数据增强(Data Augmentation):通过对原始数据进行一系列变换(如旋转、平移、缩放、翻转等),可以生成更多的训练数据,从而增强模型的泛化能力。这种方法在图像分类等任务中尤为有效。模型微调(Model Fine-tuning):在预训练模型的基础上,使用特定任务的数据进行微调,可以使模型更好地适应新任务。这种方法在迁移学习中非常...
02466 (DTU - AI and Data): Project work on TUH EEG Artifact classification Created by Albert Kjøller Jacobsen, Aron Djurhuus Jacobsen & Phillip Chavarria Højbjerg This project investigates the role of data augmentation on the Temple University Hospital EEG Artifact corpus. The outcome of the...
Paper tables with annotated results for Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface
Liu, "Data Augmentation for EEG-Based Emotion Recognition with Deep Convolutional Neural Networks", Multi-Media Modeling (MMM), 2018. Lecture Notes in Computer Science, vol.10705. Springer, Cham.F. Wang, S.-h. Zhong, J. Peng, J. Jiang, and Y. Liu, "Data augmentation for eeg-based ...
例如,生成人工数据可以用于数据增强(data augmentation),这种数据增强通过生成不包含在原始数据集中的自然外观样本,从而人为地增加不可见样本的训练数据。此外,生成具有某些特性的自然外观样本的可能性,以及对创建它们的模型的研究,可以成为理解用于训练GAN的原始数据分布的有用工具。
本篇学习报告来源:《GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition》,作者针对脑电信号情绪识别由于数据缺乏而导致深度学习模型难以建立高准确性和稳定性的问题,提出了一种生成对抗网络的自监督数据增强框架来生成高质量和高多样性的模拟脑电信号样本。它是第一个将对抗训练与...
i.e., it can noticeably improve the classification accuracy; 2) CR is robust, i.e., it consistently outperforms existing data augmentation approaches in the literature; and, 3) CR is flexible, i.e., it can be combined with other data augmentation approaches to further increase the performan...
为了在有限的label数据改善EEG情感分析性能,论文基于生物学的减数分裂,提出了一个全新的EEG自监督对比学习框架“Self-supervised Group Meiosis Contrastive learning framework (SGMC)”,论文标题中的Meiosis就是来源于减数分裂,Meiosis主要应用在data augmentation这一环。
Improve data augmentation: synthesize EEGs with a forward model enabled recurrent conditional wGAN Improve understanding and diagnostics: Create a siamese network capable of generated a manifold of EEGs Imporved singal processing: Remove Artifacts from EEGs ...
proposed a generative adversarial network-based self-supervised data augmentation (GANSER) method and utilized adversarial training to learn an EEG generator (Zhang et al., 2022). This approach makes the distribution of the generated EEG signals similar to that of the corresponding real samples, ...