It has been suggested that motor imagery ability develops gradually between 5 and 12 years of age, but ambiguity remains over the precise developmental course before 9 years. Hence, we determined the age-related differences in the use of motor imagery by children on the mental chronometry paradigm. In addition, we examined whether the use of motor imagery is related to cognitive and hand abilities. To this end, we compared duration of actual pointing and imagined pointing on a radial Fitts' task in 82 children (three age groups; 6-, 7-, and 8-year-olds). In line with previous studies, we found an age-related increase in temporal congruence between actual and imagined pointing and compliance with Fitts' law. Importantly, however, we showed that only a limited number of 7- and 8-year-olds were actually using motor imagery to perform the imagined pointing task, whereas the 6-year-olds did not employ motor imagery to perform the task. The current results extend previous research by establishing that the age of onset to use motor imagery in the mental chronometry paradigm is not prior to 7 years.
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http://dx.doi.org/10.1016/j.jecp.2015.06.008 | DOI Listing |
Neuroscience
January 2025
Kansai University of Health Sciences, Faculty of Health Sciences, Department of Physical Therapy, 2-11-1 Wakaba Sennangun Kumatori, Osaka 590-0482, Japan; Graduate School of Kansai University of Health Sciences, Graduate School of Health Sciences, 2-11-1 Wakaba Sennangun Kumatori, Osaka 590-0482, Japan.
Elderly adults may have poorer recall ability than young adults and may not fully enjoy the effects of motor imagery. To understand the age bias of the effect of motor imagery on hand dexterity, we evaluated brain activation and spinal motor nerve excitability. Brain activation was evaluated from changes in oxygenated hemoglobin concentration, while spinal motor nerve excitability was evaluated from F-waves in eight young (mean age 21.
View Article and Find Full Text PDFClin EEG Neurosci
January 2025
Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India.
Motor Imagery (MI) electroencephalographic (EEG) signal classification is a pioneer research branch essential for mobility rehabilitation. This paper proposes an end-to-end hybrid deep network "Spatio Temporal Inception Transformer Network (STIT-Net)" model for MI classification. Discrete Wavelet Transform (DWT) is used to derive the alpha (8-13) Hz and beta (13-30) Hz EEG sub bands which are dominant during motor tasks to enhance the performance of the proposed work.
View Article and Find Full Text PDFJ Neural Eng
January 2025
Shanghai Dianji University, shnaghai, Shanghai, Shanghai, 201306, CHINA.
Objective: Among all BCI paradigms, motion imagery (MI) has gained favor among researchers because it allows users to control external devices by imagining movements rather than actually performing actions. This property holds important promise for clinical applications, especially in areas such as stroke rehabilitation. Electroencephalogram (EEG) signals and functional near-infrared spectroscopy (fNIRS) signals are two of the more popular neuroimaging techniques for obtaining MI signals from the brain.
View Article and Find Full Text PDFThe complementary strengths of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have driven extensive research into integrating these two noninvasive modalities to better understand the neural mechanisms underlying cognitive, sensory, and motor functions. However, the precise neural patterns associated with motor functions, especially imagined movements, remain unclear. Specifically, the correlations between electrophysiological responses and hemodynamic activations during executed and imagined movements have not been fully elucidated at a whole-brain level.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang China.
The increasing adoption of wearable technologies highlights the potential of electroencephalogram (EEG) signals for biometric recognition. However, the intrinsic variability in cross-session EEG data presents substantial challenges in maintaining model stability and reliability. Moreover, the diversity within single-task protocols complicates achieving consistent and generalized model performance.
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