Brain volumes are related with motor skills at late childhood in children born extremely preterm [0.03%]
极早早产儿晚期儿童期脑容积与运动技能的关系
Lina Broström,Hedvig Kvanta,Maria Örtqvist et al.
Lina Broström et al.
Among EPT children, those with motor impairment especially in aiming and catching, had notably smaller brain volume in the basal ganglia (mean difference:1.2 cm3, p = 0.049), cerebellum (mean difference:14.4 cm3, p < 0.001), motor execution (mean difference:3.7 cm3, p = 0.049) network and motor imagery
Effects of motor imagery in recovery of nerve blockade in patients undergoing total knee replacement under spinal anesthesia: a randomized prospective controlled study [0.03%]
运动想象对脊髓麻醉下行全膝关节置换术患者神经阻滞恢复的影响：一项随机前瞻性对照研究
Sung Woo Hyung,Jeong Won Moon,Eun Sang Lee et al.
Sung Woo Hyung et al.
Given the limitations of current pharmacologic and physical therapy strategies in accelerating neural recovery, motor imagery (MI)-a cognitive technique that activates motor and sensory pathways without physical movement-has emerged as a potential neuromodulatory intervention.
Randomized Controlled Trial
Journal of orthopaedic surgery and research. 2025 Jun 12;20(1):583. DOI:10.1186/s13018-025-05936-4 2025
Effects of short-term dual action-simulation training combined with transcranial magnetic stimulation on corticospinal excitation and finger motor performance [0.03%]
短期双动作模拟训练结合经颅磁刺激对皮质脊髓兴奋性和手指运动表现的影响
Kazumasa Konishi,Shinya Suzuki,Tsuyoshi Nakajima et al.
Kazumasa Konishi et al.
Action-simulation training using action observation (AO), motor imagery (MI), or a combination of both (AOMI) may improve motor function in patients with neurological diseases.
Roman domination-based spiking neural network for optimized EEG signal classification of four class motor imagery [0.03%]
基于罗马统治的尖峰神经网络优化四类运动想象EEG信号分类
Raja Sekhar Banovoth,Kadambari K V
Raja Sekhar Banovoth
However, the structural limitations of SNN hinder their feature extraction capabilities for motor imagery signal classification, which leads to under performance of the task....The model's performance was evaluated on three typically representative motor imagery datasets: PhysioNet, BCI Competition IV-2a, and BCI Competition IV-2b. RDSNN achieved 73.65% accuracy on PhysioNet, 81.75% on BCI IV-2a, and 84.56% on BCI IV-2b.
Assessing Motor Cortical Activity: How Repetitions Impact Motor Execution and Imagery Analysis [0.03%]
运动皮层活动的评估：重复次数对运动执行和意象分析的影响
Marta Borràs,Sergio Romero,Leidy Y Serna et al.
Marta Borràs et al.
Keywords: LORETA; electroencephalography (EEG); event related desynchronization (ERD); motor execution (ME); motor imagery (MI); motor‐related cortical activity; motor‐related cortical potential (MRCP). © 2025 The Author(s).
Stanisław Zakrzewski,Bartłomiej Stasiak,Adam Wojciechowski
Stanisław Zakrzewski
Methods: In this work, we propose a solution for a motor imagery classification task based on parallel factor analysis (PARAFAC) of EEG data.
Seyed Hojjat Zamani Sani,Serge Brand,Sahar Mohammadzadeh et al.
Seyed Hojjat Zamani Sani et al.
Motor imagery (MI) is a cognitive process requiring mental simulation of physical actions, engaging neural networks that overlap with those activated during actual execution. This study investigated the neural correlates of slow and fast MI...
Effects of motor imagery adding to physiotherapy and rehabilitation program in children with Duchenne Muscular Dystrophy: does it make a difference? [0.03%]
电机意象训练加入杜氏肌肉营养不良儿童的物理治疗和康复方案中效果如何？
Gülsena Utku Umut,Arzu Razak Özdi̇nçler,Fitnat Uluğ et al.
Gülsena Utku Umut et al.
Introduction/background: The study aims to investigate the effects of the MI (Motor Imagery) program applied in addition to the PTR (Physiotherapy and Rehabilitation) program on gait and balance in children with DMD (Duch...
A multi-modal dataset of electroencephalography and functional near-infrared spectroscopy recordings for motor imagery of multi-types of joints from unilateral upper limb [0.03%]
用于单侧上肢多类型关节运动想象的脑电和近红外融合数据集
Weibo Yi,Jiaming Chen,Dan Wang et al.
Weibo Yi et al.
As one of the important brain-computer interface (BCI) paradigms, motor imagery (MI) enables the control of external devices via identification of motor intention by decoding the features of Electroencephalography (EEG).
BrainFusion: a Low-Code, Reproducible, and Deployable Software Framework for Multimodal Brain‒Computer Interface and Brain‒Body Interaction Research [0.03%]
脑融合：一种低代码、可重复和可部署的软件框架，用于多模态脑机接口和脑身交互研究
Wenhao Li,Chenyang Gao,Zhaobo Li et al.
Wenhao Li et al.
Demonstrated in two case studies, BrainFusion achieves 95.5% accuracy in within-subject EEG-functional near-infrared spectroscopy (fNIRS) motor imagery classification using ensemble modeling and 80.2% accuracy in EEG-electrocardiography (ECG) sleep staging using deep learning, with the latter successfully
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