A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification.

One of the challenges in motor imagery (MI) classification tasks is finding an easy-handled electroencephalogram (EEG) representation method which can preserve not only temporal features but also spatial ones. To fully utilize the features on various dimensions of EEG, a novel MI classification framework is first introduced in this paper, including a new 3D representation of EEG, a multi-branch 3D convolutional neural network (3D CNN) and the corresponding classification strategy. The 3D representation is generated by transforming EEG signals into a sequence of 2D array which preserves spatial distribution of sampling electrodes.

Surface organization, light-driven surface changes, and stability of semifluorinated azobenzene polymers.

A series of polymers with 4-perfluoroalkyl-modified azobenzene side groups was investigated for its light-induced changes in surface properties. The ultraviolet (UV) light activated trans to cis isomerization of the azobenzene group, and the influence of molecular order and orientation on this process were studied using near-edge X-ray absorption fine structure (NEXAFS) spectroscopy and water contact angle measurements. Light-induced molecular reorganization in the near-surface region was studied by NEXAFS using in situ UV irradiation of polymer thin films.