ConvoForest Classification of New and Familiar Faces using EEG

Face recognition by familiarity or recollection is a task people perform routinely in their daily lives. In the process of automating human experiences, existing studies have applied traditional machine learning applications and deep learning techniques on enough datasets (samples >= 1000) for human faces classification. However, the application of deep learning on electroencephalography (EEG) for new and familiar faces classification with limited data (samples < 100) has not been studied. We devised a face familiarity judgment EEG experiment and recruited eleven (11) participants for our study. We represented each trial by a visualization technique upon the generated EEG. The average power bands (theta, alpha, lower beta, higher beta, and gamma) from each channel at every 125ms window were computed and combined to form an image. We applied “ConvoForest,” a combination of convolution neural network (CNN) and random forest for classification. In comparison with conventional CNN where the dense layer was present, “ConvoForest” performed better with an average subject-dependent classification accuracy of 79.0% and an
$F_{1}$
score of 0.8