Hilbert Huang Transform (Hht) Applied to Memorization of Objects Using the Tactile Sense

The information given by the evolution of a natural system is generally a non-linear, non-recurrent and non-stationary temporal series. Therefore, when analyzing these data with linear and stationary formalism (such as FFT) or a normative base (FFT, Wavelet), signals is introduced that are not part of the original set, and their interpretation is inadequate. So, it is necessary to introduce an adaptive and empirical formalism, which allows analyzing an non stationary and non-linear signal, breaking in sub signals of intrinsic mode (IMF) characterized by its frequency and instantaneous amplitude. The temporal analysis of the frequency is useful because in certain phenomena, the evolution of it gives valuable information on the analyzed system. In the process, the tactile perception associated with the capture of information excites the sensory synapse, activating for approx. 1-2 seconds the sensory memory (SeM) and if a level of sufficient attention is exceeded, the sensory information is encoded and controlled by the working memory (WoM), activating actions of knowledge registration in the synaptic mental form, enabling the process storage of this information, in the long-term memory. In this context, this paper presents the analysis of an EEG signal through the Hilbert Huang Transform (HHT), applied to memorization of objects using the tactile sense that is, depriving the individual of vision and hearing. An empirical mode decomposition (EMD) is developed and the Hilbert spectral analysis is obtained revealing the temporal-frequency energy distribution of phenomena. As a result of this process an activity marker appears that can be associated to the wait (timeout) between the memorization of two objects corresponding to 0.2 sec. The activity marker is located in the second component of signal intrinsic mode (IMF2). When observing the instantaneous frequency, after the subject in study leaves a first object and prepares to receive the second, it is perceived that this frequency varies between 3 and 7 Hz for the channels AF3, F7, F3, FC5 of the left frontal lobe. This pattern is repeated in the right frontal lobe, but disappears more quickly. Finally, it can be concluded that Hilbert Huang Transform allows discriminating instantaneous frequencies and locating patterns not detectable by traditional systems of signal analysis. © 2018 IEEE.