Five-class finger flexion classification using ECoG signals

Increasing the number of car accidents and other cerebral disease cause to progress in using Brain-Compute Interface (BCI) as a common subject for research and treatment. The aim of Brain-Computer Interface system is to establish a new communication system that translates human intentions, reflected by brain signals, into a control signal for an output device such as a computer. To this end, different processes must be done on brain signals and these signals must be classified by suitable methods. There are various methods to classify ECoG signals which are different in features and classifiers. Used features depend on extracted features, feature reduction methods and measures of feature selection. So, for a specific data set, we can use different algorithms with different results. The purpose of this paper is finding the best algorithm to do a five-class finger flexion classification to choose flexed finger among one hand´s fingers. To achieve this goal, after feature extraction, some different methods of feature reduction and classification examined on training data and the best algorithm is selected according to the achieved results.