Many intriguing applications, such as the ability to move prosthetic limbs and enable more fluid man-machine contact, may be made possible by automatic interpretation of brain readings. The problem of precisely categorizing EEG signals linked with memory categories is tackled first in the inquiry. With the restricted availability of pre-trained models for such signal classification, a Convolution-based Neural Network (CNN) is constructed from scratch. By using EEG recordings from UC Berkeley’s Bio-Sense Lab, this study seeks to improve memory recall using machine learning and precise feature selection. Fifteen participants’ EEG data are converted into the frequency domain, and the amplitudes are used as key characteristics. The selection of these qualities is improved by a self-attention mechanism, which maximizes the distinction among various memory categories. The primary focus is to evaluate the performance of the most advanced algorithms, with the secondary objective of outperforming previous methods in terms of classification accuracy. A fine-tuned subset of the frequency-based characteristics is evaluated using a Support Vector Machine (SVM) classifier. By showcasing the efficiency of self-attention in honing feature subsets, this study highlights the significance of feature engineering in EEG-based memory classification. This method is positioned as a promising advancement in the analysis of EEG data since it improves the separation between memory categories through the application of frequency-domain modifications and SVM classifiers. Furthermore, investigating time series features shows how well they may capture intricate patterns, pointing to fresh avenues for future neuro-informatics and cognitive study.