Search

This study presents the results of an experiment designed to investigate whether marketing videos containing mixed emotional content can sustain consumers interest longer compared to videos conveying a consistent emotional message. During the experiment, thirteen participants, wearing EEG (electroencephalographic) caps, were exposed to eight marketing videos with diverse emotional tones. Participant engagement was measured with an engagement index, a metric derived from the power of brain activity recorded over the frontal and parietal cortex and computed within three distinct frequency bands: theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz). The outcomes indicated a statistically significant influence of emotional content type (mixed vs. consistent) on the duration of user engagement. Videos containing a mixed emotional message were notably more effective in sustaining user engagement, whereas the engagement level for videos with a consistent emotional message declined over time. The principal inference drawn from the study is that advertising materials conveying a consistent emotional message should be notably briefer than those featuring a mixed emotional message to achieve an equivalent level of message effectiveness, measured through engagement duration. [ABSTRACT FROM AUTHOR]

The research described in this paper aimed to determine whether people respond differently to short and long stimuli and whether stress stimuli repeated over time evoke a habituation effect. To meet this goal, we performed a cognitive experiment with eight subjects. During this experiment, the subjects were presented with two trays of stress-inducing stimuli (different in length) interlaced with the main tasks. The mean beta power calculated from the EEG signal recorded from the two prefrontal electrodes (Fp1 and Fp2) was used as a stress index. The main results are as follows: (i) we confirmed the previous finding that beta power assessed from the EEG signal recorded from prefrontal electrodes is significantly higher for the STRESS condition compared to NON-STRESS condition; (ii) we found a significant difference in beta power between STRESS conditions that differed in length—the beta power was four times higher for short, compared to long, stress-inducing stimuli; (iii) we did not find enough evidence to confirm (or reject) the hypothesis that stress stimuli repeated over time evoke the habituation effect; although the general trends aggregated over subjects and stressors were negative, their slopes were not statistically significant; moreover, there was no agreement among subjects with respect to the slope of individual trends. [ABSTRACT FROM AUTHOR]

The paper introduces a modified version of a genetic algorithm with aggressive mutation (GAAM) called fGAAM (fast GAAM) that significantly decreases the time needed to find feature subsets of a satisfactory classification accuracy. To demonstrate the time gains provided by fGAAM both algorithms were tested on eight datasets containing different number of features, classes, and examples. The fGAAM was also compared with four reference methods: the Holland GA with and without penalty term, Culling GA, and NSGA II. Results: (i) The fGAAM processing time was about 35% shorter than that of the original GAAM. (ii) The fGAAM was also 20 times quicker than two Holland GAs and 50 times quicker than NSGA II. (iii) For datasets of different number of features, classes, and examples, another number of individuals, stored for further processing, provided the highest acceleration. On average, the best results were obtained when individuals from the last 10 populations were stored (time acceleration: 36.39%) or when the number of individuals to be stored was calculated by the algorithm itself (time acceleration: 35.74%). (iv) The fGAAM was able to process all datasets used in the study, even those that, because of their high number of features, could not be processed by the two Holland GAs and NSGA II. [ABSTRACT FROM AUTHOR]

Generally, the more channels are used to acquire EEG signals, the better the performance of the brain-computer interface (BCI). However, from the user's point of view, a BCI system comprising a large number of channels is not desirable because of the lower comfort and extended application time. Therefore, the current trend in BCI design is to use the smallest number of channels possible. The problem is, however, that usually when we decrease the number of channels, the interface accuracy also drops significantly. In the paper, we examined whether it is possible to maintain the high accuracy of a BCI based on steady-state visual evoked potentials (SSVEP-BCI) in a low-channel setup using a preprocessing procedure successfully used in a multichannel setting: independent component analysis (ICA). The influence of ICA on the BCI performance was measured in an off-line (24 subjects) mode and online (eight subjects) mode. In the off-line mode, we compared the number of correctly recognized different stimulation frequencies, and in the online mode, we compared the classification accuracy. In both experiments, we noted the predominance of signals that underwent ICA preprocessing. In the off-line mode, we detected 50% more stimulation frequencies after ICA preprocessing than before (in the case of four EEG channels), and in the online mode, we noted a classification accuracy increase of 8%. The most important results, however, were the results obtained for a very low luminance (350 lx), where we noted 71% gain in the off-line mode and 11% gain in the online mode. [ABSTRACT FROM AUTHOR]

