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Study on method of filtering noises from electroencephalography signals and its application for identification of several electroencephalography signals

机译：从脑电图信号过滤噪声的方法及其应用中的几种脑电图信号识别

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Tuan Van Huynh; Vu Quang Huynh;
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1. Efficiency test of filtering methods for the removal of transcranial magnetic stimulation artifacts on human electroencephalography with artificially transcranial magnetic stimulation-corrupted signals [J] . Nicolas A. Zilber, Yoshinori Katayama, Keiji Iramina, Journal of Applied Physics . 2010,第9pta2期

机译：人工经颅磁刺激受损信号在人脑电图上去除经颅磁刺激伪影的滤波方法的效率测试
2. Sub-band adaptive filtering method for electroencephalography/event related potential signal using nature inspired optimisation techniques [J] . Ahirwal Mitul Kumar, Kumar Anil, Singh Girish Kumar Science, Measurement & Technology, IET . 2015,第8期

机译：脑电/事件相关电位信号的子带自适应滤波方法
3. Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods [J] . Ikhtiyor Majidov, Taegkeun Whangbo Sensors . 2019,第7期

机译：使用深度学习方法对运动图像脑电信号进行有效分类
4. Efficiency test of filtering methods for the removal of transcranial magnetic stimulation artifacts on human electroencephalography with artificially transcranial magnetic stimulation-corrupted signals [C] . Nicolas A. Zilber, Yoshinori Katayama, Keiji Iramina Joint MMM-Intermag Conference . 2010

机译：用人为经颅磁刺激损坏信号去除人体脑电图造成跨扫磁刺激伪影的过滤方法的效率试验
5. Advanced signal processing techniques for single trial electroencephalography signal classification for Brain Computer Interface applications. [D] . Li, Kun. 2010

机译：用于脑计算机接口应用程序的单次脑电图信号分类的高级信号处理技术。
6. Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer’s Disease [O] . Sou Nobukawa, Teruya Yamanishi, Shinya Kasakawa, 2020

机译：基于复杂性和脑电信号同步的阿尔茨海默氏病分类方法
7. Early-Stage Pilot Study on Using Fractional-Order Calculus-Based Filtering for the Purpose of Analysis of Electroencephalography Signals [O] . Kawala-Janik Aleksandra, Bauer Waldemar, Żołubak Magda, 2016

机译：基于分数阶微积分滤波用于脑电信号分析目的的早期试验研究

Study on method of filtering noises from electroencephalography signals and its application for identification of several electroencephalography signals

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