Although there is growing interest in studying the long‐ignored relationship between stratification and race in Chile, racial bias in person perception remains unknown. We hypothesize that the segregation of the Chilean school system generated a prestige order in which pupils are differentiated by status characteristics according to the type of school they attend, and that these evaluations are based on racial traits. To test this hypothesis, we study whether facial appearance is sufficient to impute the type of school a pupil is attending, and whether these categorizations evoke different status evaluations of wealth and morality based on race. Results confirm that participants’ perceptions of facial appearance allow them to situate pupils in the Chilean social structure. Faces categorized as studying at different types of schools varied in their perceived wealth. However, the relationship between moral traits and types of schools was weak. We also found evidence of racial bias in the participants’ perceptions of pupils’ faces: faces categorized as enrolled in municipal schools (low status) were judged with Amerindian or mestizo racial traits, while faces categorized as attending private fee‐paying schools (high status) were judged with white racial traits. We did not find a relationship between race and morality.

Interactions with consumers in a retail environment play a fundamental role to increase sales, improve satisfaction and promote loyalty among such consumers, at the same time of improve financial results that may sustain a company long-term. Such face-to-face or online interactions have several components that may cause an improved State of Empowerment perception resulting in a relevant satisfaction, thus, loyalty and retention. For such purpose, the actual study comes to analyze what are those behaviors or actions that bring into play such perception, and how such perception relates to an improved customers' satisfaction.

Recent decades have seen BCI applications as a novel and promising new channel of communication, control and entertainment for disabled and healthy people. However, BCI technology can be prone to errors due to the basic emotional state of the user: the performance of reactive and active BCIs decrease when user becomes stressed or bored, for example. Passive-BCI is a recent approach that fuses BCI technology with cognitive monitoring, providing valuable information about the user's intentions, the situational interpretations and mainly the emotional state. In this work, an architecture composed by passive-BCI co-working with SSVEP-BCI is proposed, with the aim of improving the performance of the reactive-BCI. The possibility of adjusting recognition characteristics of SSVEP-BCIs using a passive-BCI output is evaluated. In this sense, two ways to recover the accuracy of SSVEP are presented in this paper: 1) Adjusting of Amplitude of the SSVEP and 2) Adjusting of Frequency of the SSVEP response. The results are promising, because accuracy of SSVEP-BCI can be recovered in the case that it was reduced by the BCI user's emotional state.

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Abstract-In this paper, we evaluated several methods to classify electroencephalogram (EEG) signal recorded from left and right hand during motor imagery. Three subjects (two males and one woman) were volunteered to participate in this experiment. The human brain is a complex and nonlinear system; for this reason we have to try like one. We used some complexity measure techniques based on Fractal Dimension in time and frequency to extract the features patterns in EEG signal Motor Imagery. The algorithms that were selected to get the Fractal Dimension (features extraction) on time Detrended Fluctuation Analysis (DFA), Higuchis Method and on frequency was used Power Spectral Density method. Based on these algorithms we can distinguish between three states relaxing, imagination of right and left hand. After this, these features are classified with two different methods Neural Network (NN) and Support Vector Machine (SVM). Finally, the experimental results are considered to apply in a BCI application to move two robotics hands (left and right hand).

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This paper presents a comparison among three methods for Steady-State Visually Evoked Potentials (SSVEP) detection. These techniques are based on Power Spectral Density Analysis (PSDA) and Canonical Correlation Analysis (CCA). The first method estimates the signal-to-noise ratio of the power spectrum in each stimulus frequency using PSDA, which is called Traditional-PSDA. The second analysis estimates the relation between the difference of the stimulus frequency and its neighbor frequencies, using the power spectrum in these neighbor frequencies, and seeks the neighbor frequency which present the lowest relation value. This technique is referred to Ratio-PSDA. The third and final technique called Hybrid-PSDA-CCA. The performances of the methods were evaluated using a database of electroencephalogram (EEG) signals. The EEG signals were recorded from 19 volunteers, from which six people present disabilities. They were stimulated with visual stimuli flickering at 5.6, 6.4, 6.9 and 8.0 Hz. The system performance was evaluated considering the accuracy and the Information Transfer Rate (ITR) for each stimulus frequency. The results showed that the Hybrid-PSDA-CCA method achieved the best result with an average accuracy of 91.44%.

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