  This paper introduces a novel technique called "Forged Channel," which aims
to comprehensively represent EEG signals in order to achieve accurate
classification of Parkinson's disease. The forged channel method prepares EEG
signals in a manner that allows a deep learning model to effectively perceive
all EEG channels within a single input. By employing this approach alongside a
convolutional neural network, an impressive accuracy of 90.32% was achieved
using leave-one-subject-out cross-validation. This performance closely reflects
real-world conditions, highlighting the superiority of our method compared to
similar approaches.
