title: Motor imagery EEG discrimination using the correlation of wavelet features.
abstract: A novel method for motor imagery (MI) electroencephalogram (EEG) data classification is proposed in this study. Time-frequency representation is constructed by means of continuous wavelet transform from EEG signals and then weighted with 2-sample t-statistics, which are also used to automatically select the area of interest in advance. Finally, normalized cross-correlation is used to discriminate the test MI data. Compared with the nonweighted version on MI data, the experimental results indicate that the proposed system achieves satisfactory results in the applications of brain-computer interface (BCI).
authors.affiliation: Department of Information Management, National Chung Cheng University Advanced Institute of Manufacturing with High-tech Innovations, National Chung Cheng University shenswy@gmail.com shen@csie.ncku.edu.tw.
authors.forename: Wei-Yen
authors.initials: WY
authors.lastname: Hsu
mesh_terms: D001931:Brain Mapping / Q000379:methods*; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology; D006801:Humans; D007092:Imagination / Q000502:physiology*; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D058067:Wavelet Analysis
keywords: brain-computer interface (BCI); electroencephalography (EEG); motor imagery (MI); normalized cross correlation; time-frequency representation; two-sample t-statistics