Implantable neurostimulation devices provide a direct therapeutic link to the nervous system and can be considered brain-computer interfaces (BCI). Under this definition, BCI are not simply science fiction, they are part of existing neurosurgical practice. Clinical BCI are standard of care for historically difficult to treat neurological disorders. These systems target the central and peripheral nervous system and include Vagus Nerve Stimulation, Responsive Neurostimulation, and Deep Brain Stimulation. Recent advances in clinical BCI have focused on creating "closed-loop" systems. These systems rely on biomarker feedback and promise individualized therapy with optimal stimulation delivery and minimal side effects. Success of clinical BCI has paralleled research efforts to create BCI that restore upper extremity motor and sensory function to patients. Efforts to develop closed loop motor/sensory BCI is linked to the successes of today's clinical BCI.
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http://dx.doi.org/10.1016/j.jocn.2019.07.056 | DOI Listing |
Hum Brain Mapp
December 2024
Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Intracortical microstimulation (ICMS) is a method for restoring sensation to people with paralysis as part of a bidirectional brain-computer interface (BCI) to restore upper limb function. Evoking tactile sensations of the hand through ICMS requires precise targeting of implanted electrodes. Here we describe the presurgical imaging procedures used to generate functional maps of the hand area of the somatosensory cortex and subsequent planning that guided the implantation of intracortical microelectrode arrays.
View Article and Find Full Text PDFBrain Res
December 2024
Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland. Electronic address:
Objectives: This narrative review aims to analyze mechanisms underlying Brain-Computer Interface (BCI) and Artificial Intelligence (AI) integration, evaluate recent advances in signal acquisition and processing techniques, and assess AI-enhanced neural decoding strategies. The review identifies critical research gaps and examines emerging solutions across multiple domains of BCI-AI integration.
Methods: A narrative review was conducted using major biomedical and scientific databases including PubMed, Web of Science, IEEE Xplore, and Scopus (2014-2024).
J Neural Eng
December 2024
West China Hospital of Sichuan University, No.37 Guoxue Alley, Wuhou District, Chengdu City, Sichuan Province, Chengdu, Sichuan, 610041, CHINA.
Objective: Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, which makes it possible to become a new means of human-machine interaction. However, the performance of current EEG decoding methods is still insufficient for clinical applications because of inadequate EEG information extraction and limited computational resources in hospitals. This paper introduces a hybrid network that employs a Transformer with modified locally linear embedding and sliding window convolution for EEG decoding.
View Article and Find Full Text PDFJ Dairy Sci
December 2024
Department of Pathology and Microbiology, Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC, Canada; Regroupement FRQNT Op+lait, Saint-Hyacinthe, QC, Canada. Electronic address:
Mastitis, an inflammation of the udder primarily caused by an intramammary infection, is one of the most common diseases in dairy cattle. Somatic cell count (SCC) has been widely used as an indicator of udder inflammation, assisting in the detection of subclinical mastitis. More recently, differential somatic cell count (DSCC), which represents the combined proportion of lymphocytes and polymorphonuclear leukocytes, has become available for routine dairy milk screening, though it was not yet widely studied.
View Article and Find Full Text PDFJ Neural Eng
December 2024
The University of Melbourne, Parkville, Melbourne, Victoria, 3010, AUSTRALIA.
Implantable brain-computer interfaces (iBCIs) hold great promise for individuals with severe paralysis and are advancing toward commercialization. The features required to achieve autonomous use of an iBCI may be under recognized in traditional academic measures of iBCI function and deserve further consideration to achieve successful clinical translation and patient adoption. Here, we present four key considerations to achieve autonomous use, reflecting the authors' perspectives based on discussions during various sessions and workshops across the 10th International BCI Society Meeting, Brussels, 2023: (1) immediate use, (2) easy to use, (3) continuous use, and (4) stable system use.
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