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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]

As a result of the increasing role of online advertising and strong competition among advertisers, intrusive techniques are commonly used to attract web users' attention. Moreover, since marketing content is usually delivered to the target audience when they are performing typical online tasks, like searching for information or reading online content, its delivery interrupts the web user's current cognitive process. The question posed by many researchers in the field of online advertising is: how should we measure the influence of interruption of cognitive processes on human behavior and emotional state? Much research has been conducted in this field; however, most of this research has focused on monitoring activity in the simulated environment, or processing declarative responses given by users in prepared questionnaires. In this paper, a more direct real-time approach is taken, and the effect of the interruption on a web user is analyzed directly by studying the activity of his brain. This paper presents the results of an experiment that was conducted to find the brain activity patterns associated with interruptions of the cognitive process by showing internet advertisements during a text-reading task. Three specific aspects were addressed in the experiment: individual patterns, the consistency of these patterns across trials, and the intra-subject correlation of the individual patterns. Two main effects were observed for most subjects: a drop in activity in the frontal and prefrontal cortical areas across all frequency bands, and significant changes in the frontal/prefrontal asymmetry index. [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]