Unveiling the Heart-Brain Connection: An Analysis of ECG in Cognitive Performance

• URL: http://arxiv.org/abs/2601.01424v1
• Date: Sun, 04 Jan 2026 08:06:19 GMT
• Title: Unveiling the Heart-Brain Connection: An Analysis of ECG in Cognitive Performance
• Authors: Akshay Sasi, Malavika Pradeep, Nusaibah Farrukh, Rahul Venugopal, Elizabeth Sherly,
• Abstract summary: ECG signals can reliably reflect cognitive load and serve as proxies for EEG-based indicators.<n>We propose a cross-modal XGBoost framework to project the ECG features onto EEG-representative cognitive spaces.<n>Our findings underpin ECG as an interpretable, real-time, wearable solution for everyday cognitive monitoring.
• Score: 0.1631115063641726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding the interaction of neural and cardiac systems during cognitive activity is critical to advancing physiological computing. Although EEG has been the gold standard for assessing mental workload, its limited portability restricts its real-world use. Widely available ECG through wearable devices proposes a pragmatic alternative. This research investigates whether ECG signals can reliably reflect cognitive load and serve as proxies for EEG-based indicators. In this work, we present multimodal data acquired from two different paradigms involving working-memory and passive-listening tasks. For each modality, we extracted ECG time-domain HRV metrics and Catch22 descriptors against EEG spectral and Catch22 features, respectively. We propose a cross-modal XGBoost framework to project the ECG features onto EEG-representative cognitive spaces, thereby allowing workload inferences using only ECG. Our results show that ECG-derived projections expressively capture variation in cognitive states and provide good support for accurate classification. Our findings underpin ECG as an interpretable, real-time, wearable solution for everyday cognitive monitoring.

Related papers

• ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation [36.244601234085856]
  Existing multimodal large language models (MLLMs) remain unreliable for ECG interpretation.<n>ECG-R1 is the first reasoning MLLM designed for reliable ECG interpretation.<n>Code and data are publicly available at hrefhttp://ai.heartvoice.com.cn/ECG-R1here.
  arXiv  Detail & Related papers  (2026-02-04T07:17:55Z)
• Cross-Modal Computational Model of Brain-Heart Interactions via HRV and EEG Feature [0.1631115063641726]
  ECG signals are feasible on wearable equipment pieces such as headbands.<n>This study investigates whether ECG-derived features can serve as surrogate indicators of cognitive load.
  arXiv  Detail & Related papers  (2026-01-11T07:20:30Z)
• Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generation [52.19347532840774]
  We propose SE-Diff, a novel physiological simulator and experience enhanced diffusion model for ECG generation.<n> SE-Diff integrates a lightweight ordinary differential equation (ODE)-based ECG simulator into the diffusion process via a beat decoder.<n>Extensive experiments on real-world ECG datasets demonstrate that SE-Diff improves both signal fidelity and text-ECG semantic alignment.
  arXiv  Detail & Related papers  (2025-11-13T02:57:10Z)
• EchoingECG: An Electrocardiogram Cross-Modal Model for Echocardiogram Tasks [23.243697999272825]
  We introduce EchoingECG, a probabilistic student-teacher model that leverages uncertainty-aware ECG embeddings and ECHO supervision to improve ECG-based cardiac function prediction.<n>Our approach integrates Probabilistic Cross-Modal Embeddings (PCME++), a probabilistic contrastive framework, with ECHO-CLIP, a vision-language pre-trained model trained on ECHO-text pairs, to distill ECHO knowledge into ECG representations.
  arXiv  Detail & Related papers  (2025-09-30T05:03:33Z)
• UniECG: Understanding and Generating ECG in One Unified Model [26.641666246045133]
  We propose UniECG, the first unified model for ECG capable of concurrently performing evidence-based ECG interpretation and text-conditioned ECG generation tasks.<n>UniECG can autonomously choose to interpret or generate an ECG based on user input, significantly extending the capability boundaries of current ECG models.
  arXiv  Detail & Related papers  (2025-09-23T03:15:53Z)
• EEG-MedRAG: Enhancing EEG-based Clinical Decision-Making via Hierarchical Hypergraph Retrieval-Augmented Generation [45.031633614714]
  EEG-MedRAG is a three-layer hypergraph-based retrieval-augmented generation framework.<n>It unifies EEG domain knowledge, individual patient cases, and a large-scale repository into a traversable n-ary relational hypergraph.<n>We introduce the first cross-disease, cross-role EEG clinical QA benchmark, spanning seven disorders and five authentic clinical perspectives.
  arXiv  Detail & Related papers  (2025-08-19T11:12:58Z)
• Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG [40.407824759778784]
  PTACL (Patient and Temporal Alignment Contrastive Learning) is a multimodal contrastive learning framework that enhances ECG representations by integrating-temporal information from CMR.<n>We evaluate PTACL on paired ECG-CMR data from 27,951 subjects in the UK Biobank.<n>Our results highlight the potential of PTACL to enhance non-invasive cardiac diagnostics using ECG.
  arXiv  Detail & Related papers  (2025-06-24T17:19:39Z)
• GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images [44.50428701650495]
  We introduce GEM, the first MLLM unifying ECG time series, 12-lead ECG images and text for grounded and clinician-aligned ECG interpretation.<n> GEM enables feature-grounded analysis, evidence-driven reasoning, and a clinician-like diagnostic process through three core innovations.<n>We propose the Grounded ECG task, a clinically motivated benchmark designed to assess the MLLM's capability in grounded ECG understanding.
  arXiv  Detail & Related papers  (2025-03-08T05:48:53Z)
• AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings [34.078819572852446]
  Electrocardiogram (ECG) is highly sensitive in detecting acute heart attacks.<n>This paper introduces AnyECG, a foundational model designed to extract robust representations from any real-world ECG data.
  arXiv  Detail & Related papers  (2024-11-17T17:32:58Z)
• Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation [41.82319894067087]
  We propose an inter-intra period-aware ECG representation learning approach. Considering ECGs of atrial fibrillation patients exhibit the irregularity in RR intervals and the absence of P-waves, we develop specific pre-training tasks for interperiod and intraperiod representations. Our approach demonstrates remarkable AUC performances on the BTCH dataset, textiti.e., 0.953/0.996 for paroxysmal/persistent atrial fibrillation detection.
  arXiv  Detail & Related papers  (2024-10-08T10:03:52Z)
• NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis [5.8961928852930034]
  We present NERULA, a self-supervised framework designed for single-lead ECG signals. NERULA's dual-pathway architecture combines ECG reconstruction and non-contrastive learning to extract detailed cardiac features. We show that combining generative and discriminative paths into the training spectrum leads to better results by outperforming state-of-the-art self-supervised learning benchmarks in various tasks.
  arXiv  Detail & Related papers  (2024-05-21T14:01:57Z)
• MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation [28.35107188450758]
  Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions.<n>Recent studies have concentrated on classifying cardiac conditions using ECG data but have overlooked ECG report generation.<n>We propose the Multimodal ECG Instruction Tuning (MEIT) framework, the first attempt to tackle ECG report generation with LLMs and multimodal instructions.
  arXiv  Detail & Related papers  (2024-03-07T23:20:56Z)

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.