Brain-computer interfaces allow the exchange of data between the brain and an external device, bypassing the muscular system. Clinical studies of invasive brain-computer interface technologies have been conducted for over 20 years. During this time, there has been a continuous improvement of approaches to neuronal signal processing in order to improve the quality of control of external devices. Currently, brain-computer interfaces with intracortical implants allow completely paralyzed patients to control robotic limbs for self-service, use a computer or a tablet, type text, and reproduce speech at an optimal speed. Studies of invasive brain-computer interfaces regularly provide new fundamental data on functioning of the central nervous system. In recent years, breakthrough discoveries and achievements have been annually made in this sphere. This review analyzes the results of clinical experiments of brain-computer interfaces with intracortical implants, provides information on the stages of this technology development, its main discoveries and achievements.
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http://dx.doi.org/10.17691/stm2024.16.1.08 | DOI Listing |
PeerJ
January 2025
Faculty of Graduate Studies, Daffodil International University, Dhaka, Dhaka, Bangladesh.
Background: Functional magnetic resonance imaging (fMRI) has revolutionized our understanding of brain activity by non-invasively detecting changes in blood oxygen levels. This review explores how fMRI is used to study mind-reading processes in adults.
Methodology: A systematic search was conducted across Web of Science, PubMed, and Google Scholar.
Nat Med
January 2025
Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands.
Biomed Phys Eng Express
January 2025
F. Joseph Halcomb III, MD, Department of Biomedical Engineering, University of Kentucky, 143 Graham Ave., Lexington, Kentucky, 40506, UNITED STATES.
Brain-computer interfaces (BCIs) offer disabled individuals the means to interact with devices by decoding the electroencephalogram (EEG). However, decoding intent in fine motor tasks can be challenging, especially in stroke survivors with cortical lesions. Here, we attempt to decode graded finger extension from the EEG in stroke patients with left-hand paresis and healthy controls.
View Article and Find Full Text PDFPLoS One
January 2025
School of Electronics Engineering (SENSE), Vellore Institute of Technology, Vellore, Tamil Nadu, India.
In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlling various devices such as wheelchairs, assistive technologies, and driverless vehicles. However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation.
View Article and Find Full Text PDFBMC Geriatr
January 2025
Department of Electronic and Electrical Engineering, University of Liverpool, 9 Brownlow Hill, Liverpool, UK.
Background: Brain-computer interface (BCI) offers promising solutions to cognitive enhancement in older people. Despite the clear progress received, there is limited evidence of BCI implementation for rehabilitation. This systematic review addresses BCI applications and challenges in the standard practice of EEG-based neurofeedback (NF) training in healthy older people or older people with mild cognitive impairment (MCI).
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