Hi everyone,

I am currently doing research which applies RQA to EEG data. This toolbox is amazing and I really appreciate the genius sharing of Dr. Marwan for this software.

The problem I am dealing with involves multi-channel EEG, which means they have N*m matrix as an input, where N is number of channels and m is number of time points. Up to now, I compute RQA measures channel by channel - but it is really slow. I wonder how it works when I directly input the N*m matrix to the crqa function, because I found this in the file:

% The input vectors can be multi-column vectors, where

% each column will be used as a component of the

% phase-space vector. However, if the first column is

% monotonically increasing, it will be used as an

% time scale for plotting.

I am wondering about how this works in terms of computation. As I understand, this means we are going to use the entire column as a vector to represents the coordinates in the re-constructed space - however, how the embedded dimension works in this case? Or does it means all embedded dimensions are set to 1 (no embedded dimension)? Any ideas will be helpful. Thanks a lot!

## Questions about multi-column input X in function crqa

- Norbert
- Expert
**Posts:**195**Joined:**Wed Jan 4, 2006 11:03**Affiliation (Univ., Inst., Dept.):**Potsdam Institute for Climate Impact Research, Germany**Location:**Potsdam, Germany**Location:**Potsdam Institute for Climate Impact Research, Germany

### Re: Questions about multi-column input X in function crqa

Hello Fan.Mi,

thanks for your kind words.

Regarding your question: it is something different when you calculate the RQA for each of the

When you use the crqa function from the toolbox for such a

In the toolbox you could sill apply embedding to the

I hope this answers has helped.

Best wishes

Norbert

thanks for your kind words.

Regarding your question: it is something different when you calculate the RQA for each of the

*N*channels separately or when you combine all*N*channels to one phase space trajectory (of then dimension*N*), because then you get only one RQA result. It depends on your research question whether it makes sense.When you use the crqa function from the toolbox for such a

*N*-dimensional vector, then it is not necessary to embed. Embedding is only necessary when you have only one time series and need to reconstruct the phase space. If the*N*channels are considered to be the*N*components of the phase space vector, then you have it already and not reconstruction/embedding is necessary.In the toolbox you could sill apply embedding to the

*N*-dimensional vector, but usually it makes only sense in some special cases.I hope this answers has helped.

Best wishes

Norbert