title: EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease.
abstract: OBJECTIVE
  Development of an EEG preprocessing technique for improvement of detection of Alzheimer's disease (AD). The technique is based on filtering of EEG data using blind source separation (BSS) and projection of components which are possibly sensitive to cortical neuronal impairment found in early stages of AD.
  
  
  METHODS
  Artifact-free 20s intervals of raw resting EEG recordings from 22 patients with Mild Cognitive Impairment (MCI) who later proceeded to AD and 38 age-matched normal controls were decomposed into spatio-temporally decorrelated components using BSS algorithm 'AMUSE'. Filtered EEG was obtained by back projection of components with the highest linear predictability. Relative power of filtered data in delta, theta, alpha 1, alpha 2, beta 1, and beta 2 bands were processed with Linear Discriminant Analysis (LDA).
  
  
  RESULTS
  Preprocessing improved the percentage of correctly classified patients and controls computed with jack-knifing cross-validation from 59 to 73% and from 76 to 84%, correspondingly.
  
  
  CONCLUSIONS
  The proposed approach can significantly improve the sensitivity and specificity of EEG based diagnosis.
  
  
  CONCLUSIONS
  Filtering based on BSS can improve the performance of the existing EEG approaches to early diagnosis of Alzheimer's disease. It may also have potential for improvement of EEG classification in other clinical areas or fundamental research. The developed method is quite general and flexible, allowing for various extensions and improvements.
authors.affiliation: Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan. cia@brain.riken.jp
authors.forename: Andrzej
authors.initials: A
authors.lastname: Cichocki
authors.forename: Sergei L
authors.initials: SL
authors.lastname: Shishkin
authors.forename: Toshimitsu
authors.initials: T
authors.lastname: Musha
authors.forename: Zbigniew
authors.initials: Z
authors.lastname: Leonowicz
authors.forename: Takashi
authors.initials: T
authors.lastname: Asada
authors.forename: Takayoshi
authors.initials: T
authors.lastname: Kurachi
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000503:physiopathology; D016022:Case-Control Studies; D002540:Cerebral Cortex / Q000503:physiopathology*; D042241:Early Diagnosis; D004569:Electroencephalography; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D013057:Spectrum Analysis