The effects of day-to-day variability of physiological data on operator functional state classification
The effects of day-to-day variability of physiological data on operator functional state classification
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DOI：
10.1016/j.neuroimage.2011.07.091
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发表时间：
2012-01-02
期刊：
影响因子：
5.7
通讯作者：
Russell, Christopher A.
中科院分区：
文献类型：
--
作者：
Christensen, James C.;Estepp, Justin R.;Russell, Christopher A.
模式分类技术在生理数据中的应用正在迅速扩大。各种各样的任务，从磁共振图像诊断疾病，为残疾人提供脑机接口，以及基于脑电活动的脑功能解码，都已经通过模式分类相当成功地完成了。这些分类器已被进一步应用于复杂的认知任务中，以提高性能，其中一个例子是作为自适应自动化的输入。为了产生可推广的结果并促进实际系统的开发，这些技术应该在重复的会议中保持稳定。本文描述了三种流行的模式分类技术在脑电数据中的应用，这些脑电数据来自于一个月内5天内执行复杂多任务的渐近训练对象。所有三个分类器的表现都远高于随机水平。这三者的表现均受到不同天数分类的显著负向影响；然而，提出了两项修改，大大减少了错误分类。结果表明，通过适当的方法，模式分类在天和周之间是足够稳定的，是一种有效的、有用的方法。Elsevier Inc.出版。
The application of pattern classification techniques to physiological data has undergone rapid expansion. Tasks as varied as the diagnosis of disease from magnetic resonance images, brain-computer interfaces for the disabled, and the decoding of brain functioning based on electrical activity have been accomplished quite successfully with pattern classification. These classifiers have been further applied in complex cognitive tasks to improve performance, in one example as an input to adaptive automation. In order to produce generalizable results and facilitate the development of practical systems, these techniques should be stable across repeated sessions. This paper describes the application of three popular pattern classification techniques to EEG data obtained from asymptotically trained subjects performing a complex multitask across five days in one month. All three classifiers performed well above chance levels. The performance of all three was significantly negatively impacted by classifying across days; however two modifications are presented that substantially reduce misclassifications. The results demonstrate that with proper methods, pattern classification is stable enough across days and weeks to be a valid, useful approach. Published by Elsevier Inc.