Learning ability evaluation has been critical in educational and medical fields to investigate learning achievement or cognitive impairment. Previous researchers utilized biosignal data such as functional near-infrared spectroscopy and an electroencephalogram to reflect neural variation in factors related to learning ability. Additionally, machine learning algorithms have been used to identify the inherent associations between learning ability and related factors. Herein, we propose a classification framework for college scholastic ability test levels using unsupervised features extracted from a functional near-infrared spectroscopy signal dataset based on machine learning models. To extract unsupervised features from functional near-infrared spectroscopy signals, we constructed a one-dimensional convolutional autoencoder with an electroencephalogram dataset as a transfer learning approach. Eight handcrafted features (signal mean, slope, minimum, peak, skewness, kurtosis, variance, and standard deviation) with various window length conditions were calculated to compare influences on classification performance. Five evaluation metrics (accuracy, precision, recall, F1-score, and area under the curve) were applied to evaluate the proposed framework's performance. Among the five classification algorithms (XGBoost classifier, support vector classifier, naive Bayes classifier, decision tree classifier, and logistic regression), the XGBoost classifier was the best at classifying college scholastic ability test levels. We found that unsupervised features extracted from deep learning algorithms are more usable for classification than handcrafted features. Furthermore, the applicability of transfer learning between two different neural modals was validated using the experimental results. The results of this study provide new insights into the relationships between hemodynamics in functional near-infrared spectroscopy signals and college scholastic ability test levels.
Bibliographical notePublisher Copyright:
© 2013 IEEE.
All Science Journal Classification (ASJC) codes
- General Computer Science
- General Materials Science
- General Engineering