Easing Seasickness through Attention Redirection with a Mindfulness-Based Brain--Computer Interface

Seasickness is a prevalent issue that adversely impacts both passenger experiences and the operational efficiency of maritime crews. While techniques that redirect attention have proven effective in alleviating motion sickness symptoms in terrestrial environments, applying similar strategies to manage seasickness poses unique challenges due to the prolonged and intense motion environment associated with maritime travel. In this study, we propose a mindfulness brain-computer interface (BCI), specifically designed to redirect attention with the aim of mitigating seasickness symptoms in real-world settings. Our system utilizes a single-channel headband to capture prefrontal EEG signals, which are then wirelessly transmitted to computing devices for the assessment of mindfulness states. The results are transferred into real-time feedback as mindfulness scores and audiovisual stimuli, facilitating a shift in attentional focus from physiological discomfort to mindfulness practices. A total of 43 individuals participated in a real-world maritime experiment consisted of three sessions: a real-feedback mindfulness session, a resting session, and a pseudofeedback mindfulness session. Notably, 81.39% of participants reported that the mindfulness BCI intervention was effective, and there was a significant reduction in the severity of seasickness, as measured by the Misery Scale (MISC). Furthermore, EEG analysis revealed a decrease in the theta/beta ratio, corresponding with the alleviation of seasickness symptoms. A decrease in overall EEG band power during the real-feedback mindfulness session suggests that the mindfulness BCI fosters a more tranquil and downregulated state of brain activity. Together, this study presents a novel nonpharmacological, portable, and effective approach for seasickness intervention, with the potential to enhance the cruising experience for both passengers and crews.

Topology-aware Convolutional Neural Network for Efficient Skeleton-based Action Recognition

In the context of skeleton-based action recognition, graph convolutional networks (GCNs) have been rapidly developed, whereas convolutional neural networks (CNNs) have received less attention. One reason is that CNNs are considered poor in modeling the irregular skeleton topology. To alleviate this limitation, we propose a pure CNN architecture named Topology-aware CNN (Ta-CNN) in this paper. In particular, we develop a novel cross-channel feature augmentation module, which is a combo of map-attend-group-map operations. By applying the module to the coordinate level and the joint level subsequently, the topology feature is effectively enhanced. Notably, we theoretically prove that graph convolution is a special case of normal convolution when the joint dimension is treated as channels. This confirms that the topology modeling power of GCNs can also be implemented by using a CNN. Moreover, we creatively design a SkeletonMix strategy which mixes two persons in a unique manner and further boosts the performance. Extensive experiments are conducted on four widely used datasets, i.e. N-UCLA, SBU, NTU RGB+D and NTU RGB+D 120 to verify the effectiveness of Ta-CNN. We surpass existing CNN-based methods significantly. Compared with leading GCN-based methods, we achieve comparable performance with much less complexity in terms of the required GFLOPs and parameters.