Comparative Analysis of Deep Learning Approaches for Harmful Brain Activity Detection Using EEG

TL;DR

The paper tackles automated detection of harmful brain activities in EEG, comparing CNNs, Vision Transformers, and EEGNet using raw EEG and CWT-derived spectrogram representations. It demonstrates that a two-stage training regime, careful preprocessing (including spectrograms and montage), and data augmentation can yield substantial gains, often surpassing architecture advances alone, with multi-stage TinyViT and EfficientNet ensembles achieving the best performance. By evaluating with KL divergence on a multimodal dataset, the work shows that spectrogram-based, multi-modal approaches generalize better than raw-waveform models in this setting. The findings underscore the practical potential of robust, train-time-focused strategies to enable clinically reliable AI-assisted EEG analysis.

Abstract

The classification of harmful brain activities, such as seizures and periodic discharges, play a vital role in neurocritical care, enabling timely diagnosis and intervention. Electroencephalography (EEG) provides a non-invasive method for monitoring brain activity, but the manual interpretation of EEG signals are time-consuming and rely heavily on expert judgment. This study presents a comparative analysis of deep learning architectures, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and EEGNet, applied to the classification of harmful brain activities using both raw EEG data and time-frequency representations generated through Continuous Wavelet Transform (CWT). We evaluate the performance of these models use multimodal data representations, including high-resolution spectrograms and waveform data, and introduce a multi-stage training strategy to improve model robustness. Our results show that training strategies, data preprocessing, and augmentation techniques are as critical to model success as architecture choice, with multi-stage TinyViT and EfficientNet demonstrating superior performance. The findings underscore the importance of robust training regimes in achieving accurate and efficient EEG classification, providing valuable insights for deploying AI models in clinical practice.

Figures (5)

• Figure 1: EEG Waveform and Corresponding Spectrogram from the Dataset
• Figure 2: Spectrograms Generated from Preprocessed EEG Data
• Figure 3: Expert Consensus Classes Distribution
• Figure 4: Multi-Modal EEG Classification Model Architecture
• Figure 5: Confusion Matrix for EfficientNetB2