Hyper parameter Optimization in CNNs for EEG Analysis

Abstract

Electroencephalography (EEG) is a crucial tool for assessing brain activity and diagnosing neurological disorders. The application of Convolutional Neural Networks (CNNs) in EEG analysis has shown promising results due to their ability to automatically learn and extract relevant features from complex, high-dimensional data. However, the effectiveness of CNNs is heavily influenced by hyper-parameters, which govern the network's architecture and training process. This paper focuses on hyper-parameter optimization (HPO) as a means to enhance the performance of CNNs in EEG analysis.

We explore various strategies for HPO, including traditional methods such as grid search and random search, as well as advanced techniques like Bayesian optimization and evolutionary algorithms. Each method's strengths and limitations are discussed in the context of EEG applications, emphasizing their role in improving model accuracy and generalization.

Additionally, we analyze the impact of different hyper-parameter configurations on CNN performance using case studies and comparative experiments. The findings indicate that automated HPO techniques can significantly enhance model robustness and efficiency, leading to more accurate interpretations of EEG signals. By optimizing hyper-parameters, researchers can leverage CNNs more effectively, ultimately advancing the field of EEG analysis and improving clinical outcomes. This paper contributes to the understanding of how systematic hyper-parameter optimization can play a pivotal role in maximizing the potential of CNNs for interpreting complex brain data.