Search
155 articles for “brain”
-
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
-
AI Driven IoT Based Decision Making System for Brain wave study: KSK approach for Brain wave study
Abstract: The rapid convergence of Internet of Things (IoT) architectures and deep learning has unlocked unprecedented potential for real-time neuro-diagnostic monitoring. This paper presents a novel framework for an AI-driven IoT ecosystem designed to capture, transmit, and interpret human electroencephalography (EEG) signals with minimal latency. Traditional brain-computer interface (BCI) studies are often constrained by localized computing power and the high dimensionality of neural data. Our proposed architecture integrates low-power EEG sensors …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 Read article
-
Brain Tumor Detection Through CNN: Techniques, Dataset Insights, and Methodology
Abstract: Computer technologies are playing huge roles in some areas of the medical domain like surgery and therapy of different diseases. Researchers are doing studies and trying to experiment to detect different diseases like cancer, virus infections, and leprosy. There are many different medical imaging datasets that are publicly available for medical research purposes of diseases like cancer, virus infections, and leprosy, etc. where we can be able to access large …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 1, 2025 · pp. 30–40 Read article
-
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
-
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
-
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
-
Classification and Detection of Brain Tumor using Convolutional Neural Network
Abstract: Tumors are masses created when brain cells multiply uncontrollably. A brain tumor is the medical term for this condition. Brain tumors are a serious and aggressive disease that can lead to a reduced life expectancy. Developing a treatment plan is essential to raising a patient's standard of living. Tumors in different regions of the body are evaluated using a variety of imaging techniques, with MRI pictures being utilized mostly for …
Published in International Journal of Cheminformatics · Vol. 1, Issue 1, 2023 · pp. 8–13 Read article
-
Bridging Brain-Inspired Learning and Quantum Reasoning for Future AGI Systems
Abstract: This research paper presents a novel neuromorphic–quantum hybrid computing framework envisioned to advance intelligent systems toward artificial general intelligence. The architecture integrates brain-inspired spiking networks for adaptive, energy-efficient learning with quantum processors for non-classical optimization and reasoning. A shared synaptic–quantum memory layer enables dual information representation, while neuromorphic adaptive controllers provide real-time stabilization of noisy quantum circuits. While quantum processors offer features like superposition- enabled exploration and entanglement-based correlations that …
Published in Current Trends in Signal Processing · Vol. 16, Issue 1, 2026 Read article
-
Development of Neuromorphic Polymer Composites Using IoT Sensing and Brain-Inspired Learning Algorithms
Abstract: This research aims to develop neuromorphic polymer composites by combining conductive sensing materials, IoT-based sensing data collection and brain-inspired learning models for adaptive response. Hybrid conductive polymer composites were developed by adding carbon nanofibers and graphene Nano platelets to a thermoplastic polymer. IoT sensors (strain, temperature) were employed to collect real-time sensing data that was combined with environmental data. A material-aware neuromorphic learning algorithm was created with event-driven spike coding …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 755–784 Read article
-
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
-
A Comprehensive Review on Nanostructured Polymer Composites for CT Imaging Contrast Enhancement in Brain Tumor Diagnosis
Abstract: The detection and characterization of brain tumors require high-resolution and non-invasive imaging modalities that have the ability of differentiating tumorous tissues and healthy brain tissues. Computed tomography (CT) can be included in this number because, in addition to providing speedy scans and penetration to deep tissue, the diagnostic capability in soft tissue organ such as the brain is poor despite low natural contrast. The review is dedicated to the emerging …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 148–162 Read article
-
Study on Brain Tumor Detection Using Morphological Operations in MATLAB with Graphical User Interface (GUI)
Abstract: Brain tumor detection plays a crucial role in early diagnosis and effective treatment planning. This research presents a MATLAB-based Graphical User Interface (GUI) for Brain Tumor Detection, incorporating a comprehensive pipeline of image processing techniques. The GUI provides a user-friendly platform, empowering medical professionals to accurately and efficiently analyze MRI brain scans. The GUI begins with text removal to eliminate any textual artifacts that may be present in the MRI …
Published in International Journal of Radio Frequency Innovations · Vol. 1, Issue 1, 2023 · pp. 24–31 Read article
-
Brain Tumor Detection by Aggregating Deep Learning and GAN Models for Faster MRI image Synthesis
