在fieltrip 当中,有几个函数是贯穿整个工具的 ,也是MEEG信号分析的主要手段,每个函数都涉及到不同的方法,我再次对函数使用的多重method做个归纳

1. FT_TIMELOCKANALYSIS computes the timelocked average ERP/ERF and computes the covariance matrix

时域分析是EEG信号分析的第一步,尤其对SSVEP和ERP, 那么这里面

Use as
    [timelock] = ft_timelockanalysis(cfg, data)
 
  The data should be organised in a structure as obtained from the
  FT_PREPROCESSING function. The configuration should be according to
 
    cfg.channel            = Nx1 cell-array with selection of channels (default = 'all'),
                             see FT_CHANNELSELECTION for details
    cfg.trials             = 'all' or a selection given as a 1xN vector (default = 'all')
    cfg.covariance         = 'no' or 'yes' (default = 'no')协方差
    cfg.covariancewindow   = 'prestim', 'poststim', 'all' or [begin end] (default = 'all')
    cfg.keeptrials         = 'yes' or 'no', return individual trials or average (default = 'no')
    cfg.removemean         = 'no' or 'yes' for covariance computation (default = 'yes')
    cfg.vartrllength       = 0, 1 or 2 (see below)

    注意这里面的 keeptrils ,如果后续要根据trisl做分类 如 BCI 就必须保留trials 否则返回的只有average 

2.FT_FREQANALYSIS performs frequency and time-frequency analysis on time series data over multiple trials
频域分析无需多言,是脑信号中最重要的部分,挑大梁的 ,比如功能连接  溯源 都需要频域的支持
  Use as
    [freq] = ft_freqanalysis(cfg, data)
 
  The input data should be organised in a structure as obtained from
  the FT_PREPROCESSING or the FT_MVARANALYSIS function. The configuration
  depends on the type of computation that you want to perform.
 
  The configuration should contain:
    cfg.method     = different methods of calculating the spectra
                     'mtmfft', analyses an entire spectrum for the entire data
                       length, implements multitaper frequency transformation
                     'mtmconvol', implements multitaper time-frequency
                       transformation based on multiplication in the
                       frequency domain. 这个是用的比较多,教程里面都是这个method 
                     'wavelet', implements wavelet time frequency
                       transformation (using Morlet wavelets) based on
                       multiplication in the frequency domain.
                     'tfr', implements wavelet time frequency
                         transformation (using Morlet wavelets) based on
                         convolution in the time domain.
                     'mvar', does a fourier transform on the coefficients
                         of an estimated multivariate autoregressive model,
                         obtained with FT_MVARANALYSIS. In this case, the
                         output will contain a spectral transfer matrix,
                         the cross-spectral density matrix, and the
                         covariance matrix of the innovatio noise.
    cfg.output     = 'pow'       return the power-spectra  输出功率,常用
                     'powandcsd' return the power and the cross-spectra 功率和交叉频谱密度 常用 
                     'fourier'   return the complex Fourier-spectra 复杂傅里叶频谱,少用

3.FT_TIMELOCKSTATISTICS  computes significance probabilities and/or critical values of a parametric statistical test
or a non-parametric permutation test.
  时域统计分析函数,计算pvalue 的 但是 很少用这个  一般都是基于频域的函数
 
  Use as
    [stat] = ft_timelockstatistics(cfg, timelock1, timelock2, ...)
  where the input data is the result from either FT_TIMELOCKANALYSIS or
  FT_TIMELOCKGRANDAVERAGE.
 
  The configuration can contain the following options for data selection
    cfg.channel     = Nx1 cell-array with selection of channels (default = 'all'),
                      see FT_CHANNELSELECTION for details
    cfg.latency     = [begin end] in seconds or 'all' (default = 'all')
    cfg.avgoverchan = 'yes' or 'no'                   (default = 'no')
    cfg.avgovertime = 'yes' or 'no'                   (default = 'no')
    cfg.parameter   = string                          (default = 'trial' or 'avg')
 
  Furthermore, the configuration should contain
    cfg.method       = different methods for calculating the significance probability and/or critical value
                     'montecarlo'    get Monte-Carlo estimates of the significance probabilities and/or critical values from the permutation distribution,
                     'analytic'      get significance probabilities and/or critical values from the analytic reference distribution (typically, the sampling distribution under the null hypothesis),
                     'stats'         use a parametric test from the MATLAB statistics toolbox,
                     'crossvalidate' use crossvalidation to compute predictive performance

 其中四个method 很重要 1.蒙特卡洛分析 多用于permutation test  2. 教程多用于t test 
                      3. stats 调用MATLAB函数进行统计分析 4.多用于机器学习 信号分类 

4. FT_FREQSTATISTICS computes significance probabilities and/or critical
values of a parametric statistical test or a non-parametric permutation
  test.
   频域的统计分析  很重要 
  Use as
    [stat] = ft_freqstatistics(cfg, freq1, freq2, ...)
  where the input data is the result from FT_FREQANALYSIS, FT_FREQDESCRIPTIVES
  or from FT_FREQGRANDAVERAGE.
 
