Tapping Into Emotions: Understanding the Neural Mechanisms of Emotional Responses Through EEG using Music
Speech feature extraction and Emotion Recognition using Deep Learning Techniques
Smart Wearable Phonocardiogram for Real Time Heart Sound Analysis and Predictive Cardiac Healthcare
DROWSINESS DETECTION AND IT'S ANALYSIS OF BRAIN WAVES USING ELECTROENCEPHALOGRAM
A Review of Non-Invasive Breath-Based Glucose Monitoring System For Diabetic Patients
Blockchain 3.0: Towards a Secure Ballotcoin Democracy through a Digitized Public Ledger in Developing Countries
Fetal ECG Extraction from Maternal ECG using MATLAB
Brief Introduction to Modular Multilevel Converters and Relative Concepts and Functionalities
Detection of Phase to Phase Faults and Identification of Faulty Phases in Series Capacitor Compensated Six Phase Transmission Line using the Norm of Wavelet Transform
A Novel Approach to Reduce Deafness in Classical Earphones: MUEAR
A novel mathematical ECG signal analysis approach for features extraction using LabVIEW
Filtering of ECG Signal Using Adaptive and Non Adaptive Filters
Application of Polynomial Approximation Techniques for Smoothing ECG Signals
A Novel Approach to Improve the Wind Profiler Doppler Spectra Using Wavelets
A Review of Non-Invasive Breath-Based Glucose Monitoring System For Diabetic Patients
Emotion detection by evaluating Electroencephalogram (EEG) signals is an emerging field of study which provides insights about human emotional states by monitoring brain activity, using music therapy and entertainment. Our study aims to bridge a connection between human brain activities and the recognition of emotion using music. The applications in this study involves mental health assessments, emotionally intelligent agents, adaptive learning, pain assessment, patient monitoring, security and surveillance, as well as personalized music recommendations. Traditional EEG-based emotion detection techniques often struggle with the complex and noisy nature of data. Since the received EEG signals are raw bulk data, in our study, we propose the use of actor critic algorithm which allows accurate and real time emotion detection in the presence of musical stimuli. The actor-critic network architecture is a sophisticated framework designed to predict emotional states from EEG features, leveraging the rich, real-time data that it provides about brain activity. In this setup, the actor network is responsible for generating predictions about an individual’s emotional state based on the EEG signals it processes. It employs these signals to make informed guesses about various emotional conditions, such as happiness, sadness, or stress. On the other hand, the critic network plays a crucial role in evaluating the accuracy of these predictions. It assesses how well the actor’s predictions align with actual emotional states, providing a feedback mechanism that is essential for refining and enhancing the actor’s predictive capabilities.
Speech Emotion Recognition (SER) is crucial for human-computer interaction, enabling systems to better understand emotions. Traditional feature extraction methods like Gamma Tone Cepstral Coefficients (GTCC) have been used in SER for their ability to capture auditory features aligned with human hearing, but they often fall short in capturing emotional nuances. Mel Frequency Cepstral Coefficients (MFCC) have gained prominence for better representing speech signals in emotion recognition. This work introduces an approach combining traditional and modern techniques, comparing GTCC-based extraction with MFCC and utilizing the Ensemble Subspace k-Nearest Neighbors (ES-kNN) classifier to improve accuracy. Additionally, deep learning models like Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) are explored for their ability to capture temporal dependencies in speech. Datasets such as CREMA-D and SAVEE are used.
The design presents a compact and innovative wearable device designed for continuous monitoring of phonocardiogram (PCG) and electrocardiogram (ECG) waveforms. This device is intended for everyday use, allowing users to track their heart health conveniently and effectively. Key features include:Continuous Monitoring,Wireless Data Transmission,Noise Reduction Technology,User -Friendly Interface,Data Analysis and Archiving, Integration with Other Health Devices,Research and Development Opportunities.
Innovative possibilities in healthcare and personal well-being monitoring are made possible by the ability to identify this equipment integrates EEG sensors, Arduino microcontrollers, and Python scripts that do real- time detection of drowsiness. It captures signals of brain waves, processes them, and analyzes typical patterns, which indicate alertness has reduced. There are various possibilities in the health and well-being monitoring through this device. The primary components consist of the EEG sensors, Arduino microcontrollers, and CP2102 for data transfer, with Python scripts utilized in further analyses. The EEG sensors record wave signals from the brain, which are transferred to Arduino for processing. Arduino then transmits such data to a computer through CP2102. Python scripts will later analyze such EEG signals to detect those general patterns involving suppression of alpha waves or increase in theta waves as a sign of drowsiness. It would have many applications on medical and personal dimensions. On the medical aspect, it can monitor patients who are just recovering from anesthesia, assess the quality of sleep for those suffering from sleeping disorder, or detect neurological disorders such as narcolepsy. On personal level, it could track drowsiness in drivers, pilots, or even operators of heavy machinery, optimize stages of sleep for improved rest quality, and improve cognitive capabilities. The real advantages of this real-time drowsiness detecting system are that it offers immediate intervention; it is noninvasive and inexpensive, portable, easy to integrate with existing systems. Besides, it offers high accuracy in detecting the brain wave patterns associated with drowsiness-related issues and has prevented many accidents and improved patient care, which generally contributes to the overall quality of life. Future Developments and Opportunities Potential future applications of this technology are integration with wearable devices, innovative implementations of machine learning algorithms to improve pattern recognition, and multi-modal sensing. The broader potential for a real-time drowsiness detection system offers the possibility of transforming healthcare and personal wellness monitoring to enable even safer and healthier lives.
Diabetes management is crucial for millions worldwide and traditional blood glucose monitoring methods often require invasive blood sampling, leading to patient discomfort and poor adherence. This project proposes the development of a non-invasive breath-based glucose monitoring system that leverages gas sensors to detect specific volatile organic compounds (VOCs) in exhaled breath, particularly acetone, which correlates with blood glucose levels. The system will utilize metal oxide semiconductor (MOS) sensors and machine learning algorithms for accurate real-time glucose readings. By eliminating the need for finger pricks, this innovative device aims to enhance the convenience and compliance of glucose monitoring for diabetic patients, ultimately contributing to better disease management and quality of life. The feasibility, accuracy, and usability of the system will be validated through clinical trials, paving the way for future advancements in diabetes care technologies.
