Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings. Application to Alzheimer's disease.

Explorer Blind source separation to enhance spectral and non - linear features of magnetoencephalogram recordings

Comparison of Blind Source Separation Preprocessings Applied to Magnetoencephalogram Recordings to Improve the Classification of Alzheimer’s Disease Patients

This study compares diverse Blind Source Separation (BSS) techniques applied to magnetoencephalogram (MEG) background activity in order to improve the classification of Alzheimer’s Disease (AD) patients. MEG recordings from 18 AD patients and 13 control subjects were decomposed with the following BSS algorithms: AMUSE, SOBI, FastICA, and extended Infomax. Whereas AMUSE ranked the extracted BSS ...

Assessment of classification improvement in patients with Alzheimer's disease based on magnetoencephalogram blind source separation

OBJECTIVES In this pilot study, we intended to assess whether a procedure based on blind source separation (BSS) and subsequent partial reconstruction of magnetoencephalogram (MEG) recordings might enhance the differences between MEGs from Alzheimer's disease (AD) patients and elderly control subjects. MATERIALS AND METHODS We analysed MEG background activity recordings acquired with a 148-ch...

Blind Signal Processing Methods for Analyzing Multichannel Brain Signals

A great challenge in neurophysiology is to asses non-invasively the physiological changes occurring in different parts of the brain. These activation can be modeled and measured often as neuronal brain source signals that indicate the function or malfunction of various physiological subsystems. To extract the relevant information for diagnosis and therapy, expert knowledge is required not only ...

Early Detection of Alzheimer’s Disease by Blind Source Separation and Bump Modeling of EEG Signals

The early detection Alzheimer’s disease is an important challenge. Using blind source separation, wavelet time-frequency transforms and “bump modeling” of electro-encephalographic (EEG) recordings, a set of features describing the recordings of mildly impaired patients and of controls subject is built. Feature selection by the random probe method leads to the selection of a few reliable feature...