Search results

This paper aims to investigate whether electroencephalography (EEG) technology is effective in qualifying the tactile sensation evoked by non-steady cutaneous electrical stimulation. EEG is a novel method for electrotactile analysis and has demonstrated the discrimination ability for electrotactile sensation under steady contact conditions in recent years. However, in non-steady contact conditions, it is necessary to test its effectiveness. This study aims to explore an objective analysis method in comparison to psychophysical approach and to provide a methodology for non-steady electrotactile research.

With EEG experimentation on 13 volunteers, the authors collected evoked potentials by the predesigned “1” and “0” stimulation events. In addition, with a series of data preprocessing including artifact elimination, band-pass filtering, baseline normalization, data superposition and fast Fourier transform transformation, the authors got the power spectrum of alpha, beta and gamma rhythms. Furthermore, statistics analysis and ANOVA test were adopted for exploring the discrepancy of the spectrum characterizations for different non-steady electrostimulation events.

The EEG power spectrum of the central cortical brain is valuable in discriminating the two types of stimulation events. The power of alpha rhythm especially in the central cortical brain evoked by event “1,” whose current level is equal to the threshold, was significantly lower than that evoked by event “0,” whose level is less than the threshold (p < 0.05). Then, the power of the beta rhythm presented counter-change (p < 0.05). This study suggests that EEG may have the potential to qualify non-steady electrotactile sensation for engineering applications.

Limiting factors of non-steady electrotactile stimulation were considered in this study. Different tapping frequency and contact time should be investigated in future studies.

This paper fulfills a challenge in qualifying the tactile sensations evoked by non-steady electrical stimulation with EEG characteristics.

This chapter introduces electroencephalography (EEG), a measure of neurophysiological activity, as a critical method for investigating individual and team decision-making and cognition. EEG is a useful tool for expanding the theoretical and research horizons in organizational cognitive neuroscience, with a lower financial cost and higher portability than other neuroimaging methods (e.g., functional magnetic resonance imaging). This chapter briefly reviews past work that has applied cognitive neuroscience methods to investigate cognitive processes and outcomes. The focus is on describing contemporary EEG measures that reflect individual cognition and compare them to complementary measures in the field of psychology and management. The authors discuss how neurobiological measures of cognition relate to and may predict both individual cognitive performance and team cognitive performance (decision-making). This chapter aims to assist scholars in the field of managerial and organizational cognition in understanding the complementarity between psychological and neurophysiological methods, and how they may be combined to develop new hypotheses in the intersection of these research fields.

The aim of this paper is to explore the brain–computer interface (BCI) as a methodology for generating awareness and increasing reliable use cases of the same so that an individual's quality of life can be enhanced via neuroscience and neural networks, and risk evaluation of certain experiments of BCI can be conducted in a proactive manner.

This paper puts forward an efficient approach for an existing BCI device, which can enhance the performance of an electroencephalography (EEG) signal classifier in a composite multiclass problem and investigates the effects of sampling rate on feature extraction and multiple channels on the accuracy of a complex multiclass EEG signal. A one-dimensional convolutional neural network architecture is used to further classify and improve the quality of the EEG signals, and other algorithms are applied to test their variability. The paper further also dwells upon the combination of internet of things multimedia technology to be integrated with a customized design BCI network based on a conventionally used system known as the message query telemetry transport.

At the end of our implementation stage, 98% accuracy was achieved in a binary classification problem of classifying digit and non-digit stimuli, and 36% accuracy was observed in the classification of signals resulting from stimuli of digits 0 to 9.

BCI, also known as the neural-control interface, is a device that helps a user reliably interact with a computer using only his/her brain activity, which is measured usually via EEG. An EEG machine is a quality device used for observing the neural activity and electric signals generated in certain parts of the human brain, which in turn can help us in studying the different core components of the human brain and how it functions to improve the quality of human life in general.

The purpose of this paper is to apply recurrent neural networks (RNNs) and more specifically long-short term memory (LSTM)-based ones for mental task classification in terms of BCI systems. The authors have introduced novel LSTM-based multichannel architecture model which proved to be highly promising in other fields, yet was not used for mental tasks classification.

Validity of the multichannel LSTM-based solution was confronted with the results achieved by a non-multichannel state-of-the-art solutions on a well-recognized data set.

The results demonstrated evident advantage of the introduced method. The best of the provided variants outperformed most of the RNNs approaches and was comparable with the best state-of-the-art methods.

The approach presented in the manuscript enables more detailed investigation of the electroencephalography analysis methods, invaluable for BCI mental tasks classification.

The new approach to mental task classification, exploiting LSTM-based RNNs with multichannel architecture, operating on spatial features retrieving filters, has been adapted to mental tasks with noticeable results. To the best of the authors’ knowledge, such an approach was not present in the literature before.

An electroencephalography (EEG) examination may cause psychological stress in children with autism that can interfere with the examination results. The objective information on the presence or absence of psycho-emotional stress in patients can help interpret electroencephalograms. This paper aimed to demonstrate the potential of noninvasive objective diagnostics of emotional stress in autistic children undergoing an EEG examination based on analysis of saliva.

This study involved 19 children with autism spectrum disorder (ASD) (ICD-10 F84.0); the mean age was seven years. During EEG examination of the children, behavioral parameters were assessed. The activity of cytochrome P450 reductase (CYPOR) in saliva was measured before and after the EEG procedure using lucigenin-enhanced nicotinamide adenine dinucleotide phosphate-stimulated chemiluminescence assay.

