Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off León Palomares, Javier Escobar Pérez, Juan José Ortiz, Andrés Ortega Lopera, Julio González Peñalver, Jesús Martín Smith, Pedro Jesús Gan, John Q. Damas Hermoso, Miguel Electroencephalography (EEG) datasets are often small and high dimensional, owing to cumbersome recording processes. In these conditions, powerful machine learning techniques are essential to deal with the large amount of information and overcome the curse of dimensionality. Artificial Neural Networks (ANNs) have achieved promising performance in EEG-based Brain-Computer Interface (BCI) applications, but they involve computationally intensive training algorithms and hyperparameter optimization methods. Thus, an awareness of the quality-cost trade-off, although usually overlooked, is highly beneficial. In this paper, we apply a hyperparameter optimization procedure based on Genetic Algorithms to Convolutional Neural Networks (CNNs), Feed-Forward Neural Networks (FFNNs), and Recurrent Neural Networks (RNNs), all of them purposely shallow. We compare their relative quality and energy-time cost, but we also analyze the variability in the structural complexity of networks of the same type with similar accuracies. The experimental results show that the optimization procedure improves accuracy in all models, and that CNN models with only one hidden convolutional layer can equal or slightly outperform a 6-layer Deep Belief Network. FFNN and RNN were not able to reach the same quality, although the cost was significantly lower. The results also highlight the fact that size within the same type of network is not necessarily correlated with accuracy, as smaller models can and do match, or even surpass, bigger ones in performance. In this regard, overfitting is likely a contributing factor since deep learning approaches struggle with limited training examples. 2020-07-20T12:01:56Z 2020-07-20T12:01:56Z 2020-06-11 info:eu-repo/semantics/article León J, Escobar JJ, Ortiz A, Ortega J, González J, Martín-Smith P, et al. (2020) Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off. PLoS ONE 15(6): e0234178. https://doi.org/10.1371/journal.pone.0234178 http://hdl.handle.net/10481/63055 10.1371/journal. pone.0234178 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España Public Library of Science