Predicting the preference of liking using fNIRS and machine learning algorithms

The fMRI method, which is generally used to detect behavioral patterns, draws attention with its expensive and impractical features. On the other hand, the near-infrared spectroscopy (fNIRS) method is less expensive and portable, but it is as effective as fMRI in creating a good prediction model. With this method, a model has been developed that can predict whether a person likes a visual stimulus or not, using various classical machine learning algorithms including Support Vector Machines (SVM), Random Forests, XGBoost, LightGBM and K-Nearest Neighbors (KNN). With implementing tree-based and booster algorithms in addition to SVM and KNN which have been frequently used algorithms in this fNIRS domain, it was aimed to do a complementary comparison in addition to these acknowledged algorithms. Moreover, various missing value imputation methodologies were used to find the best suitable approach for this kind of classification problem. K-Means clustering, which is an unsupervised learning method, was also utilized to cluster similar fNIRS measurements of participants that may improve classification results by one-hot encoding those groups. Furthermore, certain feature extraction and wrapper methodologies were also applied for an attempt to enhance the performance of liking prediction models as a secondary goal. PCA, Isomap and t-SNE methodologies were implemented as feature extraction approaches, and forward selection wrapper design was utilized as an additional step to further development of the model by comparing their scores with each other. Cross-validation F1-scores of these models were used to find out the best model among them. Leave-one-group-out cross validation was exploited in comparison of the models. This meant that these cross-validation results corresponded to each of participants' data i.e. testing every participants' fNIRS measurements alone in each fold. This way both every score specific to each participant could be seen and it ensured models' results were statistically reliable. Following evaluations also included permutation and Wilcoxon Signed-Rank tests to compare each model's performance with each other by testing the statistical significance of those results. Keywords: machine learning, decision-making, optical brain imaging, fnirs, feature extraction, feature selection