Graph neural networks (GNNs) are gaining increasing popularity for EEG-based depression detection. However, previous GNN-based methods inadequately consider the characteristics of depression, which limit their performance. First, neuroscience studies indicate that patients with depression exhibit both common and individualized brain abnormalities. Previous GNN-based approaches typically focus either on common graph connections to capture common brain abnormalities or on individualized connections to capture individualized patterns, which is insufficient for depression detection. Second, brain network exhibits a hierarchical structure, ranging from channel-level graphs to region-level graphs. This hierarchical structure varies across individuals and contains significant information relevant to detecting depression. However, previous GNN-based methods overlook this individualized hierarchical information. To address these issues, we propose a Hybrid GNN (HybGNN) that combines a Common Graph Neural Network (CGNN) branch using common connections and an Individualized Graph Neural Network (IGNN) branch employing individualized connections. The two branches capture common and individualized depression patterns, respectively, complementing each other. Furthermore, we enhance the HybGNN with a Cross-Branch Hierarchical Information Extractor (CB-HIE) to extract more task-relevant individualized hierarchical information. Extensive experiments on the MODMA and HUSM datasets demonstrate that the proposed HybGNN achieves state-of-the-art performance.

Redirected walking (RDW) and omnidirectional treadmill (ODT) are two effective solutions to the natural locomotion interface in virtual reality. ODT fully compresses the physical space and can be used as the integration carrier of all kinds of devices. However, the user experience varies in different directions of ODT, and the premise of interaction between users and integrated devices is a good match between virtual and real objects. RDW technology uses visual cues to guide the user's location in physical space. Based on this principle, combining RDW technology with ODT to guide the user's walking direction through visual cues can effectively improve user experience on ODT and make full use of various devices integrated on ODT. This paper explores the novel prospects of combining RDW technology with ODT and formally puts forward the concept of O-RDW (ODT-based RDW). Two baseline algorithms, i.e., OS2MD (ODT-based steer to multi-direction), and OS2MT (ODT-based steer to multi-target), are proposed to combine the merits of both RDW and ODT. With the help of the simulation environment, this paper quantitatively analyzes the applicable scenarios of the two algorithms and the influence of several main factors on the performance. Based on the conclusions of the simulation experiments, the two O-RDW algorithms are successfully applied in the practical application case of multi-target haptic feedback. Combined with the user study, the practicability and effectiveness of O-RDW technology in practical use are further verified.

The natural locomotion interface is critical to the development of many VR applications. For household VR applications, there are two basic requirements: natural immersive experience and minimized space occupation. The existing locomotion strategies generally do not simultaneously satisfy these two requirements well. This article presents a novel omnidirectional treadmill (ODT) system named Hex-Core-MK1 (HCMK1). By implementing two kinds of mirror-symmetrical spiral rollers to generate the omnidirectional velocity field, this proposed system is capable of providing real walking experiences with a full-degree of freedom in an area as small as 1.76 m$^{2}$, while delivering great advantages over several existing ODT systems in terms of weight, volume, latency and dynamic performance. Compared with the sizes of Infinadeck and HCP, the two best motor-driven ODTs so far, the 8 cm height of HCMK1 is only 20% of Infinadeck and 50% of HCP. In addition, HCMK1 is a lightweight device weighing only 110 kg, which provides possibilities for further expanding VR scenarios, such as terrain simulation. The system latency of HCMK1 is only 9ms. The experiments show that HCMK1 can deliver a starting acceleration of 16.00 m/s$^{2}$and a braking acceleration of 30.00 m/s$^{2}$.