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对大脑-计算机接口的深度表示学习技术的审查
Pierre Guetschel1, Sara Ahmadi1, Michael Tangermann1
1Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands.
Journal of neural engineering
|October 21, 2024
概括
对脑计算机接口 (BCI) 的深度学习显示出有前途. 自动编码器很常见,但自主监督学习 (SSL) 正在出现用于脑电图 (EEG) 解码,尽管基础模型仍然需要.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 大脑计算机接口 (BCI) 使用脑电图 (EEG) 信号.
- 深度学习为EEG信号表示和解码提供了先进的方法.
- 代表性学习对于提高BCI绩效至关重要.
研究的目的:
- 审查和分析用于BCI解码的深度表示学习技术.
- 综合基于EEG的BCI最新技术的经验发现.
- 确定BCI深度代表性学习的趋势,动机和挑战.
主要方法:
- 对BCI的深度表示学习的81篇文章进行系统审查.
- 基于深度学习技术,动机和表现特征的研究的分类.
- 分析自动编码器和自主监督学习 (SSL) 的趋势.
主要成果:
- 自动编码器是最普遍的深度学习技术 (31篇文章).
- 自主监督学习 (SSL) 是一个不断增长的领域,最近发表了大多数SSL研究 (10在2022年+).
- 大多数研究都使用表示学习来进行转移学习,较少的研究集中在稳定性,不变性或数据结构上.
结论:
- 需要使用SSL进行EEG信号解码的标准基础模型.
- 进一步研究特征学习表示是必不可少的.
- 开发专门的基准和数据集对于在BCI中推进基础模型至关重要.
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