EEG preprocessing - A Trick Before Doing ICA

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EEG preprocessing - A Trick Before Doing ICA

EEGLab maillist My ICs don't have high power in low frequency is b/c I do a small trick here. before running ICA, it is recommended that you filter your data with 1Hz, then copy IC results back to your unfiltered dataset. More detail discussion is he…

One EEG preprocessing pipeline - EEG-fMRI paradigm

The preprocessing pipeline of EEG data from EEG-fMRI paradigm differs from that of regular EEG data, because they are mainly influenced by MRI artefacts. However, after removing the MRI artefacts at the first step, following ones would be the same as…

EEG preprocess - re-reference EEG预处理 - 重参考

Source: https://blricrex.hypotheses.org/ressources/eeg/pre-processing-for-erps/re-referencing-eeg-data Re-referencing only applies to EEG data, MEG is reference-free. In EEG, voltages recorded at each electrode are relative to voltages recorded at ot…

Machine Learning Trick of the Day (2): Gaussian Integral Trick

Machine Learning Trick of the Day (2): Gaussian Integral Trick Today's trick, the Gaussian integral trick, is one that allows us to re-express a (potentially troublesome) function in an alternative form, in particular, as an integral of a Gaussian ag…

ICA(独立成分分析)笔记

ICA又称盲源分离(Blind source separation, BSS) 它假设观察到的随机信号x服从模型,其中s为未知源信号,其分量相互独立,A为一未知混合矩阵. ICA的目的是通过且仅通过观察x来估计混合矩阵A以及源信号s. 大多数ICA的算法需要进行“数据预处理”(data preprocessing):先用PCA得到y,再把y的各个分量标准化(即让各分量除以自身的标准差)得到z.预处理后得到的z满足下面性质: z的各个分量不相关: z的各个分量的方差都为1. “ICA基本定理”:…

[Scikit-learn] 4.3 Preprocessing data

数据分析的重难点,就这么来了,欢迎欢迎,热烈欢迎. 4. Dataset transformations 4.3. Preprocessing data 4.3.1. Standardization, or mean removal and variance scaling 4.3.1.1. Scaling features to a range 4.3.1.2. Scaling sparse data 4.3.1.3. Scaling data with outliers 4.3.1.4. C…