  In this study, a novel open-source brain-computer interface (BCI) platform
was developed to decode scalp electroencephalography (EEG) signals associated
with sustained attention. The EEG signal collection was conducted using a
wireless headset during a sustained visual attention task, where participants
were instructed to discriminate between composite images superimposed with
scenes and faces, responding only to the relevant subcategory while ignoring
the irrelevant ones. Seven volunteers participated in this experiment. The data
collected were subjected to analyses through event-related potential (ERP),
Hilbert Transform, and Wavelet Transform to extract temporal and spectral
features. For each participant, utilizing its extracted features, personalized
Support Vector Machine (SVM) and Random Forest (RF) models with tuned
hyperparameters were developed. The models aimed to decode the participant's
attentional state towards the face and scene stimuli. The SVM models achieved a
higher average accuracy of 80\% and an Area Under the Curve (AUC) of 0.86,
while the RF models showed an average accuracy of 78\% and AUC of 0.8. This
work suggests potential applications for the evaluation of visual attention and
the development of closed-loop brainwave regulation systems in the future.
