A Decoding Model of Upper Limb Movement Intention Based on Data Augmentation

With the aging of the population, the application of brain-computer interfaces(BCIs) in the neural decoding of upper limb motion direction is becoming more extensive. However, how to improve the recognition accuracy of neural decoding is one of the key problems given limited training samples. In thi...

Full description

Saved in:
Bibliographic Details
Published in:Chinese Automation Congress (Online) pp. 4257 - 4260
Main Authors: Ke, Xi, Bi, Luzheng, Fei, Weijie, Feleke, Aberham Genetu
Format: Conference Proceeding
Language:English
Published: IEEE 25.11.2022
Subjects:
Aging
Analytical models
Automation
BCI
Data Augmentation
Data models
electroencephalogram(EEG)
generative adversarial networks(GANs)
Sociology
Training
Training data
ISSN:2688-0938
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With the aging of the population, the application of brain-computer interfaces(BCIs) in the neural decoding of upper limb motion direction is becoming more extensive. However, how to improve the recognition accuracy of neural decoding is one of the key problems given limited training samples. In this paper, we proposed a neural decoding method of upper limb motion direction based on data augmentation. We used the deep convolutional generative adversarial networks(DCGANs), which is a data augmentation algorithm to generate more data to expand the training set to improve the accuracy of the model. We completed analysis on different numbers of real training data across eight subjects. The analysis results show that after using data augmentation, the average decoding accuracy given small amounts of training samples significantly increases, showing that the DCGANs algorithm can indeed improve the accuracy of the neural decoding model, and help to improve the practical application of BCIs in decoding upper limb motion.
ISSN:2688-0938
DOI:10.1109/CAC57257.2022.10055468