Neural network based classification of single-trial EEG data

摘要

Standard Back Propagation (BP), Partially Recurrent (PR) and Cascade-Correlation (CC) neural networks were used to predict the side of finger movement on the basis of non-averaged single trial multi-channel EEG data recorded prior to movement. From these EEG data, power values were calculated and used as parameters for classification.The results obtained on three subjects show that the Cascade-Correlation neural network is an appropriate choice for neural network based classification of spatio-temporal single-trial EEG pattems. It is fast, stable and able to discover and recognize underlying dynamics of rhythmic activities within the alpha band which precede execution of hand movements.