A comprehensive exploration of machine learning techniques for EEG-based anxiety detection.

The performance of electroencephalogram (EEG)-based systems depends on the proper choice of feature extraction and machine learning algorithms. This study highlights the significance of selecting appropriate feature extraction and machine learning algorithms for EEG-based anxiety detection. We explored different annotation/labeling, feature extraction, and classification algorithms.

Design and Development of an Imitation Detection System for Human Action Recognition Using Deep Learning.

Human action recognition (HAR) is a rapidly growing field with numerous applications in various domains. HAR involves the development of algorithms and techniques to automatically identify and classify human actions from video data. Accurate recognition of human actions has significant implications in fields such as surveillance and sports analysis and in the health care domain.

Computational Intelligence-Based Stuttering Detection: A Systematic Review.

Stuttering is a widespread speech disorder affecting people globally, and it impacts effective communication and quality of life. Recent advancements in artificial intelligence (AI) and computational intelligence have introduced new possibilities for augmenting stuttering detection and treatment procedures. In this systematic review, the latest AI advancements and computational intelligence techniques in the context of stuttering are explored.

Measuring Engagement in Robot-Assisted Therapy for Autistic Children.

Children with autism face a range of challenges when it comes to verbal and nonverbal communication. It is essential that children participate in a variety of social, educational, and therapeutic activities to acquire knowledge that is essential for cognitive and social development. Recent studies have shown that children with autism may be interested in playing with an interactive robot.

Assessing the Potential of Robotics Technology for Enhancing Educational for Children with Autism Spectrum Disorder.

Robotics technology has been increasingly used as an educational and intervention tool for children with autism spectrum disorder (ASD). However, there remain research issues and challenges that need to be addressed to fully realize the potential benefits of robot-assisted therapy. This systematic review categorizes and summarizes the literature related to robot educational/training interventions and provides a conceptual framework for collecting and classifying these articles.

Systematic Review and Future Direction of Neuro-Tourism Research.

Neuro-tourism is the application of neuroscience in tourism to improve marketing methods of the tourism industry by analyzing the brain activities of tourists. Neuro-tourism provides accurate real-time data on tourists' conscious and unconscious emotions. Neuro-tourism uses the methods of neuromarketing such as brain-computer interface (BCI), eye-tracking, galvanic skin response, etc.

Feature selection of EEG signals in neuromarketing.

Brain-computer interface (BCI) technology uses electrophysiological (EEG) signals to detect user intent. Research on BCI has seen rapid advancement, with researchers proposing and implementing several signal processing and machine learning approaches for use in different contexts. BCI technology is also used in neuromarketing to study the brain's responses to marketing stimuli.

A Systematic Review of Research on Robot-Assisted Therapy for Children with Autism.

Recent studies have shown that children with autism may be interested in playing with an interactive robot. Moreover, the robot can engage these children in ways that demonstrate essential aspects of human interaction, guiding them in therapeutic sessions to practice more complex forms of interaction found in social human-to-human interactions. We review published articles on robot-assisted autism therapy (RAAT) to understand the trends in research on this type of therapy for children with autism and to provide practitioners and researchers with insights and possible future directions in the field.

Recognition of Consumer Preference by Analysis and Classification EEG Signals.

Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography (EEG)-based brain-computer interface (BCI) research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms.

Lightweight Building of an Electroencephalogram-Based Emotion Detection System.

Brain-computer interface (BCI) technology provides a direct interface between the brain and an external device. BCIs have facilitated the monitoring of conscious brain electrical activity via electroencephalogram (EEG) signals and the detection of human emotion. Recently, great progress has been made in the development of novel paradigms for EEG-based emotion detection.

Measuring and monitoring emotional changes in children who stutter.

The assessment of clients with speech disorders presents challenges for speech-language pathologists. For example, having a reliable way of measuring the severity of the case, determining which remedial program is aligned with a patient's needs, and measuring of treatment processes. There is potential for brain-computer interface (BCI) applications to enhance speech therapy sessions by providing objective insights and real-time visualization of brain activity during the sessions.

The Effect of the Arabic Computer Rehabilitation Program "Rannan" on Sound Detection and Discrimination in Children with Cochlear Implants.

Purpose: The aim of this work is to examine the efficacy of using computer-based training program (Rannan) as an intervention approach to enhance sound detection and discrimination in Arabic-speaking children with cochlear implants (CIs).

Research Design: A prospective study comparing performance between two groups of children. Participants were divided into two equal groups that were matched in age and programming strategies.