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This paper introduces a novel technique called "Forged Channel," which aimsto comprehensively represent EEG signals in order to achieve accurateclassification of Parkinson's disease. The forged channel method prepares EEGsignals in a manner that allows a deep learning model to effectively perceiveall EEG channels within a single input. By employing this approach alongside aconvolutional neural network, an impressive accuracy of 90.32% was achievedusing leave-one-subject-out cross-validation. This performance closely reflectsreal-world conditions, highlighting the superiority of our method compared tosimilar approaches.

Forged Channel: A Breakthrough Approach for Accurate Parkinson's Disease Classification using Leave-One-Subject-Out Cross-Validation