Fuze Tian
Biographic Data
| ID | 10054387 |
|---|---|
| NAME | Fuze Tian |
| GIVEN NAMES | Fuze |
| FAMILY NAME | Tian |
| SIGNATURE | TIAN F |
| AFFILIATIONS | Lanzhou University |
| ORCID | 0000-0001-6734-0585 |
| VERIFIED | Yes |
| TOTAL WORKS | 9 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 9 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Dynamic Evolution of Prefrontal Neural Activity in Depression
Depression is a common emotional disorder in modern society that causes growing burdens globally. This mental disorder has been frequently confirmed to be closely related to abnormalities in the prefrontal cortex (PFC). However, it remains to be fully investigated whether the neural activity in the PFC has regular quasi-steady spatiotemporal structures, and whether dynamic patterns of these structures are associated with prefrontal dysfunction in…
Decoupled Multimodal Fusion Network Based on Peripheral Physiological Signals
Multimodal peripheral physiological signal fusion for emotion recognition seeks to perceive or recognize human emotions using peripheral modalities such as electromyography, electrodermal activity, and respiratory wave. Previous approaches to multimodal fusion primarily focus on emotion-sensitive signals such as electroencephalogram (EEG), often overlooking the potential value of peripheral physiological signals in emotion recognition. Moreover, …
Hyper-Parallel Superscalar Asynchronous Risc-V Processor Based on Event-Driven Logic
Event-driven neuromorphic computing involves sparse and asynchronous signal activity, which leads to irregular computation patterns and fine-grained concurrency. As a result, processing architectures need to support both high parallelism and energy efficiency. Among existing architectural solutions, superscalar designs exhibit significant potential for addressing high parallelism demands. However, conventional superscalar processors, which rely o…
Semantic Disentangling for Audiovisual Induced Emotion
Emotions regulation play an important role in human behavior, but exhibit considerable heterogeneity among individuals, which attenuates the generalization ability of emotion models. In this work, we aim to achieve robust emotion prediction through efficient disentanglement of affective semantic representations. In detail, the data generation mechanism behind observations from different perspectives is causally set, where latent variables that re…
An EEG-Based Positive Feedback Mechanism for VR Mindfulness Meditation to Improve Emotion Regulation
Virtual reality (VR) mindfulness meditation has emerged as a prominent emotion regulation strategy in recent years. Current research often seeks to enhance meditation effectiveness through biofeedback and overlooks the trajectory of emotional changes and the changing needs during regulation. In this study, we propose an electroencephalography (EEG)–based positive feedback mechanism for VR mindfulness meditation aimed at optimizing the effects of …
Exploring the Alleviating Effects of taVNS on Negative Emotions
Emotion inhibitory control is a key executive function of the human brain, which regulates behavior by suppressing inappropriate responses. It plays an integral part in alleviating negative emotions, improving mood, and preventing depression. Transcutaneous auricular vagus nerve stimulation (taVNS) has been proved to enhance behavioral control, potentially suppressing negative emotions or facilitating their reduction in healthy individuals. Howev…
Physiological Electrosignal Asynchronous Acquisition Technology
With great pride and enthusiasm, we present the inaugural edition of IEEE Transactions on Computational Social Systems (TCSS) for 2024. Reflecting on the year gone by, 2023 stands as a hallmark of academic excellence and prolific output, wherein our journal has successfully disseminated a substantial volume of scholarly work—301 articles encompassing approximately 3600 pages, distributed across six distinct issues
Adaptive Weight and Wasserstein Distance Constrained Low-Rank Sparse Representation Method for Functional Connectivity Network Estimation
Functional connectivity (FC) network derived from resting-state functional magnetic resonance imaging (rs-fMRI) has been extensively employed in the automated identification of brain disorders. Conventional FC network modeling methods typically assign equal importance to different sampling points of rs-fMRI and brain regions of interest (ROIs). Nevertheless, considering temporal and regional heterogeneity, this assumption may not always be applic…
Advancements in Affective Disorder Detection
Currently, the integration of artificial intelligence (AI) techniques with multimodal physiological signals represents a pivotal approach to detect affective disorders (ADs). With the increasing complexity and diversity of physiological signal modalities, researchers have introduced various AI methods using multimodal physiological signals to improve model classification performance and explainability to increase trust and facilitate clinical ado…
