Version 1
: Received: 28 August 2023 / Approved: 29 August 2023 / Online: 31 August 2023 (03:13:41 CEST)
How to cite:
Collins, D. A Novel EEG Classification Method for Quantifying Kinematic Parameters. Preprints2023, 2023082103. https://doi.org/10.20944/preprints202308.2103.v1
Collins, D. A Novel EEG Classification Method for Quantifying Kinematic Parameters. Preprints 2023, 2023082103. https://doi.org/10.20944/preprints202308.2103.v1
Collins, D. A Novel EEG Classification Method for Quantifying Kinematic Parameters. Preprints2023, 2023082103. https://doi.org/10.20944/preprints202308.2103.v1
APA Style
Collins, D. (2023). A Novel EEG Classification Method for Quantifying Kinematic Parameters. Preprints. https://doi.org/10.20944/preprints202308.2103.v1
Chicago/Turabian Style
Collins, D. 2023 "A Novel EEG Classification Method for Quantifying Kinematic Parameters" Preprints. https://doi.org/10.20944/preprints202308.2103.v1
Abstract
Abstract: In recent years, low frequency (LF) electroencephalogram (EEG) signals have been decoded to obtain kinematic trajectories with the aim of achieving closed loop natural control. However, decoded trajectories suffer variability and low correlation against the measured movement. This paper suggests that if EEG features of motor intents are directly associated with fixed points along a given range of motion, then the natural kinematic parameters can be quantified. A novel classification method is proposed to evaluate this hypothesis along with equations for obtaining the movement parameters (velocity, acceleration). Furthermore, this implies that the force of an end-effector and work done in moving the end-effector may be computed, leading to natural control of EEG-based robotic arms and neuroprostheses. Finally, expectations are that combining the proposed method with existing classification methods will increase the accuracy and scalability of brain-computer interface (BCI) applications.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
