Classifying Numbers from EEG Data – Which Neural Network Architecture Performs Best?

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Abstract

This paper presents a comparison of deep learning models for classifying P300 events, i.e., event-related potentials of the brain triggered during the human decision-making process. The evaluated models include CNN, (Bi | Deep | CNN-) LSTM, ConvLSTM, LSTM + Attention. The experiments were based on a large publicly available EEG dataset of school-age children conducting the “Guess the number”-experiment. Several hyperparameter choices were experimentally investigated resulting in 30 different models included in the comparison. Ten models with good performance on the validation data set were also automatically optimized with Grid Search. Monte Carlo Cross Validation was used to test all models on test data with 30 iterations. The best performing model was the Deep LSTM with an accuracy of 77.1% followed by the baseline (CNN) 76.1%. The significance test using a 5×2 cross validation paired t-test demonstrated that no model was significantly better than the baseline. We recommend experimenting with other architectures such as Inception, ResNet and Graph Convolutional Network.