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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.

This paper aims to propose a new upper limb movement classification with two phases like pre-processing and classification. Investigation of human limb movements is a significant topic in biomedical engineering, particularly for treating patients. Usually, the limb movement is examined by analyzing the signals that occurred by the movements. However, only few attempts were made to explore the correlations among the movements that are recognized by the human brain.

The initial process is the pre-processing that is performed for detecting and removing noisy channels. The artifacts are marked by band-pass filtering that discovers the values below and above thresholds of 200 and –200 µV, correspondingly. It also discovers the trials with unusual joint probabilities, and the trials with unusual kurtosis are also determined using this method. After this, the pre-processed signals are subjected to a classification process, where the neural network (NN) model is used. The model finally classifies six movements like “elbow extension, elbow flexion, forearm pronation, forearm supination, hand open, and hand close,” respectively. To make the classification more accurate, this paper intends to optimize the weights of NN by a new hybrid algorithm known as bypass integrated jaya algorithm (BI-JA) that hybrids the concept of rider optimization algorithm (ROA) and JA. Finally, the performance of the proposed model is proved over other conventional models concerning certain measures like accuracy, sensitivity, specificity, and precision, false positive rate, false negative rate, false discovery rate, F₁-score and Matthews correlation coefficient.

From the analysis, the adopted BI-JA-NN model in terms of accuracy was high at 80th population size was 7.85%, 3.66%, 7.53%, 2.09% and 0.52% better than Levenberg–Marquardt (LM)-NN, firefly (FF)-NN, JA-NN, whale optimization algorithm (WOA)-NN and ROA-NN algorithms. On considering sensitivity, the proposed method was 2%, 0.2%, 5.01%, 0.29% and 0.3% better than LM-NN, FF-NN, JA-NN, WOA-NN and ROA-NN algorithms at 50th population size. Also, the specificity of the implemented BI-JA-NN model at 80th population size was 7.47%, 4%, 7.05%, 2.1% and 0.5% better than LM-NN, FF-NN, JA-NN, WOA-NN and ROA-NN algorithms. Thus, the betterment of the presented scheme was proved.

This paper adopts the latest optimization algorithm called BI-JA to introduce a new upper limb movement classification with two phases like pre-processing and classification. This is the first work that uses BI-JA based optimization for improving the upper limb movement detection using electroencephalography signals.

This study investigates how media brand knowledge, defined as a structural feature of the message, influences emotional and attentional responses to, and memory of, news messages.

Self-reports, facial electromyography (EMG) and electroencephalography were used as indices of emotional valence, arousal and attention in response to 42 news messages, which varied along the valence and involvement dimensions and were framed with different media brands varying along the familiarity and credibility dimensions.

Compared to the no-brand condition, news framed with brands elicited more attention. The memory tests indicated that strong media brands override the effect of involvement in information encoding, whereas details of news presented with Facebook were not well encoded. However, the headlines of news framed with Facebook were well retrieved. In addition, negative and high-involvement news elicited higher arousal ratings and corrugator EMG activity. News framed with familiar and high-credibility brands elicited higher arousal ratings.

Relevant for both brand managers and audiences, the findings show that building credibility and familiarity both work as brand attributes to differentiate media brands and influence information processing.

The results highlight the importance of media brands in news reading: as a structural feature, the brand is used as a proxy to process the message content. The study contributes by investigating how the type of source influences the reception and encoding of the mediated information; by investigating the emotional effects of brands; and by confirming previous findings in media psychology literature.

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.

Neuromarketing is a science that can examine human subconscious decisions and activities. The study aimed to investigate the neuropsychological responses of consumers to promotion strategies and the decision to buy sports products, in order to determine the more effective strategy.

An experimental design clarifies whether there is a significant difference between the neuropsychological responses resulting from selective promotional strategies (charity, endorsement, advertising and discount). The authors conducted the research with 40 young adults (20 males and 20 females; age range 25–35 years). The Enobio 20-channel electroencephalograph (EEG) is used to record waves, a two-item questionnaire is used to measure purchasing decisions and self-assessment forms are used to measure arousal and pleasure. Data analysis performed by Friedman's statistical methods and logistic regression using SPSS 22 software.

Based on the alpha wave, the results showed the two charity and endorsement strategies had the highest effect on consumer attentions. The pleasure had the highest value in the discount, and the arousal had the highest value in the charity strategy. Neuropsychological responses also explain a significant percentage of the consumer decision to buy.

Access to research samples is difficult due to the ignorance of the samples as well as the fear of possible harm from imaging and brain scanning methods.

Neuromarketing is a science that can examine human subconscious decisions and activities.

The information obtained neural methods are more accurate than traditional research methods.

This study showed the alpha brainwave (attention), arousal and pleasure explain a significant part of the consumer decision to buy. More scientific data can be obtained through new scientific approaches such as neuromarketing, which has a great impact on understanding consumer behavior. Therefore, marketers and researchers can make their promotional activities more effective in terms of them.

It is generally accepted that massage can provide a lot of benefits to human health, especially for the brain functions. Little is known about the effect of unilateral massage on the brain activities. Nowadays, Swedish massage is a modern massage technique that is popular in both treatment and research fields. The purpose of this paper is to investigate the effect of unilateral Swedish massage on brain activities with electroencephalography (EEG) recording.

In total, 18 healthy adult participants (5 men, 13 women) aged between 22 and 36 years were massaged over one side of arm, forearm, hand, neck and face. Then the same procedures were repeated to another side of the body. EEG was recorded before (baseline) and during each massage condition. The absolute power of four common brain waves consisting of δ (0.5-4 Hz), θ (4-8 Hz), α (8-13 Hz), and β activities (13-30 Hz) from the quantitative EEG analysis between baseline and each massage condition were used to compare with the paired t-test.

The study found the reduction of δ and θ powers over bilateral frontal, fronto-central, and central areas. The increments of α power over the similar brain areas were also observed. These findings indicated the generalized effect of unilateral Swedish massage for inducing relaxation. Moreover, the significant reduction of β power was also found over right central area when left-arm massage was applied. This finding revealed the initial inhibitory effect of Swedish massage over right somatosensory cortex that received sensory stimulation through massage from left side of the body.

Unilateral Swedish massage induced the inhibitory effect at the contralateral somatosensory cortex and then produced the generalized effect which is compatible with relaxation.

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.

This chapter provides an overview of the Department of Defense (DoD) laboratory structure to help equipment designers, modelers, and manufacturers determine where research, testing programs, or relevant findings can be found. The chapter includes a discussion of the performance measures and metrics typically used in DoD laboratories and concludes by considering the current state-of-the-art as well as the state-of-the-possible for human performance measurement.