Decoding kinetic features of hand motor preparation from single‐trial EEG using convolutional neural networks. (25th August 2020) - Gatti, Ramiro; Atum, Yanina; Schiaffino, Luciano; Jochumsen, Mads; Biurrun Manresa, José
Abstract: Building accurate movement decoding models from brain signals is crucial for many biomedical applications. Predicting specific movement features, such as speed and force, before movement execution 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 accuracies at or slightly above chance levels, highlighting the need for more accurate prediction strategies. Thus, the aims of this study were to accurately predict hand movement speed and force from single‐trial EEG signals and to decode neurophysiological information of motor preparation from the prediction strategies. To these ends, a decoding model based on convolutional neural networks (ConvNets) was implemented and compared against other state‐of‐the‐art prediction strategies, such as support vector machines and decision trees. ConvNets outperformed the other prediction strategies, achieving an overall accuracy of 84% in the classification of two different levels of speed and force (four‐class classification) from pre‐movement single‐trial EEG (100 ms and up to 1, 600 ms prior to movement execution). Furthermore, an analysis of the ConvNet architectures suggests that the network performs a complex spatiotemporal integration of EEG data to optimize classification accuracy. These results show that movement speed and force can be accurately predicted from single‐trial EEG, and that the prediction strategies may provide useful neurophysiological information about motor preparation. Abstract : Hand movement speed and force can be accurately predicted from pre‐movement, single‐trial electroencephalogram using convolutional neural networks (ConvNets). ConvNets require minimal pre‐processing, are not limited by feature selection or generation constrains and minimize experimenter bias. The neurophysiological information decoded from the ConvNets suggests that spatial information is crucial for classification performance.