Artificial Intelligence for EEG Prediction: Applied Chaos Theory

• URL: http://arxiv.org/abs/2402.03316v1
• Date: Tue, 3 Oct 2023 14:58:23 GMT
• Title: Artificial Intelligence for EEG Prediction: Applied Chaos Theory
• Authors: Soul Syrup
• Abstract summary: The study fuses the principles of applied chaos theory and dynamical systems theory to engender a novel feature set. The endeavour's cornerstone is a transformer-based sequence-to-sequence architecture, meticulously to capture the non-linear and high-dimensional temporal dependencies. Our model stands as a vanguard in EEG data sequence prediction, demonstrating remarkable generalisability and robustness.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In the present research, we delve into the intricate realm of electroencephalogram (EEG) data analysis, focusing on sequence-to-sequence prediction of data across 32 EEG channels. The study harmoniously fuses the principles of applied chaos theory and dynamical systems theory to engender a novel feature set, enriching the representational capacity of our deep learning model. The endeavour's cornerstone is a transformer-based sequence-to-sequence architecture, calibrated meticulously to capture the non-linear and high-dimensional temporal dependencies inherent in EEG sequences. Through judicious architecture design, parameter initialisation strategies, and optimisation techniques, we have navigated the intricate balance between computational expediency and predictive performance. Our model stands as a vanguard in EEG data sequence prediction, demonstrating remarkable generalisability and robustness. The findings not only extend our understanding of EEG data dynamics but also unveil a potent analytical framework that can be adapted to diverse temporal sequence prediction tasks in neuroscience and beyond.

