  Electrocorticogram (ECoG) well characterizes hand movement intentions and
gestures. In the present work we aim to investigate the possibility to enhance
hand pose classification, in a Rock-Paper-Scissor - and Rest - task, by
introducing topological descriptors of time series data. We hypothesized that
an innovative approach based on topological data analysis can extract hidden
information that are not detectable with standard Brain Computer Interface
(BCI)techniques. To investigate this hypothesis, we integrate topological
features together with power band features and feed them to several standard
classifiers, e.g. Random Forest,Gradient Boosting. Model selection is thus
completed after a meticulous phase of bayesian hyperparameter optimization.
With our method, we observed robust results in terms of ac-curacy for a
four-labels classification problem, with limited available data. Through
feature importance investigation, we conclude that topological descriptors are
able to extract useful discriminative information and provide novel
insights.Since our data are restricted to single-patient recordings,
generalization might be limited. Nevertheless, our method can be extended and
applied to a wide range of neurophysiological recordings and it might be an
intriguing point of departure for future studies.
