基于欧几里得距离的脑机接口运动想象任务分类

12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI:10.1109/NEUREL.2014.7011477

M. Fira, Roxana Aldea, A. Lazar, L. Goras

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引用次数: 1

摘要

本文提出并讨论了一种基于模式和欧氏距离的运动想象任务分类方法。该方法简单、快速，但对所选特征/频率的分类相当敏感。选择预定义数量的特性会得到与GTEC/BCI2000类似的结果，而最佳选择会得到改进的结果，但仍需要额外的调查。

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Classifications of motor imagery tasks in brain computer interface using Euclidean distance

In this paper we propose and discuss a new classification method of motor imagery tasks based on patterns and Euclidean distance. The proposed method is simple, fast, but considerably sensitive with respect to the selected features/frequencies for classification. Choosing a predefined number of features leads to results similar to GTEC/BCI2000 while an optimal selection gives improved results but still requires additional investigation.

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12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)

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