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Brain-computer interfaces (BCIs) enable control of computers and other assistive devices, such as neuro-prostheses, which are used for communication, movement restoration, neuro-modulation, and muscle stimulation, by using only signals measured directly from the brain. A BCI creates a new output channel for the brain to a computer or a device. This requires retrieval of signals of interest from the brain, and its use for neuro-rehabilitation by means of interfacing the signals to a computerized device. Brain signals such as action potentials from single neurons or nerve fibers, extracellular local field potentials (LFPs), electrocorticograms, electroencephalogram and its components such as the event-related brain potentials, real-time functional magnetic resonance imaging, near-infrared spectroscopy, and magneto-encephalogram have been used. BCIs are envisaged to be useful for communication, control and self-regulation of brain function. BCIs employ neurofeedback to enable operant conditioning to allow the user to learn using it. Paralytic conditions arising from stroke or other diseases are being targeted for BCI application. Neurofeedback strategies ranging from sensory feedback to direct brain stimulation are being employed. Existing BCIs are limited in their throughput in terms of letters per minute or commands per minute, and need extensive training to use the BCI. Further, they can cause rapid fatigue due to use and have limited adaptability to changes in the patient's brain state. The challenge before BCI technology for neuro-rehabilitation today is to enable effective clinical use of BCIs with minimal effort to set up and operate.
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http://dx.doi.org/10.1615/critrevbiomedeng.2014010697 | DOI Listing |
Sci Adv
September 2025
School of Electrical and Electronic Engineering, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea.
Brain-computer interfaces (BCIs) enable direct communication between the brain and computers. However, their long-term functionality remains limited due to signal degradation caused by acute insertion trauma, chronic foreign body reaction (FBR), and biofouling at the device-tissue interface. To address these challenges, we introduce a multifunctional surface modification strategy called targeting-specific interaction and blocking nonspecific adhesion (TAB) coating for flexible fiber, achieving a synergistic integration of mechanical compliance and biochemical stability.
View Article and Find Full Text PDFExp Brain Res
September 2025
School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China.
This study explores how differences in colors presented separately to each eye (binocular color differences) can be identified through EEG signals, a method of recording electrical activity from the brain. Four distinct levels of green-red color differences, defined in the CIELAB color space with constant luminance and chroma, are investigated in this study. Analysis of Event-Related Potentials (ERPs) revealed a significant decrease in the amplitude of the P300 component as binocular color differences increased, suggesting a measurable brain response to these differences.
View Article and Find Full Text PDFBiomed Phys Eng Express
September 2025
electrical engineering department, Indian Institute of Technology Roorkee, Research wing, electrical department, Roorkee, uttrakhand, 247664, INDIA.
Imagined speech classification involves decoding brain signals to recognize verbalized thoughts or intentions without actual speech production. This technology has significant implications for individuals with speech impairments, offering a means to communicate through neural signals. The prime objective of this work is to propose an innovative machine learning (ML) based classification methodology that combines electroencephalogram (EEG) data augmentation using a sliding window technique with statistical feature extraction from the amplitude and phase spectrum of frequency domain EEG segments.
View Article and Find Full Text PDFJ Integr Neurosci
August 2025
School of Computer Science, Guangdong Polytechnic Normal University, 510665 Guangzhou, Guangdong, China.
Background: Emotion recognition from electroencephalography (EEG) can play a pivotal role in the advancement of brain-computer interfaces (BCIs). Recent developments in deep learning, particularly convolutional neural networks (CNNs) and hybrid models, have significantly enhanced interest in this field. However, standard convolutional layers often conflate characteristics across various brain rhythms, complicating the identification of distinctive features vital for emotion recognition.
View Article and Find Full Text PDFACS Appl Bio Mater
September 2025
Biomedical Engineering Faculty, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-34311, Iran.
The development of high-performance neural interfaces is critical for advancing brain-machine communication and treating neurological disorders. A major challenge in neural electrode design is achieving a seamless biological-electronic interface with optimized electrochemical properties, mechanical stability, and biocompatibility. In this study, we introduce a hierarchical micronanostructured poly(3,4-ethylenedioxythiophene)-polydopamine (PEDOT-PDA) coating on titanium nitride (TiN) microelectrodes engineered to enhance electrophysiological signal recording and neural integration.
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