Frontiers in human neuroscience · 2025
Correlation analysis of upper limb muscle activation in the frequency domain in wheelchair fencers.
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Błaszczyszyn Monika, Piechota Katarzyna, Borysiuk Zbigniew, Kręcisz Krzysztof, Zmarzły Dariusz
Аннотация
BACKGROUND: The study includes a correlation analysis of EMG signals of upper limb muscle activity in wheelchair fencers. The aim of the study was to investigate neuromuscular conduction in wheelchair fencers using the EMG signal from their upper limb muscles.
METHODS: Wavelet transform analysis was used to examine the biosignals. The recorded EMG signals were subjected to time-frequency transformations. The scalograms were determined using the continuous wavelet transform. Based on the analysis, time-frequency coherence maps were extracted to determine validation in the frequency bands: 2-16 Hz, 17-30 Hz, and 31-60 Hz. The study participants were 16 wheelchair fencers, members of the Polish Paralympic Team, in two disability categories: 7 in category A and 9 in category B. Coherence was calculated for frequencies up to 60 Hz.
RESULTS: The analysis revealed the individual time-dependent coherence between two signals for different frequencies during the work cycle of the antagonist muscles of the arm (biceps/triceps) and forearm (flexor/extensor carpi radialis). A significant difference in alpha coherence (2-16 Hz) occurred in the group of forearm muscles in the frequency band of 2-16 Hz, both for G (p = 0.042) and M (p = 0.031) parameters (G: A - 0.08 Hz, B - 0.04 Hz; M: A - 0.51 and B - 0.42). Its peaks were observed during the fencing action cycle. Some differences in gamma coherence were also found in the EMG signals of the forearm muscles in the 31-60 Hz frequency band were statistically significant (p = 0.031): 0.43 in group A and 0.36 in group B.
CONCLUSION: The results showed the neuromuscular conduction, where alpha coherence reflects the reticulospinal tract responsible for the excitation of the distal muscles of the wrist and hand, while gamma coherence results from cortical signals. It is related to efferent conduction and reflects corticomuscular coupling. Frequency domain coherence analysis determines the strength of intermuscular synchronization, allowing a comprehensive investigation of the neural mechanisms underlying motor recovery. It maps separate neural pathways for arm and hand control.
METHODS: Wavelet transform analysis was used to examine the biosignals. The recorded EMG signals were subjected to time-frequency transformations. The scalograms were determined using the continuous wavelet transform. Based on the analysis, time-frequency coherence maps were extracted to determine validation in the frequency bands: 2-16 Hz, 17-30 Hz, and 31-60 Hz. The study participants were 16 wheelchair fencers, members of the Polish Paralympic Team, in two disability categories: 7 in category A and 9 in category B. Coherence was calculated for frequencies up to 60 Hz.
RESULTS: The analysis revealed the individual time-dependent coherence between two signals for different frequencies during the work cycle of the antagonist muscles of the arm (biceps/triceps) and forearm (flexor/extensor carpi radialis). A significant difference in alpha coherence (2-16 Hz) occurred in the group of forearm muscles in the frequency band of 2-16 Hz, both for G (p = 0.042) and M (p = 0.031) parameters (G: A - 0.08 Hz, B - 0.04 Hz; M: A - 0.51 and B - 0.42). Its peaks were observed during the fencing action cycle. Some differences in gamma coherence were also found in the EMG signals of the forearm muscles in the 31-60 Hz frequency band were statistically significant (p = 0.031): 0.43 in group A and 0.36 in group B.
CONCLUSION: The results showed the neuromuscular conduction, where alpha coherence reflects the reticulospinal tract responsible for the excitation of the distal muscles of the wrist and hand, while gamma coherence results from cortical signals. It is related to efferent conduction and reflects corticomuscular coupling. Frequency domain coherence analysis determines the strength of intermuscular synchronization, allowing a comprehensive investigation of the neural mechanisms underlying motor recovery. It maps separate neural pathways for arm and hand control.