Applying Conformal Prediction to Control an Exoskeleton


Charalambos Eliades, Harris Papadopoulos ;
Proceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR 105:214-227, 2019.


This paper investigates the use of the Conformal Prediction (CP) framework for providing confidence measures to assist a Brain Machine Interface (BMI) in the task of controlling an exoskeleton using electroencephalogram (EEG) and electrooculogram (EOG) clips. Reliable and accurate control of assistive robotics is still an important challenge because of the noisy nature of EEG’s and EOG’s and the fact that any misclassification can lead to unwanted actions and serious safety risks. Therefore a technique that will compliment predictions with a well-calibrated indication of how correct they are, should be very beneficial for the particular application as it can significantly enhance safety. Our approach consists of an Inductive Conformal Predictor (ICP) built on top of a Bidirectional Long Short Term Memory (BiLSTM) Neural Network. We conduct experiments on a dataset consisting of EEG and EOG data collected from one subject with a high spinal cord lesion.

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