Decomposition algorithms for analysing brain signals

Author

Müller, Klaus-Ro Bert ; Kohlmorgen, Jens ; Ziehe, Andreas ; Blankertz, Benjamin

Author_Institution

GMD FIRST.IDA, Berlin, Germany

fYear

2000

fDate

2000

Firstpage

105

Lastpage

110

Abstract

Analyzing biomedical data-e.g. from the brain-we encounter fundamental problems that lie largely in the fields of signal processing and machine learning. The current paper presents at first a method to deal with non-stationary signals, subsequently the signal processing technique of independent component analysis (ICA) is reviewed. We use EEG recordings of continuous auditory perception as illustration for the discussed algorithms

Keywords

electroencephalography; hearing; learning (artificial intelligence); medical signal processing; statistical analysis; EEG recordings; biomedical data analysis; brain signals analysis; continuous auditory perception; decomposition algorithms; independent component analysis; machine learning; nonstationary signals; signal processing; Algorithm design and analysis; Biomedical signal processing; Data analysis; Electroencephalography; Independent component analysis; Machine learning; Machine learning algorithms; Signal analysis; Signal processing; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC. The IEEE 2000

Conference_Location

Lake Louise, Alta.

Print_ISBN

0-7803-5800-7

Type

conf

DOI

10.1109/ASSPCC.2000.882455

Filename

882455