Processing of brain signals from visual and auditory stimuli using wavelet analysis and artificial neural networks

Abstract

This article presents the design and development of a portable prototype for the acquisition, processing and classification of EEG signals with the aim of characterizing visual and auditory stimuli. Two different patients were worked with to validate the results, and the signals were recorded for 4 seconds at a frequency of 500Hz. The patients were exposed to visual and auditory stimuli in different cases, whose frequency of appearance remained constant. For the recording of the signals, a 4-channel acquisition system was designed, which could be configured to work with unipolar or bipolar derivation, as required by the experiment. The selection of the best base in the multi-resolution wavelet analysis, two important parameters were taken into account, the measurement of entropy and the percentages of classification of these levels, because the evoked potentials are generally constant in their morphology, it was made coherent averaging giving as a result the space-time location where this evoked potential appears, Once the characteristics of the treated signal were obtained, they were classified using two different methods of artificial intelligence, neural networks and vector support machines. At this stage, the measurement of the standard deviation of the data was taken into account to ensure that the learning machine was trained correctly. The results obtained reliably demonstrate the general behaviour of the evoked potentials as a result of the stimuli presented. In addition, it was possible to verify the variation of the patient's alpha waves according to his or her state of relaxation or alert in each case, it is advisable to carry out a much more robust filtering system to increase the signal-to-noise ratio of the EEG signal, facilitate its analysis and improve the results.