title: Feature subset selection for age-related changes in EEG and EMG during motor tasks.
abstract: The paper presents an ongoing investigation into the feasibility of distinguishing between healthy young and older adults, but more specifically into the nature of the features that would provide this distinction. The present study compared the performance of forward, backward, and branch and bound feature selection algorithms when applied to electroencephalography and electromyography data. The results showed that the forward selection algorithm outperformed the other techniques for this particular problem. In addition, time domain features were primarily selected over frequency domain features. Validation of the selected subset suggests the approach is appropriate for future investigation.
authors.affiliation: Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250, USA. ashley.johnson@gatech.edu
authors.forename: Ashley N
authors.initials: AN
authors.lastname: Johnson
authors.forename: George J
authors.initials: GJ
authors.lastname: Vachtsevanos
authors.forename: Minoru
authors.initials: M
authors.lastname: Shinohara
mesh_terms: D000328:Adult; D000375:Aging / Q000502:physiology*; D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*