Prediction of Hand Movement Speed and Force from Single-trial EEG with Convolutional Neural Networks

Abstract

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. A strategy based on convolutional neural networks (ConvNets) was tested, since it has previously shown good performance in the classification of EEG signals. ConvNets achieved an overall accuracy of 84% in the classification of two different levels of speed and force (4class classification) from single-trial EEG. 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.

Interspecific Genetic Differences and Historical Demography in South American Arowanas (Osteoglossiformes, Osteoglossidae, Osteoglossum)

“…The scenario 2 (vicariance) had an accuracy of 84%, while the third (TAb colonization) and the fourth (AMb colonization) scenarios had 87.1% and 79.3% of accuracy, respectively (Figure 6). Accuracy levels higher than 80% are considered acceptable for CNNs [68], therefore our approach presented overall appropriate values. After training and validation, the empirical data were submitted to CNN.…”