TF-MCL: Time-frequency Fusion and Multi-domain Cross-Loss for Self-supervised Depression Detection

• URL: http://arxiv.org/abs/2512.13736v1
• Date: Sun, 14 Dec 2025 07:53:04 GMT
• Title: TF-MCL: Time-frequency Fusion and Multi-domain Cross-Loss for Self-supervised Depression Detection
• Authors: Li-Xuan Zhao, Chen-Yang Xu, Wen-Qiang Li, Bo Wang, Rong-Xing Wei, Qing-Hao Menga,
• Abstract summary: We propose a time-frequency fusion and multi-domain cross-loss (TF-MCL) model for MDD detection.<n> TF-MCL generates time-frequency hybrid representations through the use of a fusion mapping head (FMH)<n>We show a significant improvement in accuracy, outperforming the existing state-of-the-art (SOTA) method by 5.87% and 9.96%, respectively.
• Score: 9.907470250883003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, there has been a notable increase in the use of supervised detection methods of major depressive disorder (MDD) based on electroencephalogram (EEG) signals. However, the process of labeling MDD remains challenging. As a self-supervised learning method, contrastive learning could address the shortcomings of supervised learning methods, which are unduly reliant on labels in the context of MDD detection. However, existing contrastive learning methods are not specifically designed to characterize the time-frequency distribution of EEG signals, and their capacity to acquire low-semantic data representations is still inadequate for MDD detection tasks. To address the problem of contrastive learning method, we propose a time-frequency fusion and multi-domain cross-loss (TF-MCL) model for MDD detection. TF-MCL generates time-frequency hybrid representations through the use of a fusion mapping head (FMH), which efficiently remaps time-frequency domain information to the fusion domain, and thus can effectively enhance the model's capacity to synthesize time-frequency information. Moreover, by optimizing the multi-domain cross-loss function, the distribution of the representations in the time-frequency domain and the fusion domain is reconstructed, thereby improving the model's capacity to acquire fusion representations. We evaluated the performance of our model on the publicly available datasets MODMA and PRED+CT and show a significant improvement in accuracy, outperforming the existing state-of-the-art (SOTA) method by 5.87% and 9.96%, respectively.

Related papers

• Efficient Flow Matching for Sparse-View CT Reconstruction [7.05503627528473]
  Flow Matching (FM) models exhibit smooth trajectories without noise injection.<n>Motivated by this, we propose an FM-based CT reconstruction framework (FMCT)<n>We show that FMCT/EFMCT achieve competitive reconstruction quality while significantly improving computational efficiency compared with diffusion-based methods.
  arXiv  Detail & Related papers  (2026-02-27T11:13:43Z)
• TAG:Tangential Amplifying Guidance for Hallucination-Resistant Diffusion Sampling [53.61290359948953]
  Tangential Amplifying Guidance (TAG) operates solely on trajectory signals without modifying the underlying diffusion model.<n>We formalize this guidance process by leveraging a first-order Taylor expansion.<n> TAG is a plug-and-play, architecture-agnostic module that improves diffusion sampling fidelity with minimal computational addition.
  arXiv  Detail & Related papers  (2025-10-06T06:53:29Z)
• Improved Diffusion-based Generative Model with Better Adversarial Robustness [65.38540020916432]
  Diffusion Probabilistic Models (DPMs) have achieved significant success in generative tasks.<n>During the denoising process, the input data distributions differ between the training and inference stages.
  arXiv  Detail & Related papers  (2025-02-24T12:29:16Z)
• CoSTI: Consistency Models for (a faster) Spatio-Temporal Imputation [0.0]
  CoSTI employs Consistency Training to achieve comparable imputation quality to DDPMs while drastically reducing inference times.<n>We evaluate CoSTI across multiple datasets and missing data scenarios, demonstrating up to a 98% reduction in imputation time with performance par with diffusion-based models.
  arXiv  Detail & Related papers  (2025-01-31T18:14:28Z)
• A Flow-based Truncated Denoising Diffusion Model for Super-resolution Magnetic Resonance Spectroscopic Imaging [34.32290273033808]
  This work introduces a Flow-based Truncated Denoising Diffusion Model for super-resolution MRSI. It shortens the diffusion process by truncating the diffusion chain, and the truncated steps are estimated using a normalizing flow-based network. We demonstrate that FTDDM outperforms existing generative models while speeding up the sampling process by over 9-fold.
  arXiv  Detail & Related papers  (2024-10-25T03:42:35Z)
• MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent Imputation [41.681869408967586]
  Key research question is how to ensure imputation consistency, i.e., intra-consistency between observed and imputed values. Previous methods rely solely on the inductive bias of the imputation targets to guide the learning process.
  arXiv  Detail & Related papers  (2024-08-11T10:24:53Z)
• Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment [59.75420353684495]
  Machine learning applications on signals such as computer vision or biomedical data often face challenges due to the variability that exists across hardware devices or session recordings. In this work, we propose Spatio-Temporal Monge Alignment (STMA) to mitigate these variabilities. We show that STMA leads to significant and consistent performance gains between datasets acquired with very different settings.
  arXiv  Detail & Related papers  (2024-07-19T13:33:38Z)
• PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection [65.24854366973794]
  Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in domains such as medicine, social networks, and e-commerce. We introduce a simple method termed PREprocessing and Matching (PREM for short) to improve the efficiency of GAD. Our approach streamlines GAD, reducing time and memory consumption while maintaining powerful anomaly detection capabilities.
  arXiv  Detail & Related papers  (2023-10-18T02:59:57Z)
• ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection [44.21198064126152]
  We propose a novel anomaly detection framework named ImDiffusion. ImDiffusion combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets.
  arXiv  Detail & Related papers  (2023-07-03T04:57:40Z)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• Efficient Learning and Decoding of the Continuous-Time Hidden Markov Model for Disease Progression Modeling [119.50438407358862]
  We present the first complete characterization of efficient EM-based learning methods for CT-HMM models. We show that EM-based learning consists of two challenges: the estimation of posterior state probabilities and the computation of end-state conditioned statistics. We demonstrate the use of CT-HMMs with more than 100 states to visualize and predict disease progression using a glaucoma dataset and an Alzheimer's disease dataset.
  arXiv  Detail & Related papers  (2021-10-26T20:06:05Z)
• Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso [102.84915019938413]
  Non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG) offer promise of non-invasive techniques. The problem of source localization, or source imaging, poses however a high-dimensional statistical inference challenge. We propose an ensemble of desparsified multi-task Lasso (ecd-MTLasso) to deal with this problem.
  arXiv  Detail & Related papers  (2020-09-29T21:17:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.