Discrimination between Two Mental States (Rest and Motor Image) Using Wavelet Transform and Neural Networks.

Conference PaperinLecture Notes in Computer Science · January 2001with1 Read
Impact Factor: 0.51 · DOI: 10.1007/3-540-45493-4_5 · Source: DBLP
Conference: Computational Intelligence, Theory and Applications, International Conference, 7th Fuzzy Days, Dortmund, Germany, October 1-3, 2001, Proceedings


    This paper presents a method for the processing and classification of electroencephalographic (EEG) signals linked to mental
    states (rest and motor image) using the wavelet transform of these signals as input information of an LVQ neural network.
    This system obtained a 70% correct qualification rate in the first recording session, a 50% rate in the second and an 80%
    rate in the third, with a 75% classification success rate for the whole set of data. These results fall within the average
    range obtained by other systems which require more information.