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Query: "keywords" (motor imagery) .

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1.
Optimal sensor set for decoding motor imagery from EEG
Arnau Dillen, Fakhreddine Ghaffari, Olivier Romain, Bram Vanderborght, Uroš Marušič, Sidney Grosprêtre, Ann Nowé, Romain Meeusen, Kevin De Pauw, 2023, original scientific article

Abstract: Brain–computer interfaces (BCIs) have the potential to enable individuals to interact with devices by detecting their intention from brain activity. A common approach to BCI is to decode movement intention from motor imagery (MI), the mental representation of an overt action. However, research-grade electroencephalogram (EEG) acquisition devices with a high number of sensors are typically necessary to achieve the spatial resolution required for reliable analysis. This entails high monetary and computational costs that make these approaches impractical for everyday use. This study investigates the trade-off between accuracy and complexity when decoding MI from fewer EEG sensors. Data were acquired from 15 healthy participants performing MI with a 64-channel research-grade EEG device. After performing a quality assessment by identifying visually evoked potentials, several decoding pipelines were trained on these data using different subsets of electrode locations. No significant differences (p = [0.18–0.91]) in the average decoding accuracy were found when using a reduced number of sensors. Therefore, decoding MI from a limited number of sensors is feasible. Hence, using commercial sensor devices for this purpose should be attainable, reducing both monetary and computational costs for BCI control.
Keywords: brain-computer interface, motor imagery, feature reduction, electroencephalogram, machine learning
Published in DiRROS: 03.04.2023; Views: 773; Downloads: 354
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2.
Motor imagery and action observation as appropriate strategies for home-based rehabilitation : ǂa ǂmini-review focusing on improving physical function in orthopedic patients
Armin Paravlić, 2022, other scientific articles

Abstract: Dynamic stability of the knee and weakness of the extensor muscles are considered to be the most important functional limitations after anterior cruciate ligament (ACL) injury, probably due to changes at the central (cortical and corticospinal) level of motor control rather than at the peripheral level. Despite general technological advances, fewer contraindicative surgical procedures, and extensive postoperative rehabilitation, up to 65% of patients fail to return to their preinjury level of sports, and only half were able to return to competitive sport. Later, it becomes clear that current rehabilitation after knee surgery is not sufficient to address the functional limitations after ACL reconstruction even years after surgery. Therefore, new therapeutic tools targeting the central neural system, i.e., the higher centers of motor control, should be investigated and integrated into current rehabilitation practice. To improve motor performance when overt movement cannot be fully performed (e.g., due to pain, impaired motor control, and/or joint immobilization), several techniques have been developed to increase physical and mental activation without the need to perform overt movements. Among the most popular cognitive techniques used to increase physical performance are motor imagery and action observation practices. This review, which examines the available evidence, presents the underlying mechanisms of the efficacy of cognitive interventions and provides guidelines for their use at home.
Keywords: motor imagery, action observation, virtual reality, rehabilitation, physical functions, mental simulation
Published in DiRROS: 03.03.2022; Views: 807; Downloads: 640
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