Optimizing prediction model for a noninvasive brain–computer interface platform using channel selection, classification, and regression

S Borhani, J Kilmarx, D Saffo, L Ng… - IEEE journal of …, 2019 - ieeexplore.ieee.org
A brain-computer interface (BCI) platform can be utilized by a user to control an external
device without making any overt movements. An EEG-based computer cursor control task is
commonly used as a testbed for BCI applications. While traditional computer cursor control
schemes are based on sensorimotor rhythm, a new scheme has recently been developed
using imagined body kinematics (IBK) to achieve natural cursor movement in a shorter time
of training. This article attempts to explore optimal decoding algorithms for an IBK paradigm …

[PDF][PDF] Optimizing Prediction Model for a Noninvasive Brain-Computer Interface Platform using Channel Importance, Classification, and Regression

J Kilmarx, D Saffo, L Ng - nics.utk.edu
Abstract A Brain-Computer Interface (BCI) platform can be utilized by a patient to control an
external device without making any overt movements. This can be beneficial to a variety of
patients who suffer from paralysis, loss of limb, or neurodegenerative diseases. In this
project, we introduce a noninvasive method to read and decode brain signals using
imagined body kinematics to control an onscreen cursor. A linear regression model was
designed to predict intended cursor velocity from a subject's thoughts. Using channel …
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