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腦波分析（EEG analysis）也稱為腦電圖分析，是用數學的信号处理以及電腦科技 … 腦波分析（EEG analysis）也稱為腦電圖分析，是用數學的信号处理以及電腦科技，從腦電圖（EEG）信號中提取相關資訊。腦波分析的目的是幫助研究者對人腦有進一步的瞭解，輔助医生的诊断以及疗法的選擇，並且提昇脑机接口（英語：Brain-computer Interface, 簡稱BCI）的技術。腦波分析的技術有許多分類的方式，若是要從腦電圖信號中找到大致符合腦電圖信號的数学模型，此方法可以分類為參數型，否則，就是非參數型的方式。傳統上，大部份的腦波分析方法可以分為四類:時域、頻域、時頻分析及非線性方法，也有一些較新的方法，包括使用深度学习（DNNs）的方法。頻域、時頻分析及非線性方法，也有一些較新的方法，包括使用深度学习（DNNs）的方法。
, EEG analysis is exploiting mathematical si … EEG analysis is exploiting mathematical signal analysis methods and computer technology to extract information from electroencephalography (EEG) signals. The targets of EEG analysis are to help researchers gain a better understanding of the brain; assist physicians in diagnosis and treatment choices; and to boost brain-computer interface (BCI) technology. There are many ways to roughly categorize EEG analysis methods. If a mathematical model is exploited to fit the sampled EEG signals, the method can be categorized as parametric, otherwise, it is a non-parametric method. Traditionally, most EEG analysis methods fall into four categories: time domain, frequency domain, time-frequency domain, and nonlinear methods. There are also later methods including deep neural networks (DNNs).ods including deep neural networks (DNNs).
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腦波分析（EEG analysis）也稱為腦電圖分析，是用數學的信号处理以及電腦科技 … 腦波分析（EEG analysis）也稱為腦電圖分析，是用數學的信号处理以及電腦科技，從腦電圖（EEG）信號中提取相關資訊。腦波分析的目的是幫助研究者對人腦有進一步的瞭解，輔助医生的诊断以及疗法的選擇，並且提昇脑机接口（英語：Brain-computer Interface, 簡稱BCI）的技術。腦波分析的技術有許多分類的方式，若是要從腦電圖信號中找到大致符合腦電圖信號的数学模型，此方法可以分類為參數型，否則，就是非參數型的方式。傳統上，大部份的腦波分析方法可以分為四類:時域、頻域、時頻分析及非線性方法，也有一些較新的方法，包括使用深度学习（DNNs）的方法。頻域、時頻分析及非線性方法，也有一些較新的方法，包括使用深度学习（DNNs）的方法。
, EEG analysis is exploiting mathematical si … EEG analysis is exploiting mathematical signal analysis methods and computer technology to extract information from electroencephalography (EEG) signals. The targets of EEG analysis are to help researchers gain a better understanding of the brain; assist physicians in diagnosis and treatment choices; and to boost brain-computer interface (BCI) technology. There are many ways to roughly categorize EEG analysis methods. If a mathematical model is exploited to fit the sampled EEG signals, the method can be categorized as parametric, otherwise, it is a non-parametric method. Traditionally, most EEG analysis methods fall into four categories: time domain, frequency domain, time-frequency domain, and nonlinear methods. There are also later methods including deep neural networks (DNNs).ods including deep neural networks (DNNs).
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EEG analysis
, 腦波分析
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