High Resolution Common Spatial Frequency Filters for Classifying Multi-class EEG

机译：用于分类多类脑电图的高分辨率通用空间频率滤波器

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摘要

The Common Spatial Patterns (CSP) algorithm is a highly successful spatial filtering method for extracting spatial patterns related to specific mental tasks from electroencephalogram (EEG) signals. The performance of CSP highly depends on the selection of frequency band in the preprocess. However, the most discriminative frequency band features varies slightly with subjects and mental tasks. In order to provide high resolution in frequency domain, we propose an common spatial frequency patterns method to learn most discriminative spatial and frequency filters simultaneously for specific mental task. The results on EEG data during motor imagery (MI) tasks demonstrate the good performance of our method with decreased number of EEG channels.

机译：通用空间模式（CSP）算法是一种非常成功的空间滤波方法，用于从脑电图（EEG）信号中提取与特定心理任务有关的空间模式。 CSP的性能在很大程度上取决于预处理中频段的选择。但是，最具区分性的频段特征随受试者和心理任务而略有不同。为了在频域中提供高分辨率，我们提出了一种通用的空间频率模式方法，以针对特定的心理任务同时学习最具区分性的空间和频率滤波器。在运动图像（MI）任务期间对EEG数据的结果表明，在减少EEG通道数量的情况下，本方法具有良好的性能。

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来源
《Advances in cognitive neurodynamics (II)》|2011年|683-688|共6页
会议地点 Hangzhou(CN)
作者
Qibin Zhao; Tomasz M. Rutkowski; Andrzej Cichocki; Liqing Zhang;
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作者单位

Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Saitama,Japan;

Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Saitama,Japan;

Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Saitama,Japan;

Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Saitama,Japan;

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原文格式 PDF
正文语种 eng
中图分类神经系;
关键词
EEG; BCI; CSP;

机译：脑电图; BCI; CSP;

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专利

1. Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space [J] . Ioannis Xygonakis, Alkinoos Athanasiou, Niki Pandria, Computational intelligence and neuroscience . 2018,第3期

机译：在EEG源空间上通过通用空间模式滤波器对运动图像进行解码
2. Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space [J] . Xygonakis Ioannis, Athanasiou Alkinoos, Pandria Niki, Computational intelligence and neuroscience . 2018,第Pta2期

机译：通过EEG源空间的常见空间模式过滤器解码电机图像
3. A Multi-Channel Approach for Cortical Stimulation Artefact Suppression in Depth EEG Signals Using Time-Frequency and Spatial Filtering [J] . Bhattacharyya Abhijit, Ranta Radu, Le Cam Steven, IEEE Transactions on Biomedical Engineering . 2019,第7期

机译：使用时频和空间滤波的深度脑电信号皮层刺激伪影抑制的多通道方法
4. High Resolution Common Spatial Frequency Filters for Classifying Multi-class EEG [C] . Qibin Zhao, Tomasz M. Rutkowski, Andrzej Cichocki, International conference on cognitive neurodynamics . 2011

机译：用于对多级EEG进行分类的高分辨率公共空间频率滤波器
5. Separation of spiky transients in EEG/MEG using morphological filters in multi-resolution analysis. [D] . Pon, Lin-Sen. 2002

机译：在多分辨率分析中使用形态学过滤器分离脑电图/脑电图中尖峰瞬变。
6. Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space [O] . Ioannis Xygonakis, Alkinoos Athanasiou, Niki Pandria, 2018

机译：在EEG源空间上通过通用空间模式滤波器对运动图像进行解码
7. Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space [O] . Ioannis Xygonakis, Alkinoos Athanasiou, Niki Pandria, 2018

机译：通过EEG源空间的常见空间模式过滤器解码电机图像
8. Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters [R] . Daugman, J. G. 1985

机译：二维视皮层滤波器优化空间分辨率，空间频率和方向的不确定关系

High Resolution Common Spatial Frequency Filters for Classifying Multi-class EEG

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