A general dual-pathway network for EEG denoising

IntroductionScalp electroencephalogram (EEG) analysis and interpretation are crucial for tracking and analyzing brain activity. The collected scalp EEG signals, however, are weak and frequently tainted with various sorts of artifacts. The models based on deep learning provide comparable performance with that of traditional techniques. However, current deep learning networks applied to scalp EEG noise reduction are large in scale and suffer from overfitting.MethodsHere, we propose a dual-pathway autoencoder modeling framework named DPAE for scalp EEG signal denoising and demonstrate the superiority of the model on multi-layer perceptron (MLP), convolutional neural network (CNN) and recurrent neural network (RNN), respectively. We validate the denoising performance on benchmark scalp EEG artifact datasets.ResultsThe experimental results show that our model architecture not only significantly reduces the computational effort but also outperforms existing deep learning denoising algorithms in root relative mean square error (RRMSE)metrics, both in the time and frequency domains.DiscussionThe DPAE architecture does not require a priori knowledge of the noise distribution nor is it limited by the network layer structure, which is a general network model oriented toward blind source separation

Highly Selective Transformation of Biomass Derivatives to Valuable Chemicals by Single-Atom Photocatalyst Ni/TiO2

Selective C-C cleavage of the biomass derivative glycerol under mild conditions has been recognised as a promising yet challenging synthesis route to produce value-added chemicals. Here, a highly selective catalyst is presented for the transformation of glycerol to the high-value product glycolaldehyde, which is composed of nickel single atoms confined to the titanium dioxide surface. Driven by light, the catalyst operates under ambient conditions using air as a green oxidant. The optimised catalyst shows a selectivity of over 60% to glycolaldehyde, resulting in 1058 μmol·gCat -1 ·h-1 production rate, and nearly 3 times higher turnover number than NiOx nanoparticle-decorated TiO2 photocatalyst. Diverse operando and in-situ spectroscopies (including operando XANES, in-situ XPS, O2 -TPD, EXAFS, etc.) unveil the unique function of the Ni single atom, which can significantly promote oxygen adsorption, work as an electron sink and accelerate the production of superoxide radicals, thereby improving the selectivity towards glycolaldehyde over other by-products. This article is protected by copyright. All rights reserved