#Matlab 2009 grid alpha series
#Matlab 2009 grid alpha windows

Here, ITC briefly increases at about 10 Hz (at about the same time as the power increase in the upper panel). A significant ITC indicates that the EEG activity at a given time and frequency in single trials becomes phase-locked (not phase-random with respect to the time-locking experimental event). We previously encountered the ITC when we explained the ERP image plotting. The lower image shows the Inter-Trial coherence (ITC) at all frequencies. The left panel adjacent to the top image shows the baseline mean power spectrum, and the lower part of the top image, the ERSP envelope (low and high mean dB values, relative to baseline, at each time in the epoch). The ERSP image shows a brief decrease in power at about 370 ms at 8 Hz (click on the image to zoom in and determine the exact frequency), a power increase centered at 13.5 Hz and starting at 300 ms. Enabling MATLAB zoom allows zooming into any desired time/frequency window. To inspect these changes more closely, click on the color image. The top image shows mean event-related changes in spectral power (from pre-stimulus baseline) at each time during the epoch and at each frequency (< 50 Hz). With 3 cycles, the lowest frequency is 3 Hz. If we were using 1 cycle, the lowest frequency would be 1 Hz. This would specify that the window length (вЂwinlenвЂ™) at the lowest frequency should be xxx samples long (i.e., 1 sec long). To make this lowest frequency near 3 Hz, we would need to add the optional newtimef.m argument вЂњ вЂwinlenвЂ™, xxx вЂњ where xxx is the sampling rate of the data (also shown on the blue EEGLAB menu window). Using three cycles in 0.5 seconds sets the lowest frequency analyzed to about 6 Hz. By current default, the lowest frequency window is about 0.5 seconds long. Note: This information does not set the lowest frequency to be analyzed.

#Matlab 2009 grid alpha windows

This controls the shapes of the individual time/frequency windows measured by the function and their shapes in the resulting time/frequency panes. The вЂ0.8вЂ™ here means that the number of cycles in the wavelets used for higher frequencies will continue to expand slowly, reaching 20% (1 minus 0.8) of the number of cycles in the equivalent FFT window at its highest frequency. As explained in the help message for the newtimef.m function, this means that the wavelet used to measure the amount and phase of the data in each successive, overlapping time window will begin with a 3-cycle wavelet (with a Hanning-tapered window applied). Note the default вЂњWavelet cyclesвЂќ entry is 3 0.8. Below, we enter 14 (Cz) for the Channel number.

To detect transient event-related spectral perturbation, or ERSP, (Makeig, 1993) (event-related shifts in the power spectrum) and inter-trial coherence (ITC) (event-related phase-locking) events in epoched EEGLAB datasets, select Plot в†’ Time-frequency transforms в†’ Channel time-frequency calling the pop_newtimef.m function. Select the File в†’ load existing dataset menu item and select the tutorial file вЂњeeglab_data_epochs_ica.setвЂќ located in the вЂњsample_dataвЂќ folder of EEGLAB. We use here the tutorial dataset as it was after extracting data epochs. ERSP and ITC time-frequency analysis Time-frequency images If you click on the icon on the top right corner, you can see the list of all the videos in the playlist.

#Matlab 2009 grid alpha series

You may also want to watch the short series of videos on computing time-frequency decompositions in EEGLAB (hosted on Youtube) below. Their mathematical details are given in a reference paper (Delorme and Makeig, 2004). The methods used in the basic EEGLAB functions are straightforward. There are a long history and much recent development of methods for time/frequency decomposition. Time/frequency analysis characterizes changes or perturbations in the spectral content of the data considered as a sum of windowed sinusoidal functions (i.e., sinusoidal wavelets).