values 0.568 and 0.603, respectively. Our results show that the proposed CNN-VAE method raises performance to the current state of the art.

EEG Classification of Motor Imagery Using a Novel Deep Learning Framework.

This paper presents a capacitive rotary encoder for both angular position and angular speed measurements. The encoder is mainly composed of three parts: the transmitting segments; a pair of reflecting electrodes; and a pair of receiving electrodes. The transmitting segments together with four mutual quadrature carrier voltages provide a modulated electric field. The reflecting electrodes, which are patterned sinusoidally can encode the angular position to a phase/frequency modulated signal based on quadrature modulation. The modulated signal is then digitally decoded to the angular position in a field programmable gate array processor based on the quadrature demodulation and the coordinate rotational digital computer algorithm. Through a universal serial bus, the digital angular position is transmitted to a computer for further analysis in National Instruments' LabVIEW software. A prototype of the capacitive encoder shows that its precision is better than 0.006° and the resolution is 0.002°. The dynamic nonlinearity is evaluated at ±0.4° when the rotor is rotating at 1000 r/min.

A Capacitive Rotary Encoder Based on Quadrature Modulation and Demodulation

Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods.

/pdf/transfer-kernel-common-spatial-patterns-for-motor-imagery-18zkhmdqwe.pdf

Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification.

Acoustic communication is important for the reproductive behaviour of frogs. Using acoustic playback experiments, Shen et al. show that calls from male concave-eared frogs (Odorrana tormota) evoke vocal responses and phonotaxis from females, but the females show no ultrasonic sensitivity.

/pdf/ultrasonic-frogs-show-extraordinary-sex-differences-in-wbfgpoh47q.pdf

Ultrasonic frogs show extraordinary sex differences in auditory frequency sensitivity

A novel approach to improve the specific capacitance of reduced graphene oxide (rGO) films is reported. We combine the aqueous dispersion of liquid-crystalline GO incorporating salt and urea with a blade-coating technique to make hybrid films. After drying, stacked GO sheets mediated by solidified NaCl and urea are hydrothermally reduced, resulting in a nanoporous film consisting of rumpled N-doped rGO sheets. As a supercapacitor electrode, the film exhibits a high gravimetric specific capacitance of 425 F g–1 and a record volumetric specific capacitance of 693 F cm–3 at 1 A g–1 in 1 M H2SO4 aqueous electrolyte when integrated into a symmetric cell. When using Li2SO4 aqueous electrolyte, which can extend the potential window to 1.6 V, the device exhibits high energy densities up to 35 Wh kg–1, and high power densities up to 104 W kg–1. This novel strategy to intercalate solidified chemicals into stacked GO sheets to functionalize them and prevent them from restacking provides a promising route toward supe...

Dezhi Zheng

Papers

EEG Classification of Motor Imagery Using a Novel Deep Learning Framework.

A Capacitive Rotary Encoder Based on Quadrature Modulation and Demodulation

Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification.

Ultrasonic frogs show extraordinary sex differences in auditory frequency sensitivity

Supercapacitor Electrodes with Remarkable Specific Capacitance Converted from Hybrid Graphene Oxide/NaCl/Urea Films