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General-Disorders-EEG-Dataset-v1|神经疾病数据集|EEG数据数据集
收藏huggingface2024-10-05 更新2024-12-12 收录
下载链接：
https://huggingface.co/datasets/Neurazum/General-Disorders-EEG-Dataset-v1
下载链接
链接失效反馈资源简介：
这是一个由‘bai’模型生成的合成EEG数据集，基于真实数据。数据集包含1140个特征列，涵盖了性别、年龄、教育水平、IQ等基本信息，以及主要和具体的疾病分类。数据集内容包括强迫症、双相情感障碍、精神分裂症等多种神经和精神疾病。数据集由Neurazum开发和发布，遵循Apache 2.0许可证。
创建时间：
2024-10-02
原始信息汇总
数据集概述
基本信息
- 名称: Disorders EEG Data
- 许可证: Apache 2.0
- 语言: 英语
- 标签: 神经科学, 大脑, EEG, 数据集, 疾病, 合成, AI, 神经, Neura
- 数据量: 1K<n<10K
数据集描述
- 数据类型: 合成EEG数据
- 生成模型: 基于真实数据的‘bai’模型
特征/列
- No: 编号
- Sex: 性别
- Age: 参与者年龄
- EEG Date: EEG日期
- Education: 教育水平
- IQ: 参与者IQ水平
- Main Disorder: 疾病的主要分类定义
- Specific Disorder: 疾病的具体分类定义
- 总特征/列数: 1140
内容
- 强迫症
- 双相情感障碍
- 精神分裂症
- 抑郁症
- 社交焦虑症
- 成瘾障碍
- 酒精使用障碍
开发者与发布者
- 开发者: Neurazum
AI搜集汇总
数据集介绍
构建方式
General-Disorders-EEG-Dataset-v1数据集是通过‘bai’模型基于真实数据生成的合成脑电图（EEG）数据。该模型利用先进的神经网络技术，模拟了多种精神障碍患者的脑电活动，确保了数据的多样性和真实性。数据集的构建过程严格遵循科学规范，涵盖了从数据采集到模型训练的各个环节，确保了数据的可靠性和有效性。
特点
该数据集包含了1140个特征/列，涵盖了参与者的性别、年龄、教育水平、智商等基本信息，以及脑电图日期、主要障碍类别和具体障碍类别等详细内容。数据集特别关注了强迫症、双相情感障碍、精神分裂症、抑郁症、社交焦虑症、成瘾障碍和酒精使用障碍等多种精神障碍，为研究者提供了丰富的研究素材。
使用方法
General-Disorders-EEG-Dataset-v1数据集适用于神经科学、精神医学等领域的研究。研究者可以通过分析数据集中的脑电图数据，探索不同精神障碍的脑电活动特征，进而开发新的诊断工具或治疗方法。数据集的使用需遵循Apache 2.0许可协议，确保在合法合规的前提下进行科学研究。
背景与挑战
背景概述
General-Disorders-EEG-Dataset-v1是由Neurazum开发并发布的一个合成脑电图（EEG）数据集，旨在为神经科学领域的研究提供支持。该数据集基于真实数据，通过‘bai’模型生成，涵盖了多种精神障碍类别，包括强迫症、双相情感障碍、精神分裂症等。数据集的创建时间为近期，反映了当前神经科学研究中对精神障碍诊断与治疗的迫切需求。通过提供多样化的EEG数据，该数据集为研究人员探索脑电波与精神障碍之间的关系提供了重要资源，推动了神经科学和人工智能在医疗领域的交叉应用。
当前挑战
General-Disorders-EEG-Dataset-v1面临的挑战主要集中在两个方面。首先，尽管数据集通过合成方法生成，但其与真实EEG数据的相似性和有效性仍需进一步验证，以确保研究结果的可靠性。其次，精神障碍的多样性和复杂性对数据标注和分类提出了较高要求，如何准确区分不同障碍类别并捕捉其细微差异是数据集构建中的一大难题。此外，EEG数据的高维性和噪声干扰也为数据预处理和分析带来了技术挑战，需要开发更高效的算法和模型以提取有用信息。
常用场景
经典使用场景
General-Disorders-EEG-Dataset-v1数据集在神经科学研究中具有重要应用，特别是在精神障碍的脑电图（EEG）分析领域。该数据集通过合成EEG数据，模拟了多种常见精神障碍的脑电活动模式，如强迫症、双相情感障碍和精神分裂症等。研究人员可以利用这些数据来训练和验证机器学习模型，以识别和分类不同的精神障碍。
解决学术问题
该数据集解决了精神障碍研究中数据稀缺和隐私保护的问题。通过合成EEG数据，研究人员可以在不侵犯患者隐私的情况下，获得大量高质量的脑电数据。这不仅有助于提高精神障碍的诊断准确性，还为开发新的治疗方法和干预措施提供了数据支持。
衍生相关工作
基于General-Disorders-EEG-Dataset-v1数据集，许多经典研究工作得以展开。例如，研究人员开发了基于深度学习的EEG信号分类模型，能够准确识别不同类型的精神障碍。此外，该数据集还促进了跨学科合作，推动了神经科学、心理学和计算机科学在精神障碍研究中的融合。
以上内容由AI搜集并总结生成
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