标题： 用于脑电预测的人工智能：应用混沌理论 显示英文标题

标题： Artificial Intelligence for EEG Prediction: Applied Chaos Theory

摘要： 在当前的研究中，我们深入探讨了脑电图（EEG）数据分析这一复杂领域，专注于跨32个EEG通道的数据序列到序列的预测。 该研究和谐地融合了应用混沌理论和动力系统理论的原理，生成了一个新的特征集，增强了我们深度学习模型的表征能力。 这项工作的核心是一个基于变压器的序列到序列架构，经过精心校准，以捕捉EEG序列中固有的非线性和高维时间依赖性。 通过谨慎的架构设计、参数初始化策略和优化技术，我们在计算效率和预测性能之间找到了复杂的平衡。 我们的模型在EEG数据序列预测方面处于前沿，表现出显著的泛化能力和鲁棒性。 这些发现不仅扩展了我们对EEG数据动态的理解，还揭示了一个强大的分析框架，可以适应神经科学及其他领域的各种时间序列预测任务。 显示英文摘要

摘要： 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.