Yes
NITRC
Causality Challenge EEG Data
The data consists of 1000 examples of bivariate data for 6000 time points. Each example is a superposition of a signal (of interest) and noise. The signal is constructed from a unidirectional bivariate AR-model of order 10 with (otherwise) random AR-parameters and uniformly distributed input. The noise is constructed of three independent sources, generated with 3 univariate AR-models with random parameters and uniformly distributed input, which were instantaneously mixed into the two sensors with a random mixing matrix. The relative strength of noise and signal was set randomly. The data were generated with this [Matlab code]. (Of course, the seeds for the random number generators chosen for the challenge data are confidential.)
Causality Challenge EEG Data
EEG/MEG
http://www.nitrc.org/projects/causality_eeg/, http://http://clopinet.com/causality/data/nolte/