论文标题
使用脑电图记录对眼态进行分类：使用信号时期的加速增长和相互信息测量
Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure
论文作者
论文摘要
脑电图（EEG）信号的分类在广泛的应用中很有用，例如癫痫发作/预测，运动成像分类，情绪分类和药物效应诊断等。随着大量的脑电图通道获取，开发有效的数据还原方法至关重要，从一个应用程序到另一种应用程序的重要性各不相同。同样重要的是，对于许多应用程序，在脑电图录制期间实现在线分类，以监视发生变化的发生。在本文中，我们介绍了一种基于共同信息（MI）的方法，以进行通道选择。获得的结果表明，尽管分类精度得分受到惩罚，但使用MI技术可以实现有希望的加速增长。将MI与含有信号转变的信号时期（3秒）一起增强了这些加速增长。这项工作是探索性的，我们建议进行进一步的研究进行验证和开发。提高分类速度的好处包括改善在临床或教育环境中的应用。
The classification of electroencephalography (EEG) signals is useful in a wide range of applications such as seizure detection/prediction, motor imagery classification, emotion classification and drug effects diagnosis, amongst others. With the large number of EEG channels acquired, it has become vital that efficient data-reduction methods are developed, with varying importance from one application to another. It is also important that online classification is achieved during EEG recording for many applications, to monitor changes as they happen. In this paper we introduce a method based on Mutual Information (MI), for channel selection. Obtained results show that whilst there is a penalty on classification accuracy scores, promising speed-up gains can be achieved using MI techniques. Using MI with signal epochs (3secs) containing signal transitions enhances these speed-up gains. This work is exploratory and we suggest further research to be carried out for validation and development. Benefits to improving classification speed include improving application in clinical or educational settings.