This paper reports the results of an experiment to identify EEG patterns specific to different levels of player involvement when playing a video game. To obtain unbiased results, we based our patterns on both raw EEG data and expert knowledge. We used a three-step procedure to identify patterns. First, we looked for clusters in the reduced feature space extracted from EEG data. Next, we assigned experts’ interpretations to the clusters. Finally, we analysed relations between features used to form the clusters and the class labels provided by experts. The most challenging part of the procedure was feature selection simultaneous with unsupervised classification. To accomplish this task, we developed a new approach for simultaneous feature selection and clustering based on modified GAAM (genetic algorithm with aggressive mutation). When the cGAAM algorithm was applied to EEG data, it returned the feature subsets that (a) were highly consistent across subjects and (b) provided 50% more compact clusters than clusters built over the feature subsets returned by a forward selection search strategy. The main cognitive outcome of EEG signal analysis was a set of patterns differentiating players’ involvement in a game. Conclusions included: 1. For a majority of subjects the most discriminative features were activity in the theta band in the left and right frontal areas, and activity in the delta band in the left frontal area; 2. All three features significantly differentiated between low and high, or medium and high engagement; 3. All subjects showed positive correlations between selected feature values and levels of engagement. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a new algorithm for feature selection, called a genetic algorithm with aggressive mutation. The paper presents both the theoretic background of the algorithm and its application for feature selection in the brain-computer interface (BCI) domain. To fully present the potential of the algorithm and to verify its practical usability, it is compared with other methods commonly used in BCI research. The practical application of the proposed algorithm is presented via a benchmark set submitted to the second BCI Competition (data set III-motor imagery). [ABSTRACT FROM AUTHOR]

The crucial problem that has to be solved when designing an effective brain-computer interface (BCI) is: how to reduce the huge space of features extracted from raw electroencephalography (EEG) signals. One of the strategies for feature selection that is often applied by BCI researchers is based on genetic algorithms (GAs). The two types of GAs that are most commonly used in BCI research are the classic algorithm and the Culling algorithm. This paper presents both algorithms and their application for selecting features crucial for the correct classification of EEG signals recorded during imagery movements of the left and right hand. The results returned by both algorithms are compared to those returned by an algorithm with aggressive mutation and an algorithm with melting individuals, both of which have been proposed by the author of this paper. While the aggressive mutation algorithm has been published previously, the melting individuals algorithm is presented here for the first time. [ABSTRACT FROM AUTHOR]

When reading interesting content or searching for information on a website, the appearance of a pop-up advertisement in the middle of the screen is perceived as irritating by a recipient. Interrupted cognitive processes are considered unwanted by the user but desired by advertising providers. Diverting visual attention away from the main content is intended to focus the user on the appeared disruptive content. Is the attempt to reach the user by any means justified? In this study, we examined the impact of pop-up emotional content on user reactions. For this purpose, a cognitive experiment was designed where a text-reading task was interrupted by two types of affective pictures: positive and negative ones. To measure the changes in user reactions, an eye-tracker (for analysis of eye movements and changes in gaze points) and an iMotion Platform (for analysis of face muscles' movements) were used. The results confirm the impact of the type of emotional content on users' reactions during cognitive process interruptions and indicate that the negative impact of cognitive process interruptions on the user can be reduced. The negative content evoked lower cognitive load, narrower visual attention, and lower irritation compared to positive content. These results offer insight on how to provide more efficient Internet advertising. [ABSTRACT FROM AUTHOR]

The aim of the paper is to find a method of using prediction rules in time series in such a way to maximize the profit considering the risk. To deal with this task, a regression approach to prediction was chosen. Hence, the paper refers to relation between autoregression of a chosen time series and investment strategies. The time series under consideration is the most important polish financial instrument, a future contract on WIG20. Usually, it is rather easy to prove statistically that the autoregression of a single time series cannot be considered as an effective method for forecasting WIG20 quotations for investment purpose. However, the authors find the relation between the autoregression (and also multi-regression) and real future values of WIG20 which can be the source of effective strategies. The paper presents both - the theoretical description of the proposed strategies and results of their application for monthly data of WIG20, unemployment rate and money supply in Poland (data from years 1995-2007). [ABSTRACT FROM AUTHOR]

One of the most popular approaches taken in the process of automatic creation of a rule base of a fuzzy model is a classic grid partitioning of an input space of the analyzed system. The basic feature of this approach is that a rule base created on the basis of this grid contains a lot of rules which can be eliminated from the model without a loss in a model precision. The unnecessary rules exist in the model because the grid is applied over the whole input space, regardless of the distribution of data points. Hence, in case of an unequal data distribution, some parts of the input space of the analyzed system are not covered by data points, which means that some rules are unsupported by any data point. Of course rules not supported by data points should not take part in the inference process because they can produce incorrect results in future model application. However, when these rules are eliminated from the rule base, a fuzzy model becomes an incomplete one. The theory states that an incomplete model either should not be used in practice at all or should be used only in some, very precisely defined, regions of an input space. In fact, there is one more possibility. The model completeness can be extorted by restoring some of previously discarded rules - of course after necessary changes in their conclusions. The aim of this article is to present an approach to chain systems modelling which can be used for: eliminating rules, calculating new conclusions for some of them and adding them again to the model. [ABSTRACT FROM AUTHOR]