Optimal segments selection for EEG classification

Electroencephalography is non invasive technique used to measure electrical brain activity and to collect EEG signals, through surface electrodes properly positioned on the scalp, which analyzed and interpreted in BCI (Brain Computer Interface) allow the comprehension of human intensions of movement. This is particularly useful for persons with severe physical handicaps. In this paper dataset of motor imagery is used to describe left and right hand movement imagery. To extract discriminating features from EEG signals, wavelet transform is a concrete method which deals with time frequency aspect of non stationary EEG signals. Indeed, the creation of statistical parameters describing wavelet coefficients to build features vectors gives good classification results when used with Linear Discriminate Analysis (LDA) or Neural Networks.