Exploration of Web-Sites Affects Autonomic Responses Related to Unconscious Emotions.

In this work we are interested in analyzing any correlations between physiological parameters, extracted from signals such as Electrocardiogram, respiratory signal and Skin Conductance, and self-reported indices related to emotional or cognitive stimulations. For this purpose, an experiment involving twenty participants with a mean age of 25±5 years of both sexes (13 males and 7 females) was carried out. The protocol included the navigation in simulated web-sites and the vision of two different commercial products (utilitarian and hedonistic). At the end of the navigation, a questionnaire was submitted to the subject in order to measure his/her feelings and emotions in a qualitative and subjective way. Quantitative features were extracted from the physiological signals recorded during the execution of the protocol. We performed a correlation analysis between self-reported and physiological responses related to Arousal, Pleasure, Expectancy and Situational Involvement. Findings showed that when a consumer is exposed to a utilitarian product, the physiological emotional responses are disassociated from the self-reported ones. For the hedonistic product, instead, self-reported measures significantly correlate with physiological arousal features like the combined effect of cardiac and respiratory activity and the Heart Rate.

Clusters of mu rhythm from EEG data: A comparative study between 61 and 19 channel datasets.

The EEG mu rhythm is a sensorimotor oscillation which is desynchronized by voluntary movement execution. Independent Component Analysis (ICA) allows the decomposition of recorded scalp EEG data into temporally, functionally, and spatially independent source signals. Clustering techniques applied to independent sources resolved with ICA have been proven to be successful in the identification of clusters of sensorimotor mu rhythm across different subjects. The present work deals with the issue regarding the minimum number of data channels that is recommended to find reliable clusters of mu rhythm. Left and right mu clusters were identified from high-density EEG recordings (61 channels) belonging to a publicly available EEG database. A second dataset was created by selecting a small subset of the same high-density EEG recordings. Specifically, only the 19 channels belonging to the standard 10-20 International System were used for the identification of left and right mu clusters. Quantitative parameters computed from mu clusters obtained from both the 61-channel and the 19-channel datasets were statistically compared. The obtained results suggest that clusters of mu rhythm in sensorimotor areas can be reliably found from a lower number of EEG channels compared to high-density electrodes configuration.

ERP and Adaptive Autoregressive identification with spectral power decomposition to study rapid auditory processing in infants.

The ability to process rapidly-occurring auditory stimuli plays an important role in the mechanisms of language acquisition. For this reason, the research community has begun to investigate infant auditory processing, particularly using the Event Related Potentials (ERP) technique. In this paper we approach this issue by means of time domain and time-frequency domain analysis. For the latter, we propose the use of Adaptive Autoregressive (AAR) identification with spectral power decomposition. Results show EEG delta-theta oscillation enhancement related to the processing of acoustic frequency and duration changes, suggesting that, as expected, power modulation encodes rapid auditory processing (RAP) in infants and that the time-frequency analysis method proposed is able to identify this modulation.