For sign languages, transitional movements of the hands are fully visible and may be used to predict upcoming linguistic input. We investigated whether and how deaf signers and hearing nonsigners use transitional information to detect a target item in a string of either pseudosigns or grooming gestures, as well as whether motor imagery ability was related to this skill. Transitional information between items was either intact (Normal videos), digitally altered such that the hands were selectively blurred (Blurred videos), or edited to only show the frame prior to the transition which was frozen for the entire transition period, removing all transitional information (Static videos). For both pseudosigns and gestures, signers and nonsigners had faster target detection times for Blurred than Static videos, indicating similar use of movement transition cues. For linguistic stimuli (pseudosigns), only signers made use of transitional handshape information, as evidenced by faster target detection times for Normal than Blurred videos. This result indicates that signers can use their linguistic knowledge to interpret transitional handshapes to predict the upcoming signal. Signers and nonsigners did not differ in motor imagery abilities, but only non-signers exhibited evidence of using motor imagery as a prediction strategy. Overall, these results suggest that signers use transitional movement and handshape cues to facilitate sign recognition.
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http://dx.doi.org/10.1016/j.actpsy.2023.103923 | 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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