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Physiological Electrosignal Asynchronous Acquisition Technology
With great pride and enthusiasm, we present the inaugural edition of IEEE Transactions on Computational Social Systems (TCSS) for 2024. Reflecting on the year gone by, 2023 stands as a hallmark of academic excellence and prolific output, wherein our journal has successfully disseminated a substantial volume of scholarly work—301 articles encompassing approximately 3600 pages, distributed across six distinct issues
Adaptive Weight and Wasserstein Distance Constrained Low-Rank Sparse Representation Method for Functional Connectivity Network Estimation
Functional connectivity (FC) network derived from resting-state functional magnetic resonance imaging (rs-fMRI) has been extensively employed in the automated identification of brain disorders. Conventional FC network modeling methods typically assign equal importance to different sampling points of rs-fMRI and brain regions of interest (ROIs). Nevertheless, considering temporal and regional heterogeneity, this assumption may not always be applic…
Advancements in Affective Disorder Detection
Currently, the integration of artificial intelligence (AI) techniques with multimodal physiological signals represents a pivotal approach to detect affective disorders (ADs). With the increasing complexity and diversity of physiological signal modalities, researchers have introduced various AI methods using multimodal physiological signals to improve model classification performance and explainability to increase trust and facilitate clinical ado…
Semantic Disentangling for Audiovisual Induced Emotion
Emotions regulation play an important role in human behavior, but exhibit considerable heterogeneity among individuals, which attenuates the generalization ability of emotion models. In this work, we aim to achieve robust emotion prediction through efficient disentanglement of affective semantic representations. In detail, the data generation mechanism behind observations from different perspectives is causally set, where latent variables that re…
An EEG-Based Positive Feedback Mechanism for VR Mindfulness Meditation to Improve Emotion Regulation
Virtual reality (VR) mindfulness meditation has emerged as a prominent emotion regulation strategy in recent years. Current research often seeks to enhance meditation effectiveness through biofeedback and overlooks the trajectory of emotional changes and the changing needs during regulation. In this study, we propose an electroencephalography (EEG)–based positive feedback mechanism for VR mindfulness meditation aimed at optimizing the effects of …
Exploring the Alleviating Effects of taVNS on Negative Emotions
Emotion inhibitory control is a key executive function of the human brain, which regulates behavior by suppressing inappropriate responses. It plays an integral part in alleviating negative emotions, improving mood, and preventing depression. Transcutaneous auricular vagus nerve stimulation (taVNS) has been proved to enhance behavioral control, potentially suppressing negative emotions or facilitating their reduction in healthy individuals. Howev…
Dynamic Evolution of Prefrontal Neural Activity in Depression
Depression is a common emotional disorder in modern society that causes growing burdens globally. This mental disorder has been frequently confirmed to be closely related to abnormalities in the prefrontal cortex (PFC). However, it remains to be fully investigated whether the neural activity in the PFC has regular quasi-steady spatiotemporal structures, and whether dynamic patterns of these structures are associated with prefrontal dysfunction in…
Decoupled Multimodal Fusion Network Based on Peripheral Physiological Signals
Multimodal peripheral physiological signal fusion for emotion recognition seeks to perceive or recognize human emotions using peripheral modalities such as electromyography, electrodermal activity, and respiratory wave. Previous approaches to multimodal fusion primarily focus on emotion-sensitive signals such as electroencephalogram (EEG), often overlooking the potential value of peripheral physiological signals in emotion recognition. Moreover, …
Hyper-Parallel Superscalar Asynchronous Risc-V Processor Based on Event-Driven Logic
Event-driven neuromorphic computing involves sparse and asynchronous signal activity, which leads to irregular computation patterns and fine-grained concurrency. As a result, processing architectures need to support both high parallelism and energy efficiency. Among existing architectural solutions, superscalar designs exhibit significant potential for addressing high parallelism demands. However, conventional superscalar processors, which rely o…
Computer Science (6 works) · EEG and Brain-Computer Interfaces (6 works) · Psychology (5 works) · Emotion and Mood Recognition (4 works) · Advanced Memory and Neural Computing (3 works) · Artificial Intelligence (3 works) · Cognitive psychology (3 works) · Electroencephalography (3 works) · Neuroscience (3 works) · Artificial neural network (2 works)