title: Evolutionary optimization of classifiers and features for single-trial EEG discrimination.
abstract: BACKGROUND
  State-of-the-art signal processing methods are known to detect information in single-trial event-related EEG data, a crucial aspect in development of real-time applications such as brain computer interfaces. This paper investigates one such novel approach, evaluating how individual classifier and feature subset tailoring affects classification of single-trial EEG finger movements. The discrete wavelet transform was used to extract signal features that were classified using linear regression and non-linear neural network models, which were trained and architecturally optimized with evolutionary algorithms. The input feature subsets were also allowed to evolve, thus performing feature selection in a wrapper fashion. Filter approaches were implemented as well by limiting the degree of optimization.
  
  
  RESULTS
  Using only 10 features and 100 patterns, the non-linear wrapper approach achieved the highest validation classification accuracy (subject mean 75%), closely followed by the linear wrapper method (73.5%). The optimal features differed much between subjects, yet some physiologically plausible patterns were observed.
  
  
  CONCLUSIONS
  High degrees of classifier parameter, structure and feature subset tailoring on individual levels substantially increase single-trial EEG classification rates, an important consideration in areas where highly accurate detection rates are essential. Also, the presented method provides insight into the spatial characteristics of finger movement EEG patterns.
authors: Department of Neuroscience and Physiology, Göteborg University, Göteborg, 413 90, Sweden. malin.aberg@neuro.gu.se
authors: Malin C B
authors: MC
authors: Aberg
authors: Johan
authors: J
authors: Wessberg
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001931:Brain Mapping / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D009044:Motor Cortex / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*
references: Med Eng Phys. 2000 Jun;22(5):345-8
references: 11121767
references: Biol Cybern. 2002 Feb;86(2):89-95
references: 11908842
references: Clin Neurophysiol. 2002 Jun;113(6):767-91
references: 12048038
references: Evol Comput. 2002 Summer;10(2):99-127
references: 12180173
references: IEEE Trans Neural Syst Rehabil Eng. 2002 Sep;10(3):170-7
references: 12503782
references: IEEE Trans Neural Syst Rehabil Eng. 2003 Jun;11(2):141-4
references: 12899257
references: Psychophysiology. 2006 Nov;43(6):517-32
references: 17076808
references: J Neurosci Methods. 2004 Apr 30;134(2):159-68
references: 15003382
references: IEEE Trans Biomed Eng. 2004 Jun;51(6):954-62
references: 15188863
references: IEEE Trans Biomed Eng. 2004 Jun;51(6):1019-25
references: 15188873
references: Electroencephalogr Clin Neurophysiol. 1995 Mar;96(2):183-93
references: 7535223
references: Brain Lang. 1999 Jan;66(1):89-107
references: 10080866
references: Neurosci Lett. 2005 Jul 1-8;382(1-2):169-74
references: 15911143
references: IEEE Trans Neural Syst Rehabil Eng. 2003 Jun;11(2):165-9
references: 12899264