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10 articles for “Brain Machine Interface”
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Brain Machine Interface: A Review of Current Technologies and Future Directions
Abstract: Brain Machine Interface (BMI) is a rapidly growing field that aims to establish direct communication between the brain and an external device. This technology has the potential to restore lost motor and sensory functions in people with neurological disorders or injuries. A brain-computer interface (BCI) is a technology that converts signals from the brain into instructions for computers or other gadgets. This innovation allows individuals to engage with their surroundings …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 1, Issue 1, 2023 · pp. 14–24 Read article
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The Ethics and Implications of Brain–Computer Interfaces: Enhancing Human Abilities and Redefining Privacy
Abstract: This study addresses the ethical considerations and social implications of brain–computer interface (BCI) development and integration. BCI, sometimes called a brain-machine interface (BMI) or smart brain, is a direct communication path between the brain and electrical activity and an external device, usually a computer or robotic limb. BCIs are often directed towards researching, mapping, assisting, improving, or correcting human cognitive or sensorimotor functions. The implementation of BCIs varies from non-invasive …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 1, 2024 · pp. 24–28 Read article
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Mind-Machine Synergy: The Evolution and Future of Brain-Computer Interfaces
Abstract: Brain-Computer Interfaces (BCIs) represent a transformative technology that enables the direct communication between the human brain and external devices, bypassing the traditional output mechanisms, such as speech or physical movement. BCIs hold the potential to revolutionize fields, such as healthcare, neuroscience, and human-computer interaction by providing new ways to restore lost functions, enhance cognitive abilities, enable seamless communication, and create novel user experiences across various platforms and environments. This article …
Published in International Journal of Brain Sciences · Vol. 2, Issue 2, 2025 · pp. 19–31 Read article
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Investigations On Use of Poly(3,4-Ethylenedioxythiophene): Poly (Styrene Sulfonic Acid) (PEDOT: PSS) Conductive Polymers for Design of Improved EEG Based Brain Computer Interface for Seizure Control and Analysis
Abstract: This research explores the application of Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonic acid) (PEDOT:PSS) conductive polymers in the design of an enhanced Electroencephalography (EEG)-based Brain-Computer Interface (BCI) for seizure control and analysis. PEDOT: PSS, known for its high conductivity, flexibility, and biocompatibility, is employed to improve the efficiency and sensitivity of EEG electrodes, addressing challenges such as signal noise, skin-electrode impedance, and user comfort. The study evaluates the material’s properties, including its electrical conductivity, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 3, 2025 · pp. 223–241 Read article
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Neuroinformatics and Its Impact on the Future of Brain-Computer Interface Technology
Abstract: Neuroinformatics, a multidisciplinary field combining neuroscience, information technology, and data science, plays a crucial role in advancing brain-computer interface (BCI) technology. By leveraging large-scale neural data, machine learning algorithms, and computational models, neuroinformatics enhances our understanding of brain function and improves the design and development of BCIs. The integration of neuroinformatics into BCI systems offers new possibilities for interpreting complex brain signals, facilitating real-time communication between the brain and external …
Published in Research and Reviews : Journal of Computational Biology · Vol. 13, Issue 3, 2024 · pp. 9–18 Read article
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Identifying and Implementing a Machine Learning Model Suitable for Processing Visually Evoked Potential
Abstract: A Brain-Computer Interface (BCI) is a system that translates brain activity patterns into computer commands, bypassing physical movement. Electroencephalography (EEG) is commonly used to acquire signals in BCI research. Visual evoked potentials (VEPs) are brain responses in the visual cortex to visual stimuli. Recent studies show that exposing individuals to flickering at a consistent frequency generates EEG signals synchronized with the stimulation. Efficient extraction of VEP signals begins with preprocessing …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 1–8 Read article
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Machine Learning-Based Disease Prediction: A Comparative Analysis for Diabetes, Brain Tumor, and Parkinson's Disease
Abstract: This paper presents a web-based disease prediction system that integrates machine learning and deep learning techniques to assist in the early detection of Parkinson’s Disease, Diabetes, and Brain Tumors. By utilizing clinical data and MRI images, the platform provides rapid and interpretable predictions to support proactive health management. Logistic Regression models are applied to classify structured datasets for predicting Parkinson’s disease and Diabetes, making use of their effectiveness in binary …
Published in Current Trends in Signal Processing · Vol. 15, Issue 2, 2025 · pp. 44–54 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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Advancement in Image Classification: Media Player Control Using Hand Gestures
Abstract: We explore the development of picture categorization methods in this paper, with an emphasis on how they are used to manipulate media players with hand gestures. Our investigation focuses on the development of machine learning techniques, particularly on supporting vector machines (SVM) and convolutional neural networks (CNN). SVMs are used to identify and authenticate people from digital photos or video clips, but CNNs are great at face detection, which is …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 2, 2024 · pp. 1–10 Read article
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Virtual Method to Predict Dental Disease
Abstract: The integration of technology and medicine in the healthcare domain has led to the emergence of inventive strategies to improve patient care and diagnostics. One such groundbreaking methodology is the utilization of Convolutional Neural Networks (CNNs) within the domain of deep learning, particularly for image recognition and processing tasks. In this paper, we propose a novel approach to image recognition that employs state-of-the-art deep learning algorithms to create a user-friendly …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 8–15 Read article