COMPARATIVE ANALYSIS OF BRAIN ACTIVITY CLASSIFICATION USING CONVOLUTION NEURAL NETWORKS

Abstract

Electroencephalograms (EEGs) play a crucial role in detecting harmful brain activity by capturing electrical signals from the brain. This study aims to develop a deep learning framework for classifying EEG images into their respective brain activity categories. To achieve this, raw EEG signals were first converted into spectrograms, which provide a visual representation of signal frequencies over time. These spectrograms were then processed using pre-trained deep learning models to determine the most effective model for accurate classification. The dataset used for training and evaluation consists of EEG recordings categorized into six distinct brain activity labels. The deep learning models implemented for this classification task include VGG16, AlexNet, ResNet, and InceptionV3. The models were trained using a comprehensive dataset, and their performance was assessed using standard evaluation metrics. After extensive experimentation and comparison, the results indicate that InceptionV3 outperforms the other models in classifying EEG spectrograms. Its superior performance can be attributed to its advanced architecture, which effectively captures complex patterns within the spectrogram data. These findings highlight the potential of deep learning in analyzing EEG signals and classifying harmful brain activities with high accuracy. Future research could explore further improvements by incorporating more advanced neural architectures, such as Vision Transformers or custom-built CNN models, to enhance classification performance beyond what pre-trained models can achieve.