Backpropagation and EEG Data.

摘要

The development of neural networks has pursued a myriad of different courses reflecting the interests of a large number of researchers from highly varied backgrounds. This paper addresses focuses on one point of this 'many faceted gem', as Stephen Grossberg described the field. The point of focus will be to address some of the practical results of applying a backpropagation trained net to raw electroencephalogram (EEG) data. Much important work on more efficient training rules has been done; however, equally critical is consideration of the information content of the data, the net size, number of hidden nodes and order of training data. This paper explores some of the training issues raised by applying backpropagation to this very complex data.