Classification of EEG Signals Using Neural Networks to Predict Password Memorability

Feature extraction and classification is a subject of broad and current interest in the brain-computer interfaces (BCIs) community, and remains a challenging task when working with Electroencephalogram (EEG) data, with no agreed upon optimum features set and classifier algorithm. In this 75-participant lab study, we compare different feature extraction methods and classifiers as we investigate the relationship between users' perceptions of the memorability of a number of passwords and the users' EEG data collected using BCIs when presented with these passwords. We asked the participants to rank the comparative memorability of 15 blocks of 5 passwords each, while recording their EEG data. Features from the EEG signals are extracted in three domains, power spectrum from the frequency domain, statistics from the time domain, and wavelet coefficients from the time-frequency domain. The feature subsets are submitted for classification with two classes, most memorable and least memorable, based on the user's perceived memorability of the passwords. Classification performance of Neural Networks and Support Vector Machine algorithms are compared. Results show distinctive features of EEG signals in the two different classes, achieving a classification accuracy of 89%. Our results indicate that features extracted in the time-frequency domain using wavelet transform, and classified using neural networks, resulted in the highest classification performance.