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10 articles for “brain computer interaction”
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Human Robot Interface and Visual Compressive Detecting on Robot Arm
Abstract: Vision compressive sensing, brain machine reference commands, and adaptive controller have been effectively integrated that enable the robot to perform manipulation tasks as guided by human operator’s mind and according to its innovative views. The ideology proposed consists of following main phases: (1) action recognition (2) hardware interfacing. Initially action recognition captures video according to movements enacted in recognition system. Later, in hardware interfacing it passes required commands by a …
Published in Current Trends in Information Technology · Vol. 8, Issue 3, 2018 · pp. 5–12 Read article
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Study on Neuralink Chip: Brain–Computer Interface
Abstract: Neuralink, established by Elon Musk in 2016, is a neurotechnology firm focused on creating advanced brain-machine interfaces (BMIs) with enhanced data transfer capabilities. These interfaces aim to enable individuals to communicate more effectively and gain a deeper understanding of interactions with computers and other devices. One of the company's best experiences is the development of a new implantable device called the Neuralink chip. The Neuralink chip is a small device …
Published in Research and Reviews: A Journal of Health Professions · Vol. 13, Issue 1, 2023 · pp. 43–48 Read article
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Neuralink’s Role in Technology Upgradation
Abstract: Elon Musk established Neuralink, a firm that seeks to build neural implants that will enable a brain-machine interface. Neuralink is poised to bring in new developments in the area of human-computer interaction and dramatically enhance our technological capabilities with its cutting-edge technology. The implants developed by Neuralink have the potential to change a number of industries, including healthcare, education, and entertainment. The company's first focus is on assisting people with …
Published in Journal of Microwave Engineering and Technologies · Vol. 10, Issue 1, 2023 · pp. 8–13 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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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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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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Reverse Engineering’s Neural Network Approach to the Human Brain
Abstract: The organic components of the brain are capable of performing consistently and successfully in noisy situations. Brain components with highly adaptive or flexible interactions that could be low-precision, unpredictable, or excessively simultaneous are used to construct biological circuits. Two of the most remarkable characteristics of brain networks are their propensity to self-organize and their pattern organization. Recent research on neural networks, including artificial neural networks and convolutional neural networks (CNN), …
Published in Journal of Communication Engineering & Systems · Vol. 12, Issue 2, 2022 · pp. 17–24 Read article
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A Review on Detection of Autism Spectrum Disorder Using Signal Processing
Abstract: AbstractAutism is a neural developmental disability associated with impairments in communication and social interaction; it can be detected by various methods such as Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG). MRI is a technique which captures the image of various sections of brain. It is categorised as structural MRI (sMRI) and functional MRI (fMRI). The detection involves capturing the image, removing the unwanted regions of brain, segmenting the images and …
Published in Current Trends in Signal Processing · Vol. 8, Issue 2, 2018 · pp. 12–24 Read article
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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 · pp. 1–9 Read article
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ENHANCING CONTROL WITH EMBEDDED SSVEP-BCI
Abstract: Brain–Computer Interface (BCI) technology establishes a direct communication link between the human brain and external devices without relying on muscular activity. Among various BCI paradigms, the Steady-State Visually Evoked Potential (SSVEP)-based approach has gained significant attention due to its high signal-to-noise ratio, minimal user training, and suitability for real-time applications. However, implementing such systems on embedded hardware presents challenges such as limited computational resources, signal noise, and latency in processing. …
Published in Recent Trends in Electronics Communication Systems · Vol. 12, Issue 3, 2025 · pp. 41–52 Read article