A Brain Computer Interface (BCI) using Fractional Fourier Transform with Time Domain normalization and heuristic weight adjustment

The brain computer interface (BCI) using the fractional Fourier transform (FRFT) arises as the generalization of work done on thought classification through the Fourier transform (FT), given that the FT is a particular case of the FRFT, thus adding new dimensions that increase the accuracy of the correct thought classification. In this work time domain normalization has been used in the preprocessing stage. Fractional Fourier transform is used for the purpose of feature extraction. Exact radial basis neural networks are used for the classification of signals and heuristic weight adjustment of neural networks was done in final decision making. Tests carried out on two different datasets give an accuracy of more than 84% and 87% respectively.