Abstract: Brain tumors comprise a global health challenge that, in order to be treated and organized, need early and accurate diagnosis. Usually conducted through medical imaging, brain tumor detection techniques have problems of accuracy, efficiency, and confidentiality. Issues of limited datasets, strict privacy laws that provide restrictions on data sharing, and the necessity for specialized expertise on medical image analysis relegates modern methodologies to vulgar charades. For patient prognosis, treatment planning, …
Published in International Journal of Brain Sciences · Vol. 2, Issue 2, 2025 · pp. 45–53 Read article
-
Intelligent Brain Tumor Diagnosis with AI-Based Classification* * Harnessing Deep and Machine Learning for Tumor Identification
Abstract: Brain tumors have become a leading cause of cancer- related deaths, posing significant health risks to many patients. This urgent medical challenge calls for rapid, automated, and reliable techniques to detect brain tumors accurately. Timely and precise tumor identification is crucial for devising effective medical plans that have the potential to save lives and improve patient outcomes. By leveraging advanced image processing methods, healthcare professionals can enhance their diagnostic capabilities …
Published in International Journal of Cell Biology and Cellular Functions · Vol. 4, Issue 1, 2026 Read article
-
Circadian Regulation of Lipid Peroxidation in the Brain: Linking Ferroptosis to Neurodegenerative Vulnerability
Abstract: The human brain operates through highly coordinated physiological and biochemical processes that regulate cognition, behavior, and neural adaptability. Central to these processes are mechanisms governing brain function, neurophysiology, and neuroplasticity, which are increasingly recognized to be influenced by circadian rhythms. Recent advances in cognitive neuroscience, neuroimaging, and behavioral neuroscience have revealed that disruptions in circadian regulation can significantly impact oxidative balance within the brain, particularly through enhanced lipid peroxidation. Lipid …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 · pp. 1–14 Read article
-
Deep Learning Based Detection and Classification of Brain Tumors Using MRI Images
Abstract: Brain tumor detection using magnetic resonance imaging (MRI) is a critical task in the early detection and treatment of brain tumors. Manual analysis of brain tumor detection using MRI is a tedious task that requires expertise in the field. Therefore, this study proposes a deep learning-based approach for brain tumor detection and classification using Convolutional Neural Networks (CNN). The proposed approach preprocesses the MRI image using normalization, resizing, and noise …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 Read article
-
Identification and Categorization of Brain Tumors
Abstract: Brain tumors are a serious and aggressive disease that can lead to a reduced life expectancy. Strategic and well-thought-out treatment planning significantly contributes to improving a patient's overall quality of life. Many different imaging techniques, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and also ultrasound are used to evaluate tumors in different parts of the body, with a focus on using MRI images for brain tumors. It is …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 1, Issue 2, 2023 · pp. 32–37 Read article
-
Unani Concepts of Brain Pathology: Humoral Mechanisms in Māddī Neurological Disorders
Abstract: Background The Unani System of Medicine (USM) considers the brain (Dimāgh) as the principal organ of Quwā Nafsāniyya (psychic faculties), responsible for sensation, cognition, memory, movement, and emotional regulation. According to Unani principles, health is maintained through equilibrium of Mizāj (temperament) and Akhlāṭ (humours), whereas disease develops when this balance is disturbed. Among neurological disorders, Sū’-i-Mizāj Māddī Dimāgh refers to pathological conditions of the brain associated with the accumulation of …
Published in Journal of AYUSH: Ayurveda, Yoga, Unani, Siddha and Homeopathy · Vol. 15, Issue 2, 2026 Read article
-
U-Net Based Approach for Automated Brain Tumor Classification
Abstract: Brain tumor detection and identification play vital roles in diagnostic procedures in the field of medicine, with the conventional analysis of MRI images requiring a lot of time and also subject to variability. The proposed study involves the use of a CNN-U-Net based approach for brain tumor detection and identification automatically. The study uses a database of 3,064 contrast-enhanced T1-weighted MRI images from 233 patients with the tumors of meningioma, …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 2, 2025 Read article
-
Advancing Brain Tumor MRI Segmentation
Abstract: Segmentation of brain tumors in MRI scans is an integral part of neuroimaging carried out for diagnostic and therapeutic interventions. Given that manual segmentation is cumbersome and highly variable, there arises a need for automated, more precise segmentation solutions. This project, ‘Machine Learning and Deep Neural Networks to Advance Brain Tumor MRI Segmentation’ will develop a better, efficient, and accurate segmentation model to help clinicians identify brain tumors with greater …
Published in Recent Trends in Electronics Communication Systems · Vol. 12, Issue 2, 2025 · pp. 28–33 Read article