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This paper presents a novel model for classifying motor imagery EEG signals by integrating Gramian Angular Fields (GAF) and Phase-Locking Value (PLV) with a parallel convolutional neural network (CNN). The proposed method effectively captures both temporal and spatial features of EEG signals, leading to significant improvements in classification accuracy, achieving 99.73% for binary tasks and 83.37% for four-class tasks on the Physionet dataset. The study highlights the importance of considering spatiotempo

Author

Laura Flores Branttes

Language

EN