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Deep neural networks (DNN) have been studied in various machine learningareas. For example, event-related potential (ERP) signal classification is ahighly complex task potentially suitable for DNN as signal-to-noise ratio islow, and underlying spatial and temporal patterns display a large intra- andintersubject variability. Convolutional neural networks (CNN) have beencompared with baseline traditional models, i.e. linear discriminant analysis(LDA) and support vector machines (SVM) for single trial classification using alarge multi-subject publicly available P300 dataset of school-age children (138males and 112 females). For single trial classification, classificationaccuracy stayed between 62% and 64% for all tested classification models. Whenapplying the trained classification models to averaged trials, accuracyincreased to 76-79% without significant differences among classificationmodels. CNN did not prove superior to baseline for the tested dataset.Comparison with related literature, limitations and future directions arediscussed.

Evaluation of convolutional neural networks using a large multi-subject P300 dataset