之前提到过CBU的博士训练课程里有一部分是介绍Neuroimaging方法学（Cam学记05 | 剑桥如何培养心理学博士(MRC-CBU)），其中脑电/脑磁课程是由CBU的M/EEG的boss：Dr. Olaf Hauk主讲。Olaf是MEG方法学的专家，同时也从事语言方面的认知神经科学研究。近期，他将这套课程放到了网上，可供大家学习，是一个非常好的材料。 这套教学视频包含了MEG/EEG的基本知识、溯源分析、时频与功能连接、MVPA等，以教学为主，也有一些简单基于Python的代码示例。下面是课程涉及的内容： EEG/MEG measurement and pre-processing1. Overview of EEG/MEG data processing from raw data to source estimates2. A brief history of timing3. The measurement of EEG and MEG signals and their neuronal generators4. Basics of EEG/MEG artefacts and their correction5. Frequency and temporal filtering of EEG/MEG data6. Maxfiltering of EEG/MEG data7. Topographical artefact correction of EEG/MEG data8. The differential sensitivities of EEG and MEG to their neuronal generators9. Event-related potentials and fields EEG/MEG head modelling and source estimation1. The EEG/MEG forward model2. Source spaces for EEG/MEG source estimation3. Head models for EEG/MEG source estimation4. The EEG/MEG inverse problem5. The spatial resolution of linear EEG/MEG source estimation6. Comparison of spatial resolution for linear EEG/MEG source estimation methods7. Noise and regularisation in EEG/MEG source estimates Connectivity1. The basics of signals in the frequency domain2. Frequency spectra and the Fourier analysis3. Time-frequency analysis and wavelets4. The basis of functional connectivity methods5. Spatial resolution (leakage) and connectivity Further topics1. Primer on group statistics for EEG/MEG data2. Primer on decoding and RSA with EEG/MEG data3. Multimodal imaging and prior information for EEG/MEG analysis 网址链接：https://www.youtube.com/playlist?list=PLp67eqWCj2f_DBsCMkIOBpBbLWGAUKtu5
