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9 articles for “ElectroEncephaloGram(EEG)”
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Analysis of Signal Processing Algorithms for Bio-Medical Applications
Abstract: Epilepsy is a burdensome brain disorder, in which heavy seizures will happen again and again. Sudden explosion of electrical activity during the seizures will create a blackout for patients and it will break the body equilibrium, so patients may fall down. Epilepsy affects a large population around the globe. It may begin at any age and last for generations. It can be controlled by the surgical removal of the brain …
Published in Current Trends in Signal Processing · Vol. 12, Issue 3, 2022 · pp. 24–27 Read article
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Advancing EEG Technology for Affordable and Effective Epilepsy Detection
Abstract: For a proper diagnosis and prompt treatment, epilepsy, a neurological condition marked by recurring seizures, needs to be continuously monitored. Manual interpretation is frequently used in traditional approaches for identifying epileptic seizures from electroencephalogram (EEG) signals, which can be laborious and error-prone. In this research, a novel method for automatically detecting epilepsy from EEG data using deep learning algorithms is presented. According to centers for disease control and prevention (CDC) …
Published in Trends in Opto-electro & Optical Communication · Vol. 14, Issue 3, 2024 · pp. 11–18 Read article
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Alzheimer disorders diagnosis system design using machine learning for EEG signal
Abstract: The diagnosis of Alzheimer's disorders (AD), a prevalent neurological disorder, can created by utilising a range of therapeutic methods, including the electroencephalogram (EEG), which has been especially successful in the past. The objective for this study is to develop a computer-aided diagnosis tool which may recognize AD from EEG data. The EEG information was cleaned up with a band-pass elliptic digital filter to remove any interference or disruptions. The filtered …
Published in Journal of Control & Instrumentation Read article
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Alzheimer disorders diagnosis system design using machine learning for EEG signal
Abstract: The diagnosis of Alzheimer's disorders (AD), a prevalent neurological disorder, can created by utilising a range of therapeutic methods, including the electroencephalogram (EEG), which has been especially successful in the past. The objective for this study is to develop a computer-aided diagnosis tool which may recognize AD from EEG data. The EEG information was cleaned up with a band-pass elliptic digital filter to remove any interference or disruptions. The filtered …
Published in Journal of Control & Instrumentation · Vol. 14, Issue 1, 2023 · pp. 9–22 Read article
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Smart Patient Monitoring and Motion Tracking System
Abstract: The integration of smart technologies in healthcare has revolutionized patient monitoring and diagnostics. This paper presents a Smart Patient Monitoring and Motion Tracking System designed for hospitals, leveraging EEG (Electroencephalogram) signals to track patient movements and monitor neurological health. The proposed system combines motion tracking with real time EEG signal analysis to enhance patient safety, especially for individuals prone to seizures, neurological disorders, or other mobility-related risks. The system employs …
Published in International Journal of Radio Frequency Innovations · Vol. 3, Issue 2, 2025 · pp. 9–23 Read article
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Global Burden of Major Depressive Disorder: Prevalence, Diagnosis, and Impact: A Comprehensive review
Abstract: In this review, we will discuss about the major depressive disorders on the basis of neurobiological changes. MDD, a prevalent psychiatric condition, manifests as a complex interplay of genetic, environmental, and physiological factors with a substantial impact on individuals and societies globally. Here the clinical assessment is fully based on diagnosis and the statistical manual for mental disorder, 5th edition (DSM-5). The pathophysiology involves Diagnosis alterations in neurotransmitter systems, dysregulation …
Published in Emerging Trends in Metabolites · Vol. 1, Issue 1, 2024 · pp. 54–62 Read article
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EMOTION RECOGNITION FROM ELECTROENCEPHALOGRAM SIGNAL AND EYE MOVEMENT BASED ON DEEP LEARNING
Abstract: Emotion recognition from electroencephalogram (EEG) signals has gained significant attention due to its potential in human- computer interaction (HCI), mental health monitoring, and personalized content delivery. This paper presents the use of Convolutional neural networks (CNNs) to classify emotions such as happiness, sadness, fear, neutral, and disgust by leveraging a fusion of EEG signals and eye movements data. Compared to conventional methods of emotion detection, such as those that rely …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 2, 2025 · pp. 8–15 Read article
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Steady State Visual Evoked Potential Based Brain Computer Interface System
Abstract: The steady-state visual evoked potential (SSVEP) is a brain response that can be measured by an electroencephalogram (EEG) when a subject looks at periodic luminance- or contrast-modulated stimuli. In this study, flickering Light Emitting Diode (LED) is used as visual stimulation. Mostly, brain response is recorded using an electroencephalograph (EEG) and recorded in the brain's occipital lobe which is associated with human vision. The study encompasses key stages, beginning with …
Published in Current Trends in Signal Processing · Vol. 13, Issue 3, 2023 · pp. 35–43 Read article
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Performance Analysis and Detection of Stress using IoT Sensors
Abstract: The objective of this paper is to review recent researches on several stress prediction and detection techniques and also to provide better streamline on various stress management techniques. Worldwide, people experience mental stress concerns with different reasons including family issues, financial issues, social issues and environmental issues. The adjustable stress can develop a person; however, the strong response or continuous stress becomes critical. Hence, prediction, detection and management of physical …
Published in Journal of Control & Instrumentation · Vol. 13, Issue 1, 2022 · pp. 7–15 Read article