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Mengjie Huang

Publications and source records attributed to Mengjie Huang.

3 recordsLinked to original sources

The Evaluation for Usability Methods of Unmanned Surface Vehicles: Are Current Usability Methods Viable for Unmanned Surface Vehicles? Insights from a Multiple Case Study Approach to Human-Robot Interaction

Unmanned Surface Vehicles (USVs) are increasingly utilised for diverse applications, ranging from environmental monitoring to security patrols. While USV technology is progressing, it remains clear that full autonomy is not achievable in all scenarios, and remote human intervention is still crucial, particularly in dynamic or complex environments. This continued reliance on human intervention highlights a range of Human-Robot Interaction (HRI) challenges that remain unresolved. Compared to the extensive body of HRI research in domains such as unmanned aerial vehicles and autonomous vehicles, HRI considerations specific to USVs remain significantly underexplored. Addressing this gap, our study investigates real-world usability challenges in USV operation through in-depth interviews with 9 engineers and users, supported by field observations. We focus especially on the difficulties beginner operators encounter and their coping strategies. Our findings reveal existing usability issues, mental models, and adaptation strategies of beginners that inform future user-centered design of USV systems, contributing new insights to the emerging field of maritime HRI. Based on these findings, we argue that current USV systems are poorly suited for beginner operation in dynamic inland and offshore environments, where operators must make timely decisions under uncertainty, manage complex spatial awareness, and adapt to changing environmental conditions. Furthermore, we identify key operational patterns in three representative use cases-harmful algal bloom detection, underwater concealed pipe inspection and post-construction hydrographic survey, and summarise key interaction constraints that should inform future maritime HRI design efforts.

cs.HC

EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Towards Large-Scale EEG Model

The increasing number of dispersed EEG dataset publications and the advancement of large-scale Electroencephalogram (EEG) models have increased the demand for practical tools to manage diverse EEG datasets. However, the inherent complexity of EEG data, characterized by variability in content data, metadata, and data formats, poses challenges for integrating multiple datasets and conducting large-scale EEG model research. To tackle the challenges, this paper introduces EEGUnity, an open-source tool that incorporates modules of 'EEG Parser', 'Correction', 'Batch Processing', and 'Large Language Model Boost'. Leveraging the functionality of such modules, EEGUnity facilitates the efficient management of multiple EEG datasets, such as intelligent data structure inference, data cleaning, and data unification. In addition, the capabilities of EEGUnity ensure high data quality and consistency, providing a reliable foundation for large-scale EEG data research. EEGUnity is evaluated across 25 EEG datasets from different sources, offering several typical batch processing workflows. The results demonstrate the high performance and flexibility of EEGUnity in parsing and data processing. The project code is publicly available at github.com/Baizhige/EEGUnity.

eess.SP

BDAN: Mitigating Temporal Difference Across Electrodes in Cross-Subject Motor Imagery Classification via Generative Bridging Domain

Because of "the non-repeatability of the experiment settings and conditions" and "the variability of brain patterns among subjects", the data distributions across sessions and electrodes are different in cross-subject motor imagery (MI) studies, eventually reducing the performance of the classification model. Systematically summarised based on the existing studies, a novel temporal-electrode data distribution problem is investigated under both intra-subject and inter-subject scenarios in this paper. Based on the presented issue, a novel bridging domain adaptation network (BDAN) is proposed, aiming to minimise the data distribution difference across sessions in the aspect of the electrode, thus improving and enhancing model performance. In the proposed BDAN, deep features of all the EEG data are extracted via a specially designed spatial feature extractor. With the obtained spatio-temporal features, a special generative bridging domain is established, bridging the data from all the subjects across sessions. The difference across sessions and electrodes is then minimized using the customized bridging loss functions, and the known knowledge is automatically transferred through the constructed bridging domain. To show the effectiveness of the proposed BDAN, comparison experiments and ablation studies are conducted on a public EEG dataset. The overall comparison results demonstrate the superior performance of the proposed BDAN compared with the other advanced deep learning and domain adaptation methods.

cs.HC