32,109 research outputs found
Gender and Age Related Effects While Watching TV Advertisements: An EEG Study
The aim of the present paper is to show how the variation of the EEG frontal cortical asymmetry is related to the general appreciation perceived during the observation of TV advertisements, in particular considering the influence of the gender and age on it. In particular, we investigated the influence of the gender on the perception of a car advertisement (Experiment 1) and the influence of the factor age on a chewing gum commercial (Experiment 2). Experiment 1 results showed statistically significant higher approach values for the men group throughout the commercial. Results from Experiment 2 showed significant lower values by older adults for the spot, containing scenes not very enjoyed by them. In both studies, there was no statistical significant difference in the scene
relative to the product offering between the experimental populations, suggesting the absence in our study of a bias towards the specific product in the evaluated populations. These evidences state the importance of the creativity in advertising, in order to attract the target population
Neurophysiological Profile of Antismoking Campaigns
Over the past few decades, antismoking public service announcements (PSAs) have been used by governments to promote healthy
behaviours in citizens, for instance, against drinking before the drive and against smoke. Effectiveness of such PSAs has been
suggested especially for young persons. By now, PSAs efficacy is still mainly assessed through traditional methods (questionnaires
and metrics) and could be performed only after the PSAs broadcasting, leading to waste of economic resources and time in the
case of Ineffective PSAs. One possible countermeasure to such ineffective use of PSAs could be promoted by the evaluation of the
cerebral reaction to the PSA of particular segments of population (e.g., old, young, and heavy smokers). In addition, it is crucial to
gather such cerebral activity in front of PSAs that have been assessed to be effective against smoke (Effective PSAs), comparing
results to the cerebral reactions to PSAs that have been certified to be not effective (Ineffective PSAs). &e eventual differences
between the cerebral responses toward the two PSA groups will provide crucial information about the possible outcome of new
PSAs before to its broadcasting. &is study focused on adult population, by investigating the cerebral reaction to the vision of
different PSA images, which have already been shown to be Effective and Ineffective for the promotion of an antismoking
behaviour. Results showed how variables as gender and smoking habits can influence the perception of PSA images, and how
different communication styles of the antismoking campaigns could facilitate the comprehension of PSA’s message and then
enhance the related impac
EEG Resting-State Brain Topological Reorganization as a Function of Age
Resting state connectivity has been increasingly studied to investigate the effects of aging on the brain. A reduced organization
in the communication between brain areas was demonstrated b
y combining a variety of different imaging technologies (fMRI,
EEG, and MEG) and graph theory. In this paper, we propose a methodology to get new insights into resting state connectivity and
its variations with age, by combining advanced techniques of effective connectivity estimation, graph theoretical approach, and
classification by SVM method. We analyzed high density EEG signal
srecordedatrestfrom71healthysubjects(age:20–63years).
Weighted and directed connectivity was computed by means of Partial Directed Coherence based on a General Linear Kalman filter
approach. To keep the information collected by the estimator, weighted and directed graph indices were extracted from the resulting
networks. A relation between brain network properties and age of the subject was found, indicating a tendency of the network to
randomly organize increasing with age. This result is also confirmed dividing the whole population into two subgroups according
to the age (young and middle-aged adults): significant differences exist in terms of network organization measures. Classification
of the subjects by means of such indices returns an accuracy greater than 80
Neurophysiological Responses to Different Product Experiences
It is well known that the evaluation of a product from the shelf considers the simultaneous cerebral and emotional evaluation of
the different qualities of the product such as its colour, the eventual images shown, and the envelope’s texture (hereafter all
included in the term “product experience”). However, the measurement of cerebral and emotional reactions during the interaction
with food products has not been investigated in depth in specialized literature. (e aim of this paper was to investigate
such reactions by the EEG and the autonomic activities, as elicited by the cross-sensory interaction (sight and touch) across several
different products. In addition, we investigated whether (i) the brand (Major Brand or Private Label), (ii) the familiarity (Foreign
or Local Brand), and (iii) the hedonic value of products (Comfort Food or Daily Food) influenced the reaction of a group of
volunteers during their interaction with the products. Results showed statistically significantly higher tendency of cerebral
approach (as indexed by EEG frontal alpha asymmetry) in response to comfort food during the visual exploration and the visual
and tactile exploration phases. Furthermore, for the same index, a higher tendency of approach has been found toward foreign
food products in comparison with local food products during the visual and tactile exploration phase. Finally, the same
comparison performed on a different index (EEG frontal theta) showed higher mental effort during the interaction with foreign
products during the visual exploration and the visual and tactile exploration phases. Results from the present study could deepen
the knowledge on the neurophysiological response to food products characterized by different nature in terms of hedonic value
familiarity; moreover, they could have implications for food marketers and finally lead to further study on how people make food
choices through the interactions with their commercial envelope
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
A LightGBM-Based EEG Analysis Method for Driver Mental States Classification
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography-
(EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated.
