EEGLAB11_0_4_4B

Contents

1 License and disclaimer 3

5 EOG and EMG removal using spatial filters 8

6 Correction examples 12

7 Version history 13

8 Acknowledgments 21

1 License and disclaimer

Rights, ownership, and copyright information related to each MATLAB file

included in the AAR toolbox are individually stated in each file. This infor-

mation can also be found in the document readme.aar.txt, which is included

in the public release of this software. The current release is relatively stable

and many bugs have been corrected. However, it is intented to be used for

with the symbol $ represents a non-literal string, i.e. a string whose value

might be different for different operating systems, MATLAB versions, etc.

For instance, $MATLABROOT denotes the folder where MATLAB is installed.

3 Installation

Note that the current release of the t oolbox (v1.3, release 04.12.2007) has

only been tested under MATLAB 7.4.0 (R2007a) and it might not work

properly on older versions of MATLAB. Some features of the toolbox (e.g.

the emg psd criterion) require MATLAB’s Signal Processing Toolbox v6.2 or

newer. Such version of the Signal Processing Toolbox is usually included in

MATLAB 7.0 (R14) and newer MATLAB releases.

if a small enough learning step is used. However, small learning steps lead

to very slow convergence. The second algorithm is based on Recursive Least

Squares (RLS) [9] which offers a much greater convergence speed at t he cost

of reduced numerical stability. The third algorithm is a numerically stable

version of the RLS algorithm [14]. Note that the implementation of this lat-

ter algorithm is still very naive and inefficient so the r equired computation

time can be very large. The two last algo r ithms are based on H

ples [16, 17]. The most importa nt information related to these regression

algorithms is summarized in Table. 1.