Interpersonal Relationship Analysis with Dyadic EEG Signals via Learning Spatial-Temporal Patterns

• URL: http://arxiv.org/abs/2401.03250v1
• Date: Sat, 6 Jan 2024 16:17:58 GMT
• Title: Interpersonal Relationship Analysis with Dyadic EEG Signals via Learning Spatial-Temporal Patterns
• Authors: Wenqi Ji, Fang liu, Xinxin Du, Niqi Liu, Chao Zhou, Mingjin Yu, Guozhen Zhao, Yong-Jin Liu
• Abstract summary: We propose a social relationship analysis framework using patterns derived from dyadic EEG signals. We show that the social relationship type (stranger or friend) between two individuals can be effectively identified through their EEG data.
• Score: 21.082038315707923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interpersonal relationship quality is pivotal in social and occupational contexts. Existing analysis of interpersonal relationships mostly rely on subjective self-reports, whereas objective quantification remains challenging. In this paper, we propose a novel social relationship analysis framework using spatio-temporal patterns derived from dyadic EEG signals, which can be applied to quantitatively measure team cooperation in corporate team building, and evaluate interpersonal dynamics between therapists and patients in psychiatric therapy. First, we constructed a dyadic-EEG dataset from 72 pairs of participants with two relationships (stranger or friend) when watching emotional videos simultaneously. Then we proposed a deep neural network on dyadic-subject EEG signals, in which we combine the dynamic graph convolutional neural network for characterizing the interpersonal relationships among the EEG channels and 1-dimension convolution for extracting the information from the time sequence. To obtain the feature vectors from two EEG recordings that well represent the relationship of two subjects, we integrate deep canonical correlation analysis and triplet loss for training the network. Experimental results show that the social relationship type (stranger or friend) between two individuals can be effectively identified through their EEG data.

