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Fig. 5 | Journal of NeuroEngineering and Rehabilitation

Fig. 5

From: An artificial neural-network approach to identify motor hotspot for upper-limb based on electroencephalography: a proof-of-concept study

Fig. 5

a Mean error distances between the motor hotspot locations identified by TMS-induced MEP and our EEG-based machine learning approach with respect to the frequency band (RM-ANOVA with Bonferroni corrected p-value < 0.05: delta = theta = alpha > beta = gamma = full for both hands). The numbers below the bar graphs represent the mean error distances of each frequency band and their standard errors denoted by error bars. The standard deviations of the mean error distances are ± 0.70 for delta band, ± 0.79 for theta band, ± 0.72 for alpha band, ± 0.21 for beta band, ± 0.20 for gamma band, and ± 0.22 for full band, respectively. No significant difference was observed between the left and right hands for all the frequency bands in terms of the error distance (paired t-test p > 0.05), except the full band (right hand > left hand). b A representative example showing the 3D locations of the motor hotspots identified by TMS-induced MEP (blue rectangle) and our EEG-based approach with respect to the frequency band. The X, Y, and Z coordinates correspond to the left/right, posterior/anterior, and ventral/dorsal dimension, respectively, based on the Cz (origin: 0, 0, 0)

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