EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals.

Convolutional neural networks (CNN) have been frequently used to extract subject-invariant features from electroencephalogram (EEG) for classification tasks. This approach holds the underlying assumption that electrodes are equidistant analogous to pixels of an image and hence fails to explore/exploit the complex functional neural connectivity between different electrode sites. We overcome this limitation by tailoring the concepts of convolution and pooling applied to 2D grid-like inputs for the functional network of electrode sites. Furthermore, we develop various graph neural network (GNN) models that project electrodes onto the nodes of a graph, where the node features are represented as EEG channel samples collected over a trial, and nodes can be connected by weighted/unweighted edges according to a flexible policy formulated by a neuroscientist. The empirical evaluations show that our proposed GNN-based framework outperforms standard CNN classifiers across ErrP, and RSVP datasets, as well as allowing neuroscientific interpretability and explainability to deep learning methods tailored to EEG related classification problems. Another practical advantage of our GNN-based framework is that it can be used in EEG channel selection, which is critical for reducing computational cost, and designing portable EEG headsets.

Proposal and Evaluation of Visual Haptics for Manipulation of Remote Machine System.

Remote machine systems have drawn a lot of attention owing to accelerations of virtual reality (VR), augmented reality (AR), and the fifth generation (5G) networks. Despite recent trends of developing autonomous systems, the realization of sophisticated dexterous hand that can fully replace human hands is considered to be decades away. It is also extremely difficult to reproduce the sensilla of complex human hands. On the other hand, it is known that humans can perceive haptic information from visual information even without any physical feedback as cross modal sensation between visual and haptics sensations or pseudo haptics. In this paper, we propose a visual haptic technology, where haptic information is visualized in more perceptual images overlaid at the contact points of a remote machine hand. The usability of the proposed visual haptics was evaluated by subject's brain waves aiming to find out a new approach for quantifying "sense of oneness." In our proof-of-concept experiments using VR, subjects are asked to operate a virtual arm and hand presented in the VR space, and the performance of the operation with and without visual haptics information as measured with brain wave sensing. Consequently, three results were verified. Firstly, the information flow in the brain were significantly reduced with the proposed visual haptics for the whole α, ß, and θ-waves by 45% across nine subjects. This result suggests that superimposing visual effects may be able to reduce the cognitive burden on the operator during the manipulation for the remote machine system. Secondly, high correlation (Pearson correlation factor of 0.795 at a p-value of 0.011) was verified between the subjective usability points and the brainwave measurement results. Finally, the number of the task successes across sessions were improved in the presence of overlaid visual stimulus. It implies that the visual haptics image could also facilitate operators' pre-training to get skillful at manipulating the remote machine interface more quickly.

Cryogenic optical measurements of 12-segment-bonded carbon-fiber-reinforced silicon carbide composite mirror with support mechanism.

A 720 mm diameter 12-segment-bonded carbon-fiber-reinforced silicon carbide (C/SiC) composite mirror has been fabricated and tested at cryogenic temperatures. Interferometric measurements show significant cryogenic deformation of the C/SiC composite mirror, which is well reproduced by a model analysis with measured properties of the bonded segments. It is concluded that the deformation is due mostly to variation in coefficients of thermal expansion among segments. In parallel, a 4-degree-of-freedom ball-bearing support mechanism has been developed for cryogenic applications. The C/SiC composite mirror was mounted on an aluminum base plate with the support mechanism and tested again. Cryogenic deformation of the mirror attributed to thermal contraction of the aluminum base plate via the support mechanism is highly reduced by the support, confirming that the newly developed support mechanism is promising for its future application to large-aperture cooled space telescopes.