基于非线性双稳态动力学模型的脑电信号判别分析方法研究
Study on non-linear bistable dynamics model based EEG signal discrimination analysis method.
作者信息
Ying Xiaoguo, Lin Han, Hui Guohua
机构信息
a College of Information Engineering; Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province ; Zhejiang A & F University ; Linan , China.
出版信息
Bioengineered. 2015;6(5):297-8. doi: 10.1080/21655979.2015.1065360. Epub 2015 Jul 15.
Electroencephalogram (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations generating from ionic current flows within the neurons of the brain. EEG signal is looked as one of the most important factors that will be focused in the next 20 years. In this paper, EEG signal discrimination based on non-linear bistable dynamical model was proposed. EEG signals were processed by non-linear bistable dynamical model, and features of EEG signals were characterized by coherence index. Experimental results showed that the proposed method could properly extract the features of different EEG signals.
脑电图（EEG）是沿头皮记录的电活动。脑电图测量大脑神经元内离子电流流动产生的电压波动。脑电图信号被视为未来20年将重点关注的最重要因素之一。本文提出了基于非线性双稳动力学模型的脑电图信号判别方法。利用非线性双稳动力学模型对脑电图信号进行处理，并用相干指数表征脑电图信号的特征。实验结果表明，该方法能够正确提取不同脑电图信号的特征。
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