Nour, MajidArabacı, BahadırÖcal, HakanPolat, Kemal2024-09-252024-09-2520241758-87151758-8723https://doi.org/10.1504/IJIEI.2024.137706https://hdl.handle.net/20.500.12491/14006This study employs the University of Bonn Dataset to address the importance of frequency information in EEG data and introduces a methodology utilising the short-time Fourier transform. The proposed method transforms conventional 1D EEG signals into informative 2D spectrograms, offering an approach for advancing the detection of neurological diseases. Integrating advanced CNN architectures with the conversion of EEG signals into 2D spectrograms forms the foundation of our proposed methodology. The 1D CNN model utilised in this study demonstrates exceptional performance metrics, achieving a specificity of 0.996, an overall test accuracy of 0.991, a sensitivity of 0.987, and an F1 score of 0.989. Shifting to the 2D approach discloses a slight reduction in accuracy to 0.987, sensitivity of 0.976, specificity of 0.988, and an F1 score of 0.97. This analysis provides nuanced insights into the performance of 1D and 2D CNNs, clarifying respective strengths in the context of neurological disease detection.eninfo:eu-repo/semantics/closedAccessSeizure PredictionEpilepsyEEG Signals1D Convolutional Neural NetworkDeep LearningClassificationArticle10.1504/IJIEI.2024.1377061212-s2.0-85189666246N/AWOS:001196022700001N/A