Significant differences in CYPOR activity were found between the children who were distressed during an EEG examination and the children without behavioral disturbances (Mann–Whitney test, p = 0.002). Thus, the EEG examination resulted in an increase in CYPOR activity in saliva cells, which may prove the stressful effect of this procedure on autistic children.

The chemiluminescent indices reflecting the activity of microsomal CYPOR in cells presenting in saliva correlate with the absence or presence of psychological stress in children; this phenomenon can be explained by an increased metabolism of the stress hormone, cortisol, by the cytochrome P450 microsomal system. Furthermore, the proposed method is completely safe, noninvasive, rapid (recording time is 20 min), inexpensive and promising for an objective assessment of psycho-emotional stress in autistic children undergoing medical examinations.

This chapter explains and discusses the role and potential of psychophysiological tools of research in tourism and hospitality. As tourism and hospitality services are in general inseparable, i.e. the delivery and the consumption of the service mostly take place at the same time, they tend to involve service encounters which intense and frequent contact and social interactions between the customers and the service providers. These intense and frequent contact and social interactions during service encounters may determine the satisfaction and dissatisfaction of the customers. Hence, the measurement of actual emotions to understand the reactions of customers to various aspects of the service is of paramount importance. Psychophysiological tools, often referred to as neuromarketing tools, allow the collection of realistic data regarding the emotions of the customers. Based on the above background, this chapter explains and discusses the use of tools such as the EEG, Eye Tracker, Galvanic Skin Response, and Facial Expression Recognition in understanding tourism and hospitality customers' reactions and emotions to various aspects of the service.

The subject of this paper is the idea of Brain–Computer Interface (BCI). The main goal is to assess the potential impact of BCI on the design, use and evaluation of information retrieval systems operating in libraries.

The method of literature review was used to establish the state of research. The search according to accepted queries was carried out in the Scopus database and complementary in Google Scholar. To determine the state of research on BCI on the basis of library and information science, a specialist LISTA abstract database was also searched. The most current papers published in the years 2015–2019 in the English language or having at least an abstract in this language were taken into account.

The analysis showed that BCI issues are extremely popular in subject literature from various fields, mainly computer science, but practically does not occur in the context of using this technology in information retrieval systems.

Due to the fact that BCI solutions are not yet implemented in libraries and are rarely the subject of scientific considerations in the field of library and information science, this article is mainly based on literature from other disciplines. The goal was to consider how much BCI solutions can affect library information retrieval systems. The considerations presented in this article are theoretical in nature due to the lack of empirical materials on which to base. The author's assumption was to initiate a discussion about BCI on the basis of library and information science, not to propose final solutions.

The results can be widely used in practice as a framework for the implementation of BCI in libraries.

The article can help to facilitate the debate on the role of implementing new technologies in libraries.

The problem of BCI is very rarely addressed in the subject literature in the field of library and information science.

Creating memorable tourism experiences (MTE) is vital to obtain sustained tourism visits. In the digital era, infusions of various digital technologies in tourism services without admitting tourist emotions could jeopardize the experience. Drawing from a Service-Dominant Logic (S-DL) perspective, this study explains the complexity of digital tourism experience in the service system view, highlighting the importance of emotions as resources. It is composed of actors' orchestrations, connected by shared emotions, and enabled by sensory stimuli facilitated by the digital tourism ecosystem throughout the tourism journey. This study proposes a Memorable Digital Tourism Experience (MDTE) framework by identifying the focal actors, recognizing the emotions, and determining the moderating role of sensory stimuli enabled by various novel technologies. At last, several agenda and practical guidelines are proposed on how to operationalize the framework and different methodologies to explore Memorable Digital Tourism Experience.

Decision-making in human resources management is done at both the micro and macro level of organizations. Unfortunately, the decisions at each level are often executed without consideration of the other, and current theory reflects this issue. In response to a call for integration of micro- and macro-level processes by Huselid and Becker (2011), we review the extant literature on strategic human resources and high-performance work systems to provide recommendations for both research and practice. We aimed to contribute to the literature by proposing the incorporation of the situation awareness literature into the high-performance work systems framework to encourage the alignment of human resources efforts. In addition, we provide practical recommendations for integrating situation awareness and strategic decision-making. We discuss a process for the employment of situation awareness in organizations that might not only streamline human resources management but also result in more effective decisions. Additional considerations include implications for teams, boundary conditions (e.g., individual differences), and measurement.

The purpose of this paper is to provide insight into the temporal development of the customers' psychological state, described by a multitude of constructs, throughout the whole decision‐making process in a service context in order to derive ideas for customer‐focused benchmarking.

A combination of qualitative and quantitative data contributes to a better understanding of psychological phenomena during purchase decision making. The present longitudinal study investigates the consumers' psychological state during the decision‐making process considering services in tourism industry applying the diary method. Qualitative in‐depth investigation of individual case studies provides further information for interpreting the results of quantitative analyses which aim at measuring constructs such as perceived risk, cognitive dissonance (CD), satisfaction and arousal on a higher level of abstraction.

Findings show that qualitative and quantitative data not only complement one another but also their interaction provides additional information about the individual consumer decision‐making process. As the longitudinal quantitative analysis shows that CD does not completely dissolve over time and significantly influences the level of satisfaction, complaint behaviour and word‐of‐mouth, additional qualitative data help interpret these findings and derive more effective marketing implications.

Implications for the marketing‐mix of service companies in general and travel agencies in particular are derived. The paper underlines the potential of innovational methodological approaches in marketing research.

The paper introduces a longitudinal mixed‐method approach to collect customer‐focused information relevant for benchmarking purposes of service companies. It contributes to scientific knowledge of decision making in general and CD in particular.