Classification Of Hand Movement In Eeg Using Erd/Ers And Multilayer Perceptron

Continuous EEG activity in the measured subjects includes different patterns depending on what activity the subject performed. ERD and ERS are examples of such patterns related to movement, for example of a hand, finger or foot. This article deals with the detection of motion based on the ERD/ERS patterns. By linking ERD/ERS, feature vectors which are later classified by neural network are created. The resulting neural network consists of one input and one output layer and two hidden layers. The first hidden layer contains 3,000 neurons and the second one 1,500 neurons. A training set of feature vectors is used for the training of this neural network and the back-propagation algorithm is used for the subsequent adjustment of the weights. With this setting and training, the neural network is able to classify motion in an EEG record with an average accuracy of 79.92%.