A Deep Learning approach to Noise Tagging
Keywords
Loading...
Authors
Issue Date
2021-07-22
Language
en
Document type
Journal Title
Journal ISSN
Volume Title
Publisher
Title
ISSN
Volume
Issue
Startpage
Endpage
DOI
Abstract
Brain-computer interfaces (BCI) are systems that allow direct communication
between the brain and a computer. A speci c type of BCI is a codemodulated
visual evoked potential (cVEP) based spelling BCI. These cVEP
BCIs can use di erent approaches to decoding and classifying brain signals.
Reconvolution, linear discriminant analysis (LDA) and deep learning are
some of these approaches. In my research, I implement my own adaptation
of a deep learning model and compare it against a baseline model which is
LDA, and I compare both deep learning and LDA against a simple event
type reconvolution. The deep learning approach (96.0% accuracy) is shown
to be signi cantly better (p < :05) than the simple event type reconvolution
(56.8% accuracy). Furthermore, on trial classi cation the deep learning approach
(96.0% accuracy) does not perform signi cantly di erent (p > :05)
from LDA (95.7% accuracy). On epoch classi cation deep learning (65.3%
accuracy) performs signi cantly better (p < :05) than LDA (63.2% accuracy).
This proves deep learning to be a useful option for cVEP BCIs with
many ways to go further in research.
Description
Citation
Supervisor
Faculty
Faculteit der Sociale Wetenschappen