Microchip Expands Security Services in the Trust Platform to Help Manufacturers Meet Cybersecurity Regulations
TA101 TrustFLEX and TA101 TrustMANAGER combine factory- or in-field provisioned secure authentication ICs with scalable…
Year: 2026
Advancing Brain–Computer Interfaces for Rehabilitation and Assistive Technologies
Motor imagery electroencephalography (EEG) signals depict changes in brain activity during imagined limb movements. Conventional methods, however, often fail to capture these spatiotemporal variations. Researchers from Chiba University have developed a novel Embedding-Driven Graph Convolutional Network that can decode the spatiotemporal heterogeneity in EEG signals, advancing brain-computer interface technologies.
Image credit: Professor Akio Namiki from Chiba University, Japan
Image source link: https://www.sciencedirect.com/science/article/pii/S1566253526000497?via%3Dihub
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