Fig. 4.

Comparison of performance of models for 3-class classification. (a) and (b) show boxplots of accuracy of models trained with features selected by different methods for classifying patients with TBI and stroke history and normal subjects. Accuracy was evaluated with 10-fold CV and independent dataset respectively. Models trained with BSFS and LDA selected features performed best in 10-fold CV. In IV, models trained with features selected by LDA, statistics, and FSFS showed comparable performance. (c) and (d) compare the accuracy of models trained with input features including demographic information and those without demographic information (Demo: demographic). The majority models with demographic inputs appear to perform better than their counterparts. (e) and (f) compare the performance of models trained with features generated from artifact removed clean EEG data versus those from raw EEG. The majority models built upon clean data performed significantly better than raw data. (∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, One way ANOVA and post-hoc Tukey test in (a) and (b).) (CV: cross-validation, Stats: statistics, LDA: linear discriminant analysis, FSFS: forward sequential feature selection, PCA: principal component analysis, BSFS: backwards sequential feature selection).