PERFORMANCE EVALUATION OF CLASSIFIERS IN DISTINGUISHING MENTAL TASKS FROM EEG SIGNALS

Isaak Kavasidis, Carmelo Pino, Concetto Spampinato, Francesco Maiorana

Keywords

Brain Computer Interface, Performance Evaluation,Classifiers, Combiners

Abstract

During the last decade a lot of research has been done to study the possibility of voluntarily controlling ma- chines by EEG signals. To accomplish that, very ac- curate recognition of the intended task is needed and so, given the variability of the human brain’s signals, discriminant features and high performance classifiers are demanded. In this paper, a study on the perfor- mance of different classifiers for distinguishing three mental tasks using EEG signals, is presented. The ac- quired EEG data is filtered and processed, and a set of nine features about power, the signals’ synchroniza- tion, and instantaneous frequency are extracted. Clas- sification performance was analyzed across subjects using seventeen individual classifiers. The results ob- tained from each classifier were also combined in or- der to evaluate both the efficiency of individual clas- sifiers and the use of combiners.

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