Related papers

• Biaxialformer: Leveraging Channel Independence and Inter-Channel Correlations in EEG Signal Decoding for Predicting Neurological Outcomes [1.7656489005289302]
  Accurate decoding of EEG signals requires modeling of both temporal dynamics within individual channels and spatial dependencies across channels.<n> Transformer-based models utilizing channel-independence (CI) strategies have demonstrated strong performance in various time series tasks.<n>We propose Biformer, characterized by a meticulously engineered two-stage attention-based framework.
  arXiv  Detail & Related papers  (2025-06-18T11:47:26Z)
• Decoding the Stressed Brain with Geometric Machine Learning [3.8982812950782253]
  Stress contributes to both mental and physical disorders.<n>In this study, we introduce a novel framework that employs geometric machine learning to detect stress from raw recordings.
  arXiv  Detail & Related papers  (2025-05-31T14:47:48Z)
• Shared Attention-based Autoencoder with Hierarchical Fusion-based Graph Convolution Network for sEEG SOZ Identification [10.413397851804152]
  Diagnosing seizure onset zone (SOZ) is a challenge in neurosurgery, where stereoelectroencephalography (sEEG) serves as a critical technique. In sEEG SOZ identification, the existing studies focus solely on the intra-patient representation of epileptic information. We propose the shared attention-based autoencoder (sATAE) to address the aforementioned challenges.
  arXiv  Detail & Related papers  (2024-12-17T08:20:02Z)
• Who Walks With You Matters: Perceiving Social Interactions with Groups for Pedestrian Trajectory Prediction [14.009392073139441]
  This work proposes the GrouP ConCeption (short for GPCC) model composed of the Group method and the Conception module. The GPCC model demonstrates significant improvements in trajectory prediction accuracy, validating its effectiveness in modeling both social and individual dynamics.
  arXiv  Detail & Related papers  (2024-12-03T11:47:33Z)
• Dynamic EEG-fMRI mapping: Revealing the relationship between brain connectivity and cognitive state [0.0]
  This study investigated the dynamic connectivity patterns between EEG and fMRI modalities, contributing to our understanding of brain network interactions. The results revealed modular organization within the intrinsic connectivity networks (ICNs) of the brain, highlighting the significant roles of sensory systems and the default mode network.
  arXiv  Detail & Related papers  (2024-11-29T18:36:58Z)
• Visual-Geometric Collaborative Guidance for Affordance Learning [63.038406948791454]
  We propose a visual-geometric collaborative guided affordance learning network that incorporates visual and geometric cues. Our method outperforms the representative models regarding objective metrics and visual quality.
  arXiv  Detail & Related papers  (2024-10-15T07:35:51Z)
• Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learning [28.681229869236393]
  longitudinal neuroimaging studies aim to improve the understanding of brain aging and diseases by studying the dynamic interactions between brain function and cognition. We propose an unsupervised learning model that encodes their relationship via Graph Attention Networks and generalized Correlational Analysis. To create brain-cognition fingerprints reflecting unique neural and cognitive phenotype of each person, the model also relies on individualized and multimodal contrastive learning.
  arXiv  Detail & Related papers  (2024-09-20T20:36:20Z)
• Towards a Unified Transformer-based Framework for Scene Graph Generation and Human-object Interaction Detection [116.21529970404653]
  We introduce SG2HOI+, a unified one-step model based on the Transformer architecture. Our approach employs two interactive hierarchical Transformers to seamlessly unify the tasks of SGG and HOI detection. Our approach achieves competitive performance when compared to state-of-the-art HOI methods.
  arXiv  Detail & Related papers  (2023-11-03T07:25:57Z)
• Graph AI in Medicine [9.733108180046555]
  Graph neural networks (GNNs) process data holistically by viewing modalities as nodes interconnected by their relationships. GNNs capture information through localized neural transformations defined on graph relationships. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge.
  arXiv  Detail & Related papers  (2023-10-20T19:01:01Z)
• MedNgage: A Dataset for Understanding Engagement in Patient-Nurse Conversations [4.847266237348932]
  Patients who effectively manage their symptoms often demonstrate higher levels of engagement in conversations and interventions with healthcare practitioners. It is crucial for AI systems to understand the engagement in natural conversations between patients and practitioners to better contribute toward patient care. We present a novel dataset (MedNgage) which consists of patient-nurse conversations about cancer symptom management.
  arXiv  Detail & Related papers  (2023-05-31T16:06:07Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• Spatio-Temporal Interaction Graph Parsing Networks for Human-Object Interaction Recognition [55.7731053128204]
  In given video-based Human-Object Interaction scene, modeling thetemporal relationship between humans and objects are the important cue to understand the contextual information presented in the video. With the effective-temporal relationship modeling, it is possible not only to uncover contextual information in each frame but also directly capture inter-time dependencies. The full use of appearance features, spatial location and the semantic information are also the key to improve the video-based Human-Object Interaction recognition performance.
  arXiv  Detail & Related papers  (2021-08-19T11:57:27Z)
• Deep sr-DDL: Deep Structurally Regularized Dynamic Dictionary Learning to Integrate Multimodal and Dynamic Functional Connectomics data for Multidimensional Clinical Characterizations [5.200461964737113]
  We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography. Our framework couples a generative model of the connectomics data with a deep network that predicts behavioral scores. Our hybrid model outperforms several state-of-the-art approaches at clinical outcome prediction and learns interpretable multimodal neural signatures of brain organization.
  arXiv  Detail & Related papers  (2020-08-27T23:43:56Z)
• DRG: Dual Relation Graph for Human-Object Interaction Detection [65.50707710054141]
  We tackle the challenging problem of human-object interaction (HOI) detection. Existing methods either recognize the interaction of each human-object pair in isolation or perform joint inference based on complex appearance-based features. In this paper, we leverage an abstract spatial-semantic representation to describe each human-object pair and aggregate the contextual information of the scene via a dual relation graph.
  arXiv  Detail & Related papers  (2020-08-26T17:59:40Z)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

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.