Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings

TL;DR

This study demonstrates that topological data analysis can improve hand pose classification accuracy from ECoG recordings by extracting hidden features, outperforming standard methods in limited-data scenarios.

Contribution

The paper introduces the integration of topological descriptors with traditional features for ECoG-based hand pose classification, showing improved accuracy and interpretability.

Findings

Topological features enhance classification accuracy.

Topological descriptors provide useful discriminative information.

Method shows robustness with limited data.

Abstract

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.