Adaptive Temporal Dynamics for Personalized Emotion Recognition: A Liquid Neural Network Approach

• URL: http://arxiv.org/abs/2602.06997v1
• Date: Wed, 28 Jan 2026 12:14:45 GMT
• Title: Adaptive Temporal Dynamics for Personalized Emotion Recognition: A Liquid Neural Network Approach
• Authors: Anindya Bhattacharjee, Nittya Ananda Biswas, K. A. Shahriar, Adib Rahman,
• Abstract summary: This work presents to the best of our knowledge, the first comprehensive application of liquid neural networks for EEG-based emotion recognition.<n>The proposed framework combines convolutional feature extraction, liquid neural networks with learnable time constants, and attention-guided fusion.<n>Subject-dependent experiments conducted on the PhyMER dataset across seven emotional classes achieve an accuracy of 95.45%, surpassing previously reported results.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emotion recognition from physiological signals remains challenging due to their non-stationary, noisy, and subject-dependent characteristics. This work presents, to the best of our knowledge, the first comprehensive application of liquid neural networks for EEG-based emotion recognition. The proposed multimodal framework combines convolutional feature extraction, liquid neural networks with learnable time constants, and attention-guided fusion to model temporal EEG dynamics with complementary peripheral physiological and personality features. Dedicated subnetworks are used to process EEG features and auxiliary modalities, and a shared autoencoder-based fusion module is used to learn discriminative latent representations before classification. Subject-dependent experiments conducted on the PhyMER dataset across seven emotional classes achieve an accuracy of 95.45%, surpassing previously reported results. Furthermore, temporal attention analysis provides interpretable insights into emotion-specific temporal relevance, and t-SNE visualizations demonstrate enhanced class separability, highlighting the effectiveness of the proposed approach. Finally, statistical analysis of temporal dynamics confirms that the network self-organizes into distinct functional groups with specialized fast and slow neurons, proving it independently tunes learnable time constants and memory dominance to effectively capture complex emotion artifacts.

Related papers

• Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition [56.00118641432005]
  We propose a Memory-guided Prototypical Co-occurrence Learning framework that explicitly models emotion co-occurrence patterns.<n>Inspired by human cognitive memory systems, we introduce a memory retrieval strategy to extract semantic-level co-occurrence associations.<n>Our model learns affectively informative representations for accurate emotion distribution prediction.
  arXiv  Detail & Related papers  (2026-02-24T04:11:25Z)
• Fractional Spike Differential Equations Neural Network with Efficient Adjoint Parameters Training [63.3991315762955]
  Spiking Neural Networks (SNNs) draw inspiration from biological neurons to create realistic models for brain-like computation.<n>Most existing SNNs assume a single time constant for neuronal membrane voltage dynamics, modeled by first-order ordinary differential equations (ODEs) with Markovian characteristics.<n>We propose the Fractional SPIKE Differential Equation neural network (fspikeDE), which captures long-term dependencies in membrane voltage and spike trains through fractional-order dynamics.
  arXiv  Detail & Related papers  (2025-07-22T18:20:56Z)
• CAST-Phys: Contactless Affective States Through Physiological signals Database [74.28082880875368]
  The lack of affective multi-modal datasets remains a major bottleneck in developing accurate emotion recognition systems.<n>We present the Contactless Affective States Through Physiological Signals Database (CAST-Phys), a novel high-quality dataset capable of remote physiological emotion recognition.<n>Our analysis highlights the crucial role of physiological signals in realistic scenarios where facial expressions alone may not provide sufficient emotional information.
  arXiv  Detail & Related papers  (2025-07-08T15:20:24Z)
• FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition [1.9198890060313585]
  Cross-subject generalization is a challenge due to individual variability, cognitive traits, and emotional responses.<n>We propose FreqDGT, a frequency-adaptive dynamic graph transformer that addresses these limitations through an integrated framework.<n>FreqDGT significantly improves cross-subject emotion recognition accuracy, confirming the effectiveness of integrating frequency-adaptive, spatial-dynamic, and temporal-hierarchical modeling.
  arXiv  Detail & Related papers  (2025-06-28T08:18:05Z)
• Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition [53.359383163184425]
  We introduce a novel multimodality synergistic knowledge distillation scheme tailored for efficient single-eye motion recognition tasks. This method allows a lightweight, unimodal student spiking neural network (SNN) to extract rich knowledge from an event-frame multimodal teacher network.
  arXiv  Detail & Related papers  (2024-06-20T07:24:47Z)
• Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition [23.505616142198487]
  We develop a Pre-trained model based Multimodal Mood Reader for cross-subject emotion recognition. The model learns universal latent representations of EEG signals through pre-training on large scale dataset. Extensive experiments on public datasets demonstrate Mood Reader's superior performance in cross-subject emotion recognition tasks.
  arXiv  Detail & Related papers  (2024-05-28T14:31:11Z)
• DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks [4.041732967881764]
  Most rs-fMRI studies compute a single static functional connectivity matrix across brain regions of interest. These approaches are at risk of oversimplifying brain dynamics and lack proper consideration of the goal at hand. We propose a novel interpretable deep learning framework that learns goal-specific functional connectivity matrix directly from time series.
  arXiv  Detail & Related papers  (2024-05-19T23:35:06Z)
• Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain [16.62883475350025]
  Graph Neural Networks (GNNs) have shown promise in learning dynamic functional connectivity for distinguishing phenotypes from human brain networks.<n>We introduce the Spatio-Temporal Joint Embedding Masked Autoencoder (ST-JEMA), drawing inspiration from the Joint Embedding Predictive Architecture (JEPA) in computer vision.
  arXiv  Detail & Related papers  (2024-03-11T04:49:41Z)
• Long Short-term Memory with Two-Compartment Spiking Neuron [64.02161577259426]
  We propose a novel biologically inspired Long Short-Term Memory Leaky Integrate-and-Fire spiking neuron model, dubbed LSTM-LIF. Our experimental results, on a diverse range of temporal classification tasks, demonstrate superior temporal classification capability, rapid training convergence, strong network generalizability, and high energy efficiency of the proposed LSTM-LIF model. This work, therefore, opens up a myriad of opportunities for resolving challenging temporal processing tasks on emerging neuromorphic computing machines.
  arXiv  Detail & Related papers  (2023-07-14T08:51:03Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)
• EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters [72.19032452642728]
  We propose a novel differentiable EEG decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. We demonstrate the utility of our model towards emotion recognition from EEG signals on the SEED dataset and on a new EEG dataset of unprecedented size. The discovered features align with previous neuroscience studies and offer new insights, such as marked differences in the functional connectivity profile between left and right temporal areas during music listening.
  arXiv  Detail & Related papers  (2021-10-19T14:22:04Z)
• Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition [8.356765961526955]
  We investigate three functional connectivity network features: strength, clustering, coefficient and eigenvector centrality. The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public EEG datasets. We construct a multimodal emotion recognition model by combining the functional connectivity features from EEG and the features from eye movements or physiological signals.
  arXiv  Detail & Related papers  (2020-04-04T16:51:56Z)

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.