Brain-computer interfaces (BCIs) allow control of various applications or external devices solely by brain activity, e.g., measured by electroencephalography during motor imagery. Many users are unable to modulate their brain activity sufficiently in order to control a BCI. Most of the studies have been focusing on improving the accuracy of BCI control through advances in signal processing and BCI protocol modification. However, some research suggests that motor skills and physiological factors may affect BCI performance as well. Previous studies have indicated that there is differential lateralization of hand movements' neural representation in right- and left-handed individuals. However, the effects of handedness on sensorimotor rhythm (SMR) distribution and BCI control have not been investigated in detail yet. Our study aims to fill this gap, by comparing the SMR patterns during motor imagery and real-feedback BCI control in right- (N = 20) and left-handers (N = 20). The results of our study show that the lateralization of SMR during a motor imagery task differs according to handedness. Left-handers present lower accuracy during BCI performance (single session) and weaker SMR suppression in the alpha band (8-13 Hz) during mental simulation of left-hand movements. Consequently, to improve BCI control, the user's training should take into account individual differences in hand dominance.
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http://dx.doi.org/10.1038/s41598-020-59222-w | DOI Listing |
Med Biol Eng Comput
December 2024
School of Mechanical Engineering, Yanshan University, Qinhuangdao, China.
This study focuses on improving the performance of steady-state visual evoked potential (SSVEP) in brain-computer interfaces (BCIs) for robotic control systems. The challenge lies in effectively reducing the impact of artifacts on raw data to enhance the performance both in quality and reliability. The proposed MVMD-MSI algorithm combines the advantages of multivariate variational mode decomposition (MVMD) and multivariate synchronization index (MSI).
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).
Rev Sci Instrum
December 2024
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
Lower-limb exoskeletons have become increasingly popular in rehabilitation to help patients with disabilities regain mobility and independence. Brain-computer interface (BCI) offers a natural control method for these exoskeletons, allowing users to operate them through their electroencephalogram (EEG) signals. However, the limited EEG decoding performance of the BCI system restricts its application for lower limb exoskeletons.
View Article and Find Full Text PDFCogn Neurodyn
December 2024
Laboratory of Brain Atlas and Brain-Inspired Intelligence, Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China.
Motor imagery (MI) is an important brain-computer interface (BCI) paradigm. The traditional MI paradigm (imagining different limbs) limits the intuitive control of the outer devices, while fine MI paradigm (imagining different joint movements from the same limb) can control the mechanical arm without cognitive disconnection. However, the decoding performance of fine MI limits its application.
View Article and Find Full Text PDFCogn Neurodyn
December 2024
Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, 650500 P. R. China.
The development and potential applications of brain-computer interfaces (BCIs) are directly related to the human brain and may have adverse effects on the users' physical and mental health. Ethical issues, particularly those associated with BCIs, including both non-medical and medical applications, have captured societal attention. This article initially reviews the application of three ethical frameworks in BCI technology: consequentialism, deontology, and virtue ethics.
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