Isaak Kavasidis, Carmelo Pino, Concetto Spampinato, Francesco Maiorana
Brain Computer Interface, Performance Evaluation,Classifiers, Combiners
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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