METHODS: We collected and analyzed functional near-infrared spectroscopy data of 38 participants while performing the revised lateralized attention network tast.
RESULTS: Elite players were significantly faster than novices (p = .005), and the experts' overall accuracy rate (ACC) was higher than that of novices (p = .001). The effect of the executive network on reaction time was higher in novices than in elite players (p = .008) and experts (p = .004). The effect of the executive network on the ACC was lower in elite players than in experts (p = .009) and novices (p = .010). Finally, elite player had higher flanker conflict effects on RT (p = .005) under the invalid cue condition. the effect of the alertness network and orientation on the ACC was lower in elite players than in novices (p = .000) and experts (p = .022). Changes in the blood oxygen level-dependent signal related to the flanker effect were significantly different in the right dorsolateral prefrontal cortex (F=3.980, p = .028) and right inferior frontal gyrus (F=3.703, p = .035) among the three groups. Elit players showed more efficient executive control (reduced conflict effect on ACC) (p = .006)in the RH.The changes related to the effect of blood oxygen level on orienting were significantly different in the right frontal eye fields (F=3.883, p = .030) among the three groups, Accompanied by significant activation of the right dorsolateral prefrontal cortex(p = .026).
CONCLUSION: Our findings provide partial evidence of the superior cognitive performance and high neural efficiency of elite ice hockey players during cognitive tasks. These results demonstrate the right hemisphere superiority for executive control.We also found that specific brain activation in hockey players does not show a clear and linear relationship with skill level.
METHOD: The paper explores a combination of variational mode decomposition (VMD), and Hilbert transform (HT) called VMD-HT to extract hidden information from EEG signals. Forty-one statistical parameters extracted from the absolute value of analytical mode functions (AMF) have been classified using the explainable boosted machine (EBM) model. The interpretability of the model is tested using statistical analysis and performance measurement. The importance of the features, channels and brain regions has been identified using the glass-box and black-box approach. The model's local and global explainability has been visualized using Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Morris sensitivity. To the best of our knowledge, this is the first work that explores the explainability of the model prediction in ADHD detection, particularly for children.
RESULTS: Our results show that the explainable model has provided an accuracy of 99.81%, a sensitivity of 99.78%, 99.84% specificity, an F-1 measure of 99.83%, the precision of 99.87%, a false detection rate of 0.13%, and Mathew's correlation coefficient, negative predicted value, and critical success index of 99.61%, 99.73%, and 99.66%, respectively in detecting the ADHD automatically with ten-fold cross-validation. The model has provided an area under the curve of 100% while the detection rate of 99.87% and 99.73% has been obtained for ADHD and HC, respectively.
CONCLUSIONS: The model show that the interpretability and explainability of frontal region is highest compared to pre-frontal, central, parietal, occipital, and temporal regions. Our findings has provided important insight into the developed model which is highly reliable, robust, interpretable, and explainable for the clinicians to detect ADHD in children. Early and rapid ADHD diagnosis using robust explainable technologies may reduce the cost of treatment and lessen the number of patients undergoing lengthy diagnosis procedures.