NDLI: Application of the empirical mode decomposition to the extraction of features from EEG signals for mental task classification

Content Provider	IEEE Xplore Digital Library
Author	Diez, P.F. Mut, V. Laciar, E. Torres, A. Avila, E.
Copyright Year	2009
Description	Author affiliation: Instituto de Automática, Facultad de Ingeniería. UNSJ, San Juan, Argentina (Mut, V.) \|\| Dept. ESAII, Universitat Politècnica de Catalunya, Institut de Bioenginyeria de Catalunya (IBEC) and CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain (Torres, A.) \|\| Laboratorio de Electrónica Digital (LED), Facultad de Ingeniería. UNSJ, Argentina (Avila, E.) \|\| Gabinete de Tecnología Médica (GATEME), Universidad Nacional de San Juan (UNSJ), Argentina (Diez, P.F.; Laciar, E.)
Abstract	In this work, it is proposed a technique for the feature extraction of electroencephalographic (EEG) signals for classification of mental tasks which is an important part in the development of Brain Computer Interfaces (BCI). The Empirical Mode Decomposition (EMD) is a method capable to process nonstationary and nonlinear signals as the EEG. This technique was applied in EEG signals of 7 subjects performing 5 mental tasks. For each mode obtained from the EMD and each EEG channel were computed six features: Root Mean Square (RMS), Variance, Shannon Entropy, Lempel-Ziv Complexity Value, and Central and Maximum Frequencies, obtaining a feature vector of 180 components. The Wilks' lambda parameter was applied for the selection of the most important variables reducing the dimensionality of the feature vector. The classification of mental tasks was performed using Linear Discriminate Analysis (LD) and Neural Networks (NN). With this method, the average classification over all subjects in database was 91±5% and 87±5% using LD and NN, respectively. It was concluded that the EMD allows getting better performances in the classification of mental tasks than the obtained with other traditional methods, like spectral analysis.
Starting Page	2579
Ending Page	2582
File Size	482770
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5335278
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Electroencephalography Vectors Neural networks Application software Brain computer interfaces Signal processing Root mean square Entropy Frequency
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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