Applied Neuroscience Branch, 711th Human Performance Wing, Air Force Research Laboratory, Wright-Patterson Air Force Base, OH 45433, USA.
Neuroimage. 2012 Jan 2;59(1):57-63. doi: 10.1016/j.neuroimage.2011.07.091. Epub 2011 Aug 5.
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.
模式分类技术在生理数据中的应用已经迅速扩展。通过模式分类，已经成功地完成了各种任务，例如从磁共振图像中诊断疾病、为残疾人士设计脑机接口，以及基于电活动解码大脑功能。这些分类器已进一步应用于复杂认知任务中，以提高性能，例如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用系统的发展，这些技术应该在重复的会话中保持稳定。本文描述了三种流行的模式分类技术在 EEG 数据中的应用，这些数据是从在一个月内进行五天复杂多任务的渐近训练的受试者中获得的。所有三种分类器的性能都明显高于随机水平。所有三种分类器的性能都受到跨天分类的显著负面影响；但是提出了两种修改方法，可以大大减少错误分类。结果表明，通过适当的方法，模式分类在天和周之间足够稳定，可以成为一种有效且有用的方法。