  The configuration can contain the following options for data selection
    cfg.channel     = Nx1 cell-array with selection of channels (default = 'all'),
                      see FT_CHANNELSELECTION for details
    cfg.latency     = [begin end] in seconds or 'all' (default = 'all')
    cfg.frequency   = [begin end], can be 'all'       (default = 'all')
    cfg.avgoverchan = 'yes' or 'no'                   (default = 'no')
    cfg.avgovertime = 'yes' or 'no'                   (default = 'no')
    cfg.avgoverfreq = 'yes' or 'no'                   (default = 'no')
    cfg.parameter   = string                          (default = 'powspctrm')
 
  If you specify cfg.correctm='cluster', then the following is required
    cfg.neighbours  = neighbourhood structure, see FT_PREPARE_NEIGHBOURS
 
  Furthermore, the configuration should contain
    cfg.method       = different methods for calculating the significance probability and/or critical value
                     'montecarlo'    get Monte-Carlo estimates of the significance probabilities and/or critical values from the permutation distribution,
                     'analytic'      get significance probabilities and/or critical values from the analytic reference distribution (typically, the sampling distribution under the null hypothesis),
                     'stats'         use a parametric test from the MATLAB statistics toolbox,
                     'crossvalidate' use crossvalidation to compute predictive performance

  method 请参考3  

5. FT_SOURCEANALYSIS performs beamformer dipole analysis on EEG or MEG data
after preprocessing and a timelocked or frequency analysis
   溯源分析,其实是重建信号的一个过程 ,做功能连接必用
  Use as either
    [source] = ft_sourceanalysis(cfg, freq)
    [source] = ft_sourceanalysis(cfg, timelock)
 
  where the data in freq or timelock should be organised in a structure
  as obtained from the FT_FREQANALYSIS or FT_TIMELOCKANALYSIS function. The
  configuration "cfg" is a structure containing information about
  source positions and other options.
 
  The different source reconstruction algorithms that are implemented
  are
    cfg.method     = 'lcmv'    linear constrained minimum variance beamformer
                     'sam'     synthetic aperture magnetometry
                     'dics'    dynamic imaging of coherent sources
                     'pcc'     partial cannonical correlation/coherence
                     'mne'     minimum norm estimation
                     'rv'      scan residual variance with single dipole
                     'music'   multiple signal classification
                     'sloreta' standardized low-resolution electromagnetic tomography
                     'eloreta' exact low-resolution electromagnetic tomography
  The DICS and PCC methods are for frequency or time-frequency domain data, all other
  methods are for time domain data. ELORETA can be used both for time, frequency and
  time-frequency domain data.

        多用 LCMV  dics pcc mne  教程一般用dics 和 pcc   各位自编就行, 博主推荐dics  pcc

6.FT_CONNECTIVITYANALYSIS computes various measures of connectivity between
  MEG/EEG channels or between source-level signals.
 Filetrip大杀器, 连接分析,提供了各种各样的method ,
  Use as
    stat = ft_connectivityanalysis(cfg, data)
    stat = ft_connectivityanalysis(cfg, timelock)
    stat = ft_connectivityanalysis(cfg, freq)
    stat = ft_connectivityanalysis(cfg, source)
  where the first input argument is a configuration structure (see below)
  and the second argument is the output of FT_PREPROCESSING,
  FT_TIMELOCKANLAYSIS, FT_FREQANALYSIS, FT_MVARANALYSIS or FT_SOURCEANALYSIS.
 
  The different connectivity metrics are supported only for specific
  datatypes (see below).
 
  The configuration structure has to contain
    cfg.method  =  string, can be
      'amplcorr',  amplitude correlation, support for freq and source data
      'coh',       coherence, support for freq, freqmvar and source data.
                   For partial coherence also specify cfg.partchannel, see below.
                   For imaginary part of coherency or coherency also specify
                   cfg.complex, see below.
      'csd',       cross-spectral density matrix, can also calculate partial
                   csds - if cfg.partchannel is specified, support for freq
                   and freqmvar data
      'dtf',       directed transfer function, support for freq and
                   freqmvar data
      'granger',   granger causality, support for freq and freqmvar data
      'pdc',       partial directed coherence, support for freq and
                   freqmvar data
      'plv',       phase-locking value, support for freq and freqmvar data
      'powcorr',   power correlation, support for freq and source data
      'powcorr_ortho', power correlation with single trial
                   orthogonalisation, support for source data
      'ppc'        pairwise phase consistency
      'psi',       phaseslope index, support for freq and freqmvar data
      'wpli',      weighted phase lag index (signed one,
                   still have to take absolute value to get indication of
                   strength of interaction. Note: measure has positive
                   bias. Use wpli_debiased to avoid this.
      'wpli_debiased'  debiased weighted phase lag index
                   (estimates squared wpli)
      'wppc'       weighted pairwise phase consistency
      'corr'       Pearson correlation, support for timelock or raw data

不一一解释,自己动手,玩玩吧