Parkinson’s Disease Recognition from EEG Using Memory-Efficient Hexagonal Vision Convolutional Neural Network with Snake Optimizer

Early detection can help slow down Parkinson’s Disease (PD), which is a progressive neurological condition characterized by immense impairment of motor and cognitive functions. EEG is useful as a non-invasive diagnostic approach because PD alters brain activity. However, the methodologies developed so far using EEG suffer from such issues as interference due to noise, high computational cost, and poor accuracy. This paper presents an efficient Memory-Efficient Hexagonal Vision Convolutional Neural Network optimized with the Snake Optimizer, known as MEHVCNNet[Formula: see text]SO, for accurate recognition of PD. This framework first removes noise from the raw EEG signals using a Wavelet Decomposition Threshold Anisotropic Filter (WDTAF), followed by DenseNet-201 that extracts robust features. The classified features obtained are then fed into MEHVCNNet, which is optimized by the use of the Snake Optimizer. This optimization in the proposed MEHVCNNet[Formula: see text]SO framework enhances accuracy while reducing computational complexity. The experimental results on three benchmark EEG datasets, San Diego, UNM, and Iowa, indicated an outstanding performance that outperformed current state-of-the-art techniques, with 99.2% accuracy, 98.4% recall, and 0.1% error rate. Offering great potential in clinical applications, these findings demonstrated the proposed MEHVCNNet[Formula: see text]SO framework as a reliable and efficient means of early identification of PD.