Metric Learning in Freewill EEG Pre-Movement and Movement Intention Classification for Brain Machine Interfaces.

ABSTRACT: Decoding movement related intentions is a key step to implement BMIs. Decoding EEG has been challenging due to its low spatial resolution and signal to noise ratio. Metric learning allows finding a representation of data in a way that captures a desired notion of similarity between data points. In this study, we investigate how metric learning can help finding a representation of the data to efficiently classify EEG movement and pre-movement intentions. We evaluate the effectiveness of the obtained representation by comparing classification the performance of a Support Vector Machine (SVM) as a classifier when trained on the original representation, called Euclidean, and representations obtained with three different metric learning algorithms, including Conditional Entropy Metric Learning

SUBMITTER: Plucknett W 

PROVIDER: S-EPMC9283905 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature