Classification of Motor Imagery Tasks with Information-Accumulated Technique

裴晓梅; 郑崇勋; 徐进

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Classification of Motor Imagery Tasks with Information-Accumulated Technique

更新时间：2025-12-08

- Classification of Motor Imagery Tasks with Information-Accumulated Technique
- Vol. 42, Issue 6, Pages: 756-759(2008)
- 作者机构：
  
  西安交通大学生物医学信息工程教育部重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： R318
- Online First：09 June 2008，
  
  Published：2008
- 稿件说明：
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裴晓梅, 郑崇勋, 徐进. Classification of Motor Imagery Tasks with Information-Accumulated Technique[J]. 2008, 42(6): 756-759.
DOI：

裴晓梅, 郑崇勋, 徐进. Classification of Motor Imagery Tasks with Information-Accumulated Technique[J]. 2008, 42(6): 756-759. DOI：

摘要

提出了将信息积累技术结合Fisher判别式分析的方法

实现大脑想象左右手运动意识任务的连续分类

获得了满意的结果.利用Morlet小波滤波方法提取优化的运动相关脑电特征

对两组实验数据4个受试者的运动相关脑电模式进行分类

平均最大分类正确率达到87.95%.通过最大分类正确率、最大互信息等评价指标的比较

验证了信息积累技术对改善大脑运动意识任务分类性能的有效性

从而为脑机接口系统应用中大脑运动意识任务分类提供了新的思路.

Abstract

The information-accumulated technique combined with Fisher discriminant analysis is proposed to realize the classification of left and right hand motor imagery tasks

and a satisfactory result is found. By Morlet wavelet filter to extract the optimized movement-related electroencephalogram(EEG)features

the movement-related EEG patterns from two groups of experiment data including 4 subjects are recognized and the average maximum classification accuracy reaches to 87.95%. With the two indexes

i.e. maximum classification accuracy and mutual information(MI)

the effectiveness of information-accumulated technique is verified to provide a new idea for identification of motor imagery tasks in brain-computer interface application.

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references

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