Ng Kam Swee, Yang Hyung-Jeong, Kim Sun-Hee
Department of Computer Science, Chonnam National University, Gwangju 500-757, South Korea.
Biosystems. 2009 Jul;97(1):15-27. doi: 10.1016/j.biosystems.2009.03.007. Epub 2009 Apr 5.
EEG signals are important to capture brain disorders. They are useful for analyzing the cognitive activity of the brain and diagnosing types of seizure and potential mental health problems. The Event Related Potential can be measured through the EEG signal. However, it is always difficult to interpret due to its low amplitude and sensitivity to changes of the mental activity. In this paper, we propose a novel approach to incrementally detect the pattern of this kind of EEG signal. This approach successfully summarizes the whole stream of the EEG signal by finding the correlations across the electrodes and discriminates the signals corresponding to various tasks into different patterns. It is also able to detect the transition period between different EEG signals and identify the electrodes which contribute the most to these signals. The experimental results show that the proposed method allows the significant meaning of the EEG signal to be obtained from the extracted pattern.
脑电图（EEG）信号对于捕捉脑部疾病很重要。它们有助于分析大脑的认知活动，并诊断癫痫类型和潜在的心理健康问题。事件相关电位（ERP）可以通过EEG信号来测量。然而，由于其幅度较低且对心理活动变化敏感，ERP一直难以解读。在本文中，我们提出了一种新颖的方法来增量检测这类EEG信号的模式。该方法通过找到电极之间的相关性成功地总结了EEG信号的整个流，并将对应于各种任务的信号区分为不同的模式。它还能够检测不同EEG信号之间的过渡期，并识别对这些信号贡献最大的电极。实验结果表明，所提出的方法能够从提取的模式中获得EEG信号的重要意义。