However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a
challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is
based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers,
such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin
nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision
efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of
driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state
prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)
Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification
We perform an extensive study of the performance of different classification approaches on twenty-five datasets (fourteen image datasets and eleven UCI data mining datasets). The aim is to find General-Purpose (GP) heterogeneous ensembles (requiring little to no parameter tuning) that perform competitively across multiple datasets. The state-of-the-art classifiers examined in this study include the support vector machine, Gaussian process classifiers, random subspace of adaboost, random subspace of rotation boosting, and deep learning classifiers. We demonstrate that a heterogeneous ensemble based on the simple fusion by sum rule of different classifiers performs consistently well across all twenty-five datasets. The most important result of our investigation is demonstrating that some very recent approaches, including the heterogeneous ensemble we propose in this paper, are capable of outperforming an SVM classifier (implemented with LibSVM), even when both kernel selection and SVM parameters are carefully tuned for each dataset
Antismoking campaigns’ perception and gender differences: a comparison among EEG Indices
Human factors’ aim is to understand and evaluate the interactions between people and tasks, technologies, and environment. Among human factors, it is possible then to include the subjective reaction to external stimuli, due to individual’s characteristics and states of mind. These processes are also involved in the perception of antismoking public service announcements (PSAs), the main tool for governments to contrast the first cause of preventable deaths in the world: tobacco addiction. In the light of that, in the present article, it has been investigated through the comparison of different electroencephalographic (EEG) indices a typical item known to be able of influencing PSA perception, that is gender. In order to investigate the neurophysiological underpinnings of such different perception, we tested two PSAs: one with a female character and one with a male character. Furthermore, the experimental sample was divided into men and women, as well as smokers and nonsmokers. The employed EEG indices were the mental engagement (ME: the ratio between beta activity and the sum of alpha and theta activity); the approach/withdrawal (AW: the frontal alpha asymmetry in the alpha band); and the frontal theta activity and the spectral asymmetry index (SASI: the ratio between beta minus theta and beta plus theta). Results suggested that the ME and the AW presented an opposite trend, with smokers showing higher ME and lower AW than nonsmokers. The ME and the frontal theta also evidenced a statistically significant interaction between the kind of the PSA and the gender of the observers; specifically, women showed higher ME and frontal theta activity for the male character PSA. This study then supports the usefulness of the ME and frontal theta for purposes of PSAs targeting on the basis of gender issues and of the ME and the AW and for purposes of PSAs targeting on the basis of smoking habits
Evaluation of second-level inference in fMRI analysis
We investigate the impact of decisions in the second-level (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the data-analytical stability, both proxies for the reproducibility of results. Second-level analysis based on a mass univariate approach typically consists of 3 phases. First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects. We evaluate models that take into account first-level (within-subjects) variability and models that do not take into account this variability. Second, one proceeds via inference based on parametrical assumptions or via permutation-based inference. Third, we evaluate 3 commonly used procedures to address the multiple testing problem: familywise error rate correction, False Discovery Rate (FDR) correction, and a two-step procedure with minimal cluster size. Based on a simulation study and real data we find that the two-step procedure with minimal cluster size results in most stable results, followed by the familywise error rate correction. The FDR results in most variable results, for both permutation-based inference and parametrical inference. Modeling the subject-specific variability yields a better balance between false positives and false negatives when using parametric inference
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