RESUMEN
Engagement is described as a state in which an individual involved in an activity can ignore other influences. The engagement level is important to obtaining good performance especially under study conditions. Numerous methods using electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS) for the recognition of engagement have been proposed. However, the results were either unsatisfactory or required many channels. In this study, we introduce the implementation of a low-density hybrid system for engagement recognition. We used a two-electrode wireless EEG, a wireless ECG, and two wireless channels NIRS to measure engagement recognition during cognitive tasks. We used electrooculograms (EOG) and eye tracking to record eye movements for data labeling. We calculated the recognition accuracy using the combination of correlation-based feature selection and k-nearest neighbor algorithm. Following that, we did a comparative study against a stand-alone system. The results show that the hybrid system had an acceptable accuracy for practical use (71.65 ± 0.16%). In comparison, the accuracy of a pure EEG system was (65.73 ± 0.17%), pure ECG (67.44 ± 0.19%), and pure NIRS (66.83 ± 0.17%). Overall, our results demonstrate that the proposed method can be used to improve performance in engagement recognition.
Asunto(s)
Algoritmos , Cognición/fisiología , Análisis y Desempeño de Tareas , Adulto , Electrocardiografía , Electroencefalografía , Humanos , Espectroscopía Infrarroja Corta , Adulto JovenRESUMEN
Taberniacins A (1) and B (2), new indole alkaloids, were isolated from the stems of Tabernaemontana divaricata (Apocynaceae). Structure elucidation of 1 and 2 was based on spectroscopic methods and total synthesis. Each alkaloid showed vasorelaxant activity against phenylephrine-induced contraction of isolated rat aorta.