Analyzing the Brain Response to Marketing Stimuli Using Electroencephalogram (EEG) Signal in the Neuromarketing Application

Cognitive neuroscience is useful for understanding human behaviors related to marketing and adapting to consumer preferences. By analyzing consumers' brain responses to marketing stimuli, researchers seek to discover the reasons for decision-making. This study proposes a framework for participants' decision-making processes in terms of liking and disliking when viewing and selecting the products of an online store. To this end, the participants' brain signal (EEG) is used when displaying different products. Estimation of power spectrum density by Welch method, detrended fluctuation analysis (DFA), and recurrence quantification analysis (RQA) were used to extract the feature vector. The results show that the two categories of liking or disliking a product can be classified with 73.5% accuracy using a support vector machine (SVM), which compared to the previous study, there is a 3.5% improvement in results. By better understanding consumer behavior and mastery of consumer demands, market strategies can be determined in a way that in addition to customer satisfaction, increase sales and profits. The results are promising and the proposed method can be used for a better electronic commerce model

EEG-Based Effective Connectivity Analysis for Attention Deficit Hyperactivity Disorder Detection Using Color-Coded Granger-Causality Images and Custom Convolutional Neural Network

Background: Attention deficit hyperactivity disorder (ADHD) is prevalent worldwide, affecting approximately 8-12% of children. Early detection and effective treatment of ADHD are crucial for improving academic, social, and emotional outcomes. Despite numerous studies on ADHD detection, existing models still lack accuracy distinguishing between ADHD and healthy control (HC) children.Methods: This study introduces an innovative methodology that utilizes granger causality (GC), a well-established brain connectivity analysis technique, to reduce the required EEG electrodes. We computed GC indexes (GCI) for the entire brain and specific brain regions, known as regional GCI, across different frequency bands. Subsequently, these GCIs were transformed into color-coded images and fed into a custom-developed 11-layer convolutional neural network.Results: The proposed model is evaluated through a five-fold cross-validation, achieving the highest accuracy of 99.80% in the gamma frequency band for the entire brain and an accuracy of 98.50% in distinguishing the theta frequency band of the right hemisphere of ADHD and HC children by only using eight electrodes.Conclusion: The proposed framework provides a powerful automated tool for accurately classifying ADHD and HC children. The study’s outcome demonstrates that the innovative proposed methodology utilizing GCI and a custom-developed convolutional neural network can significantly improve ADHD detection accuracy, improving affected children’s overall quality of life

Emotion and Attention Recognition Based on Biological Signals and Images

Emotion, stress, and attention recognition are the most important aspects in neuropsychology, cognitive science, neuroscience, and engineering. Biological signals and images processing such as galvanic skin response (GSR), electrocardiography (ECG), heart rate variability (HRV), electromyography (EMG), electroencephalography (EEG), event-related potentials (ERP), eye tracking, functional near-infrared spectroscopy (fNIRS), and functional magnetic resonance imaging (fMRI) have a great help in understanding the mentioned cognitive processes. Emotion, stress, and attention recognition systems based on different soft computing approaches have many engineering and medical applications. The book Emotion and Attention Recognition Based on Biological Signals and Images attempts to introduce the different soft computing approaches and technologies for recognition of emotion, stress, and attention, from a historical development, focusing particularly on the recent development of the field and its specialization within neuropsychology, cognitive science, neuroscience, and engineering. The basic idea is to present a common framework for the neuroscientists from diverse backgrounds in the cognitive neuroscience to illustrate their theoretical and applied research findings in emotion, stress, and attention

Cognitive and Computational Neuroscience - Principles, Algorithms and Applications

The book ""Cognitive and Computational Neuroscience - Principles, Algorithms and Applications"" will answer the following question and statements: System-level neural modeling: what and why? We know a lot about the brain! Need to integrate data: molecular/cellular/system levels. Complexity: need to abstract away higher-order principles. Models are tools to develop explicit theories, constrained by multiple levels (neural and behavioral). Key: models (should) make novel testable predictions on both neural and behavioral levels. Models are useful tools for guiding experiments. The hope is that the information provided in this book will trigger new researches that will help to connect basic neuroscience to clinical medicine

Improving of Multivariable PI Controller with a High Gain Structure for an Irregular System by Genetic Algorithm

This paper describes an optimal design for multivariable PI controller with a high gain structure for an irregular system by genetic algorithm. PI controllers with a high gain structure leads to the asymptotic decomposition of the fast and slow modes in the closed loop system that have unique characteristics. The slow modes are asymptotically uncontrollable and unobservable; therefore, they have not role in input and output behavior. The closed-loop response is affected only from rapid poles; therefore, the system response will have quick behavior. An essential requirement of this design is that the first Markov parameter of multivariable system (the matrix product CB) must have full rank. If the CB matrix is not full rank, the measurement matrix (M) is used with internal feedback. In this structure, the measurement matrix is chosen using genetic algorithm in order to reach the stable closed-loop system and minimize interference between outputs. The research is implemented on the two kind of different systems. The results show that the response time of PI controller with a high gain structure by genetic algorithms has good behavior in comparison with other methods