Related papers

• BEAT-Net: Injecting Biomimetic Spatio-Temporal Priors for Interpretable ECG Classification [1.3909285316906435]
  BEAT-Net is a Biomimetic ECG Analysis with Tokenization framework.<n>It decomposes cardiac physiology through specialized encoders that extract local beat morphology.<n>It exhibits exceptional data efficiency, recovering fully supervised performance using only 30 to 35 percent of annotated data.
  arXiv  Detail & Related papers  (2026-01-12T08:37:47Z)
• THD-BAR: Topology Hierarchical Derived Brain Autoregressive Modeling for EEG Generic Representations [3.253716156877394]
  We propose a novel Topology Hierarchical Derived Brain Autoregressive Modeling (THD-BAR) for EEG generic representations.<n>The core innovation of THD-BAR lies in the introduction of the Brain Topology Hierarchy (BTH), which establishes a multi-scale spatial order for EEG channels.<n>Based on BTH, we design a Topology-Hierarchical Vector Quantized-Variational Autoencoder (THVQ-VAE) for multi-scale tokenization and develop an enhanced Brain Autoregressive (BAR) module with specialized masking strategies for prediction.
  arXiv  Detail & Related papers  (2025-11-05T13:20:14Z)
• A Time-Series Foundation Model by Universal Delay Embedding [4.221753069966852]
  This study introduces Universal Delay Embedding (UDE), a pretrained foundation model designed to revolutionize time-series forecasting.<n>UDE as a dynamical representation of observed data constructs two-dimensional subspace patches from Hankel matrices.<n>In particular, the learned dynamical representations and Koopman operator prediction forms from the patches exhibit exceptional interpretability.
  arXiv  Detail & Related papers  (2025-09-15T16:11:49Z)
• Quantum Spectral Reasoning: A Non-Neural Architecture for Interpretable Machine Learning [0.0]
  We propose a novel machine learning architecture that departs from conventional neural network paradigms.<n>We use quantum spectral methods, specifically Pade approximants and the Lanczos algorithm, for interpretable signal analysis and symbolic reasoning.<n>Our results show that this spectral-symbolic architecture achieves competitive accuracy while maintaining interpretability and data efficiency.
  arXiv  Detail & Related papers  (2025-08-05T07:16:45Z)
• Generalized Factor Neural Network Model for High-dimensional Regression [50.554377879576066]
  We tackle the challenges of modeling high-dimensional data sets with latent low-dimensional structures hidden within complex, non-linear, and noisy relationships. Our approach enables a seamless integration of concepts from non-parametric regression, factor models, and neural networks for high-dimensional regression.
  arXiv  Detail & Related papers  (2025-02-16T23:13:55Z)
• Large Cognition Model: Towards Pretrained EEG Foundation Model [0.0]
  We propose a transformer-based foundation model designed to generalize across diverse EEG datasets and downstream tasks. Our findings highlight the potential of pretrained EEG foundation models to accelerate advancements in neuroscience, personalized medicine, and BCI technology.
  arXiv  Detail & Related papers  (2025-02-11T04:28:10Z)
• EEG-ReMinD: Enhancing Neurodegenerative EEG Decoding through Self-Supervised State Reconstruction-Primed Riemannian Dynamics [24.57253767771542]
  We propose a novel two-stage approach to EEG decoding called EEG-ReMinD. EEG-ReMinD mitigates reliance on supervised learning and integrates inherent geometric features. It efficiently handles EEG data corruptions and reduces the dependency on labels.
  arXiv  Detail & Related papers  (2025-01-14T14:19:40Z)
• GEFM: Graph-Enhanced EEG Foundation Model [16.335330142000657]
  Foundation models offer a promising solution by leveraging large-scale unlabeled data through pre-training. We propose Graph-Enhanced EEG Foundation Model (GEFM), a novel foundation model for EEG that integrates both temporal and inter-channel information. Our architecture combines Graph Neural Networks (GNNs), which effectively capture relational structures, with a masked autoencoder to enable efficient pre-training.
  arXiv  Detail & Related papers  (2024-11-29T06:57:50Z)
• Non-asymptotic Convergence of Training Transformers for Next-token Prediction [48.9399496805422]
  Transformers have achieved extraordinary success in modern machine learning due to their excellent ability to handle sequential data. This paper provides a fine-grained non-asymptotic analysis of the training dynamics of a one-layer transformer. We show that the trained transformer presents non-token prediction ability with dataset shift.
  arXiv  Detail & Related papers  (2024-09-25T20:22:06Z)
• eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling [9.52474299688276]
  We introduce a low-rank structured variational autoencoder framework for nonlinear state-space graphical models. We show that our approach consistently demonstrates the ability to learn a more predictive generative model.
  arXiv  Detail & Related papers  (2024-03-03T02:19:49Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Neural Extended Kalman Filters for Learning and Predicting Dynamics of Structural Systems [5.252966797394752]
  We propose a learnable Extended Kalman Filter (EKF) for learning the latent evolution dynamics of complex physical systems. Neural EKF is a generalized version of the conventional EKF, where the modeling of process dynamics and sensory observations can be parameterized by neural networks. We show that the structure imposed by the Neural EKF is beneficial to the learning process.
  arXiv  Detail & Related papers  (2022-10-09T04:39:15Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging [6.512092052306553]
  Examples include M/EEG inverse problems, encoding neural models for task-based fMRI analyses, and temperature monitoring schemes. We devise a novel hierarchical flexible Bayesian framework within which the intrinsic-temporal dynamics of model parameters and noise are modeled.
  arXiv  Detail & Related papers  (2021-11-02T15:50:01Z)
• Optimized ensemble deep learning framework for scalable forecasting of dynamics containing extreme events [0.0]
  Two machine learning techniques are jointly used to achieve synergistic improvements in model accuracy, stability, scalability, and prompting a new wave of applications in the forecasting of dynamics. The proposed OEDL model based on a best convex combination of feed-forward neural networks, reservoir computing, and long short-term memory can play a key role in advancing predictions of dynamics consisting of extreme events.
  arXiv  Detail & Related papers  (2021-06-09T10:59:41Z)
• Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms [71.62575565990502]
  We prove that the generalization error of an optimization algorithm can be bounded on the complexity' of the fractal structure that underlies its generalization measure. We further specialize our results to specific problems (e.g., linear/logistic regression, one hidden/layered neural networks) and algorithms.
  arXiv  Detail & Related papers  (2021-06-09T08:05:36Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)

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.