FEATURES EXTRACTION METHOD FOR BRAIN-MACHINE COMMUNICATION BASED ON THE EMPIRICAL MODE DECOMPOSITION

DIEZ P.; MUT, V.; LACIAR E.; TORRES A.; AVILA E.

BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS

WORLD SCIENTIFIC PUBL CO PTE LTD

Lugar: London, UK; Año: 2013 vol. 25 p. 1 - 13

1016-2372

A Brain-Machine Interface (BMI) is a communication system that translates human brain activity into commands, and then these commands are conveyed to a machine or a computer. It is proposed a technique for features extraction from electroencephalographic (EEG) signals and afterward, their classification on different mental tasks. The Empirical Mode Decomposition (EMD) is a method capable of processing non-stationary and non-linear signals, as the EEG. It was applied on EEG signals of 7 subjects performing 5 mental tasks. Six features were computed, namely, Root Mean Square (RMS), Variance, Shannon Entropy, Lempel-Ziv Complexity Value, and Central and Maximum Frequencies. In order to reduce the dimensionality of the feature vector the Wilks? lambda parameter, was used for the selection of the most important variables. The classification of mental tasks was performed using Linear Discriminant Analysis (LDA) and Neural Networks (NN). Using this method, the average classification over all subjects in database is 91±5% and 87±5% using LDA and NN, respectively. Bit rate was ranging from 0.24 bits/trial up to 0.84 bits/trial. The proposed method allows achieving higher performances in the classification of mental tasks than other traditional methods, such as spectral analysis using the same database. This represents an improvement in the Brain-machine communication system.