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The durability of communication with the use of brain-computer interfaces in persons with progressive neurodegenerative disease has not been extensively examined. We report on 7 years of independent at-home use of an implanted brain-computer interface for communication by a person with advanced amyotrophic lateral sclerosis (ALS), the inception of which was reported in 2016. The frequency of at-home use increased over time to compensate for gradual loss of control of an eye-gaze-tracking device, followed by a progressive decrease in use starting 6 years after implantation. At-home use ended when control of the brain-computer interface became unreliable. No signs of technical malfunction were found. Instead, the amplitude of neural signals declined, and computed tomographic imaging revealed progressive atrophy, which suggested that ALS-related neurodegeneration ultimately rendered the brain-computer interface ineffective after years of successful use, although alternative explanations are plausible. (Funded by the National Institute on Deafness and Other Communication Disorders and others; ClinicalTrials.gov number, NCT02224469.).
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http://dx.doi.org/10.1056/NEJMoa2314598 | DOI Listing |
J 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.
View Article and Find Full Text PDFNeuroimage
September 2025
The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, PR China; Brain-Computer Interface & Brain-Inspired Intelligence Key Laboratory of Sichuan Province, University of Electronic S
Functional magnetic resonance imaging (fMRI) opens a window on observing spontaneous activities of the human brain in vivo. However, the high complexity of fMRI signals makes brain functional representations intractable. Here, we introduce a state decomposition method to reduce this complexity and decipher individual brain functions at multiple levels.
View Article and Find Full Text PDFSci Bull (Beijing)
August 2025
Beijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Xiaoxiang Institute for Brain Health and Yongzhou Central