Classification performances of Ensemble (Subspace KNN), SVM, and KNN algorithms when trained with time domain HbO features.

<p>Classification performances of Ensemble (Subspace KNN), SVM, and KNN algorithms when trained with time domain HbO features.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Ayşenur Eser (21713911) (author)
مؤلفون آخرون:	Sinem Burcu Erdoğan (21713914) (author)
منشور في:	2025
الموضوعات:	Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified support vector machines personalized user experiences enhancing user experiences based bci systems two channel system twenty healthy participants three class classification offers significant potential knn ), ensemble hbo time traces e ., positive e ., high clinical applications due basic emotional states two class clinical applications emotional states svm ), subspace knn classification performances channel corresponding validation procedure validation groups tenfold cross ten runs stimulus period standardized stimuli prefrontal cortical precision metrics performance metric objective identification objective detection nearest neighbors mental health involved positive interactive entertainment infrared spectroscopy hemodynamic responses future developments functional near daily life computer interfaces computer interface advancing field adaptive learning accurate prediction 1 score
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