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Recent advances in nanotechnology have generated wide interest in applying nanomaterials for neural prostheses. An ideal neural interface should create seamless integration into the nervous system and performs reliably for long periods of time. As a result, many nanoscale materials not originally developed for neural interfaces become attractive candidates to detect neural signals and stimulate neurons. In this comprehensive review, an overview of state-of-the-art microelectrode technologies provided fi rst, with focus on the material properties of these microdevices. The advancements in electro active nanomaterials are then reviewed, including conducting polymers, carbon nanotubes, graphene, silicon nanowires, and hybrid organic-inorganic nanomaterials, for neural recording, stimulation, and growth. Finally, technical and scientific challenges are discussed regarding biocompatibility, mechanical mismatch, and electrical properties faced by these nanomaterials for the development of long-lasting functional neural interfaces.
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http://dx.doi.org/10.1002/adma.201304496 | DOI Listing |
Exp 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 PDFJ R Soc Interface
September 2025
Institute of Intelligent Systems and Robotics, Sorbonne Université, Paris, Île-de-France, France.
A number of techniques have been developed to measure the three-dimensional trajectories of protists, which require special experimental set-ups, such as a pair of orthogonal cameras. On the other hand, machine learning techniques have been used to estimate the vertical position of spherical particles from the defocus pattern, but they require the acquisition of a labelled dataset with finely spaced vertical positions. Here, we describe a simple way to make a dataset of images labelled with vertical position from a single 5 min movie, based on a tilted slide set-up.
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 PDFSmall Methods
September 2025
Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, 100871, China.
Understanding the brain's complexity and developing treatments for its disorders necessitates advanced neural technologies. Magnetic fields can deeply penetrate biological tissues-including bone and air-without significant attenuation, offering a compelling approach for wireless, bidirectional neural interfacing. This review explores the rapidly advancing field of magnetic implantable devices and materials designed for modulation and sensing of the brain.
View Article and Find Full Text PDFFood Res Int
November 2025
Institute of Eco-Environmental Forensics, School of Environmental Science and Engineering, Shandong University, Qingdao 266237, China; Key Laboratory of Colloid and Interface Chemistry, Ministry of Education, School of Chemistry and Chemical Engineering, Shandong University, Jinan 250100, China.
The origin and grade of green tea significantly influence its market value, yet their concurrent authentication remains challenging. Here, we developed a robust method combining multiple metallic elements and back propagation neural network (BPNN) to identify the origin and grade of tea simultaneously. This strategy utilizes inductively coupled plasma atomic emission spectrometry (ICP-AES) to analyze the content of multiple elements in green tea (e.
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