Task Recognition in BCI via Short- and Long-Term Dynamic Entropy with Robotic Aid in Sight

Zavala-Yoe, Ricardo; Cantillo-Negrete, Jessica; Ramírez-Mendoza, Ricardo A.

doi:10.1007/s13369-023-08281-y

Cited by 3 publications

(2 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to previous studies (in the literature), for clinical data, typically, ''m'' is to be set at 2, ''r'' to be set between 0.1 and 0.25 times the standard deviation of the data, and ''N'' as 1000 [25,[41][42][43][44]. However, there is still no consensus on determining these quantities, especially in short-term data [25,45]. Although some studies introduced particular entropy algorithms to make them less sensitive input parameters [45,46], there has yet to be a study that investigates this issue in human movement data specifically with gait data while the sensitivity of different entropy algorithms to changing parameters has been investigate.…”

Section: Hybrid Features In ML Modelsmentioning

confidence: 99%

“…However, there is still no consensus on determining these quantities, especially in short-term data [25,45]. Although some studies introduced particular entropy algorithms to make them less sensitive input parameters [45,46], there has yet to be a study that investigates this issue in human movement data specifically with gait data while the sensitivity of different entropy algorithms to changing parameters has been investigate. Therefore, we should consider that the choice of parameters used is critical for these types of datasets, in particular short data.…”

Section: Hybrid Features In ML Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing age-related postural sway classification using partial least squares-discriminant analysis and hybrid feature set

Alcan

2024

Neural Comput & Applic

View full text Add to dashboard Cite

Feature sets in a machine learning algorithm can have an impact on the robustness, interpretability, and characterization of the data. To detect age-related changes, traditional linear methods for analyzing center of pressure (COP) signals offer limited insight into the complex nonlinear dynamics of postural control. To overcome this limitation, a novel approach that combines a partial least squares-discriminant analysis (PLS-DA) classifier with the nonlinear dynamics of COP time series was proposed. Three small feature sets were compared: time-domain features alone, entropy-based features alone, and a hybrid approach incorporating both types of features. The performance of the PLS-DA model was assessed in four different eyes and surface conditions by using the accuracy, sensitivity, selectivity, precision metrics, and ROC curves. The results indicated that the PLS-DA model utilizing the hybrid feature set achieved significantly higher accuracy than the time-domain and entropy-based feature sets. The best classification performance was observed when the eyes were open on a compliant surface, with an overall accuracy of 89% for training and 88% for cross-validation. For the old group, while the results indicated 93% sensitivity, 94% specificity, and 93% precision in the training, the results revealed 88% sensitivity, 93% specificity, and 91% precision in cross-validation. Notably, the hybrid feature set yielded an AUC value of 0.96, indicating a superior performance. This study emphasizes the robust classification capabilities of PLS-DA for age-related postural changes and highlights the effectiveness of utilizing a small hybrid feature set to improve classification accuracy and reliability.

show abstract