Optimizing 1D-CNN-Based Emotion Recognition Process through Channel and Feature Selection from EEG Signals.

EEG-based emotion recognition has numerous real-world applications in fields such as affective computing, human-computer interaction, and mental health monitoring. This offers the potential for developing IOT-based, emotion-aware systems and personalized interventions using real-time EEG data. This study focused on unique EEG channel selection and feature selection methods to remove unnecessary data from high-quality features.

A Bimodal Emotion Recognition Approach through the Fusion of Electroencephalography and Facial Sequences.

In recent years, human-computer interaction (HCI) systems have become increasingly popular. Some of these systems demand particular approaches for discriminating actual emotions through the use of better multimodal methods. In this work, a deep canonical correlation analysis (DCCA) based multimodal emotion recognition method is presented through the fusion of electroencephalography (EEG) and facial video clips.

Human state anxiety classification framework using EEG signals in response to exposure therapy.

Human anxiety is a grave mental health concern that needs to be addressed in the appropriate manner in order to develop a healthy society. In this study, an objective human anxiety assessment framework is developed by using physiological signals of electroencephalography (EEG) and recorded in response to exposure therapy. The EEG signals of twenty-three subjects from an existing database called "A Database for Anxious States which is based on a Psychological Stimulation (DASPS)" are used for anxiety quantification into two and four levels.

Structural, Electromagnetic and Microwave Properties of Magnetite Extracted from Mill Scale Waste via Conventional Ball Milling and Mechanical Alloying Techniques.

This study presents the utilization of mill scale waste, which has attracted much attention due to its high content of magnetite (FeO). This work focuses on the extraction of FeO from mill scale waste via magnetic separation, and ball milling was used to fabricate a microwave absorber. The extracted magnetic powder was ground-milled using two different techniques: (i) a conventional milling technique (CM) and (ii) mechanical alloying (MM) process.

Comparison of Foam Glass-Ceramics with Different Composition Derived from Ark Clamshell (ACS) and Soda Lime Silica (SLS) Glass Bottles Sintered at Various Temperatures.

Soda lime silica (SLS) waste as the source of silica (SiO) and ark clamshell (ACS) as the foaming agent has been utilized to fabricate the low-cost and lightweight foam glass-ceramics. A series of 1 and 6 wt% foam glass-ceramics were successfully prepared by the conventional solid-state sintering method at various sintering temperatures for 60 min. The bulk density of the samples has achieved minimum density (1.

Temperature-insensitive bend sensor using entirely centered Erbium doping in the fiber core.

A fiber based bend sensor using a uniquely designed Bend-Sensitive Erbium Doped Fiber (BSEDF) is proposed and demonstrated. The BSEDF has two core regions, namely an undoped outer region with a diameter of about 9.38 μm encompassing a doped, inner core region with a diameter of 4.