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Use of brain-computer interfaces (BCIs) is rapidly becoming a transformative approach for diagnosing and treating various brain disorders. By facilitating direct communication between the brain and external devices, BCIs have the potential to revolutionize neural activity monitoring, targeted neuromodulation strategies, and the restoration of brain functions. However, BCI technology faces significant challenges in achieving long-term, stable, high-quality recordings and accurately modulating neural activity. Traditional implantable electrodes, primarily made from rigid materials like metal, silicon, and carbon, provide excellent conductivity but encounter serious issues such as foreign body rejection, neural signal attenuation, and micromotion with brain tissue. To address these limitations, hydrogels are emerging as promising candidates for BCIs, given their mechanical and chemical similarities to brain tissues. These hydrogels are particularly suitable for implantable neural electrodes due to their three-dimensional water-rich structures, soft elastomeric properties, biocompatibility, and enhanced electrochemical characteristics. These exceptional features make them ideal for signal recording, neural modulation, and effective therapies for neurological conditions. This review highlights the current advancements in implantable hydrogel electrodes, focusing on their unique properties for neural signal recording and neuromodulation technologies, with the ultimate aim of treating brain disorders. A comprehensive overview is provided to encourage future progress in this field. Implantable hydrogel electrodes for BCIs have enormous potential to influence the broader scientific landscape and drive groundbreaking innovations across various sectors.
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http://dx.doi.org/10.1039/d4cs01074d | 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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