Offline Lower-Limb Kinematic Decodification by Segments of EEG Signals.

In this work, hip and knee angles were decoded from low frequency EEG components recorded during the execution of two tasks. In order to compare their performance, three decoders based on multiple linear regression (MLR) models were applied under different conditions; which consisted in considering the processed data as a whole or divided into segments. Results suggest that, when the segments are related to specific tasks, the segmentation provides a better performance than applying the decoding method to unsegmented data.