Robust Classification and Change Detection for Brain-Computer Interfaces

Vahid Tarokh

Duke University

In this talk, we will first discuss eye movement decoding in a working memory experiment involving a macaque monkey. Our objective is to use the local field potentials (LFPs) collected from the brain of the monkey to decode the type of task that the monkey is doing, and the direction of saccade in each task. We will show that the LFP time-series data can be modeled using a nonparametric regression framework, and show that the classifiers trained using minimax function estimators as features are robust and consistent. We will also discuss application of the resulting classifier to the brain data. 

We will then briefly discuss the problem of change detection apply it to spike data from a mice experiment collected using cues and electric shocks.

This is a joint work with Taposh Banerjee.

Published on April 4th, 2018

Last updated on March 29th, 2018

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