Randomized Reference Classifier with Gaussian Distribution and Soft Confusion Matrix Applied to the Improving Weak Classifiers

In this paper, an issue of building the RRC model using probability distributions other than beta distribution is addressed. More precisely, in this paper, we propose to build the RRR model using the truncated normal distribution. Heuristic procedures for expected value and the variance of the truncated-normal distribution are also proposed. The proposed approach is tested using SCM-based model for testing the consequences of applying the truncated normal distribution in the RRC model. The experimental evaluation is performed using four different base classifiers and seven quality measures. The results showed that the proposed approach is comparable to the RRC model built using beta distribution. What is more, for some base classifiers, the truncated-normal-based SCM algorithm turned out to be better at discovering objects coming from minority classes.Comment: arXiv admin note: text overlap with arXiv:1901.0882

Predicting Humans’ Identity and Mental Load from EEG: Performed by AI

EEG-based brain machine/computer interfaces (BMIs/BCIs) have a wide range of clinical and non-clinical applications. Mental workload (MW) classification, emotion recognition, motor imagery, seizure detection, and sleep stage scoring are among the active BCI research areas. One of the relatively new BCI area is EEG-based human subject recognition (i.e., EEG biometric). There still exist several challenges that need to be addressed to design a successful EEG-based biometric model applicable for real-world environments. First, there is a need for a protocol that can elicit the individual dependent EEG responses in a short period of time. A classification algorithm with high generalization power is also required to deal with the EEG signals classification task. The latter is a common challenge for all EEG-based BCI paradigms; given the non-stationary nature of the EEG signals and the small size of the EEG datasets. In addition, to building a stable EEG biometric model, the effects of human mental states (e.g., emotion, mental load) on the model performance needs to be carefully examined. In this thesis, a new protocol for the area of the EEG biometric has been proposed. The proposed protocol called “(the) N-back task” is based on the human working memory and the experimental results obtained in this thesis prove that the EEG signals elicited by the N-back task contain subject specific features, even for very short time intervals. It has also been shown that three load levels of the typical N-back task are all capable of evoking subject specific EEG features. As a result, the N-back task can be used as a protocol having more than one mode (i.e, cancelable protocol) that comes with added security benefits. The EEG signals evoked by the N-back task have been used to train a compact convolutional neural network called the EEGNet. A configuration of the EEGNet having 16 temporal and 2 spatial filters has reached an identification accuracy of approximately 97% using data instances as short as 1.1s for a pool of 26 subjects. To further improve the accuracy, a novel ensemble classifier has been designed in this thesis. The principle underlying the proposed ensemble is the “division and exclusion” of the EEG channels guided by scalp locations. The ensemble classifier has (statistically significantly) improved the subject recognition rate from 97% to 99%. Performance of the proposed ensemble model has also been assessed in the EEG-based MW classification paradigm. The ensemble classifier outperformed the single EEGNet as well as a state-of-the-art classifier called WLnet in the challenging scenario of the subject-independent (cross-subject) MW classification. The results suggest that the ensemble structure proposed in this thesis can generalize to different BCI paradigms. Finally, effects of the mental workload on the performance of the EEG-based subject authentication models have been thoroughly explored in this thesis. The obtained results affirm that MW of the genuine and impostor subjects at the train and test phases have significant effects on both false negative rate (FNR) and false positive rate (FPR) of an authentication system. Different subjects have also shown different clusters of authentication behaviors when affected by the MW changes. This finding establishes the importance of the human’s mental load in the design of real-world EEG authentication systems and introduces a new investigation line for the EEG biometric community