PT - JOURNAL ARTICLE AU - Ramiro Gatti AU - Yanina Atum AU - Luciano Schiaffino AU - Mads Jochumsen AU - José Biurrun Manresa TI - Convolutional Neural Networks Improve the Prediction of Hand Movement Speed and Force from Single-trial EEG AID - 10.1101/492660 DP - 2019 Jan 01 TA - bioRxiv PG - 492660 4099 - http://biorxiv.org/content/early/2019/05/22/492660.short 4100 - http://biorxiv.org/content/early/2019/05/22/492660.full AB - Objective Building accurate movement decoding models from brain signals is crucial for many biomedical applications. Decoding specific movement features, such as speed and force, may provide additional useful information at the expense of increasing the complexity of the decoding problem. Recent attempts to predict movement speed and force from the electroencephalogram (EEG) achieved classification accuracy levels not better than chance, stressing the demand for more accurate prediction strategies. Thus, the aim of this study was to improve the prediction accuracy of hand movement speed and force from single-trial EEG signals recorded from healthy volunteers.Approach A strategy based on convolutional neural networks (ConvNets) was tested, since it has previously shown good performance in the classification of EEG signals.Main results ConvNets achieved an overall accuracy of 84% in the classification of two different levels of speed and force (4-class classification) from single-trial EEG.Significance These results represent a substantial improvement over previously reported results, suggesting that hand movement speed and force can be accurately predicted from single-trial EEG.