  This study evaluates the discriminating capacity (uniqueness) of the EEG data
from the WAY EEG GAL public dataset to authenticate individuals against one
another as well as its permanence. In addition to the EEG data, Luciw et al.
provide EMG (Electromyography), and kinematics data for engineers and
researchers to utilize WAY EEG GAL for further studies. However, evaluating the
EMG and kinematics data is outside the scope of this study. The goal of the
state-of-the-art is to determine whether EEG data can be utilized to control
prosthetic devices. On the other hand, this study aims to evaluate the
separability of individuals through EEG data to perform user authentication. A
feature importance algorithm is utilized to select the best features for each
user to authenticate them against all others. The authentication platform
implemented for this study is based on Machine Learning models/classifiers. As
an initial test, two pilot studies are performed using Linear Discriminant
Analysis (LDA) and Support Vector Machine (SVM) to observe the learning trends
of the models by multi-labeling the EEG dataset. Utilizing kNN first as the
classifier for user authentication, accuracy around 75% is observed. Thereafter
to improve the performance both linear and non-linear SVMs are used to perform
classification. The overall average accuracies of 85.18% and 86.92% are
achieved using linear and non-linear SVMs respectively. In addition to
accuracy, F1 scores are also calculated. The overall average F1 score of 87.51%
and 88.94% are achieved for linear and non-linear SVMs respectively. Beyond the
overall performance, high performing individuals with 95.3% accuracy (95.3% F1
score) using linear SVM and 97.4% accuracy (97.3% F1 score) using non-linear
SVM are also observed.
