Jorge Del Río Vera 		BCI competition 2003

data set iv (berlin)

Algorithm

	The algorithm used is based in a MLP neural network and a key
	feature extraction based in PCA (principal component analysis).

	The principal component analysis is computed for each channel, then
	the second principal mode is used (I have not used the first because
	it contains the normal activity of the brain instead the features to
	discriminate between left or rigth hand). This mode is correlated
	with the signals (only the signals of the left side of the brain are
	used, beacuse the information of the laterallity is contained in the
	second principal mode) and the delay of the maximun is used for
	training the net (a 12 component vector).  The net used is a MLP
	with two inner layers in order to accelerate the convergence (10 and
	5 neurons respectively) and the output layer has two neurons.


Results

	Results are given in a matlab data file, it have the same order than
	the test signals and the class is given by a 0 or a 1.