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Epilepsy is one of the most common neurological disorders characterized by recurrent
seizures. Electroencephalography (EEG) serves as a crucial diagnostic tool for epilepsy, yet
traditional diagnosis relies heavily on manual interpretation, which is time-intensive, subjective,
and prone to errors. This study addresses the need for automated, reliable, and efficient
diagnostic methods by exploring the classification of healthy and epileptic individuals using raw
EEG data analyzed through a one-dimensional Convolutional Neural Network (1D CNN). The
proposed model was trained and evaluated on a dataset comprising 148 scalp EEG recordings
(72 epileptic patients and 78 healthy individuals) obtained from a local hospital. The CNN model
automatically extracted features from the EEG signals and achieved an accuracy of 97.73%,
sensitivity of 98%, and specificity of 98%. Channel-specific analyses were conducted to evaluate
the contribution of individual EEG channels, and the model's performance was further examined
by progressively reducing the number of channels. These findings underscore the potential of
integrating EEG data with deep learning for accurate, automated, and non-invasive epilepsy
diagnosis. Additionally, the study highlights the significance of channel reduction in simplifying
data while preserving diagnostic precision, facilitating more efficient clinical applications and
real-time seizure detection systems.