Selection of a suitable wavelet for cognitive memory using electroencephalograph signal

Abstract

The aim of this study is to recognize the best and suitable wavelet family for analyzing cognitive memory using Electroencephalograph (EEG) signal. The participant was given some visual stimuli during the study phase, which were a sequence of pictures that had to be remembered to acquire the EEG signal. The Neurofax EEG 9200 was used to record the acquisition of cognitive memory at channel Fz. The raw EEG signals were analyzed using Wavelet Transform. A lot of mother wavelets can be used for analyzing the signal, but do not lose any information on the wavelet, some predictions must be made beforehand. The criteria of the EEG signal were narrowed down to the Daubechies, Symlets and Coiflets, and it is the final selection depending on their Mean Square Error (MSE). The best solution would have the least difference between the original and constructed signal. Results indicated that the Daubechies wavelet at a level of decomposition of 4 (db4) was the most suitable wavelet for pre-processing the raw EEG signal of cognitive memory. To conclude, choosing the suitable wavelet family is more important than relying on the MSE value alone to successfully perform a wavelet transformation.