PARADIGM-INDEPENDENT CLASSIFICATION ON MULTIDIMENSIONAL NEUROIMAGING DATASET USING CONVOLUTIONAL NEURAL NETWORKS
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Date
2021-05
Authors
Alimanov, Kanat
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
Brain-Computer Interfaces (BCI) are gaining popularity with each day. A lot of
research was carried out to improve both accuracy and usability of individual BCI
paradigms, most popular in current literature being Motor Imagery(MI), Event-Related
Potential (ERP)and Steady-State Visually Evoked Potential (SSVEP). However,
individual BCI paradigms are limited in some areas such as number of classes in
cases of MI and SSVEP paradigms, illiteracy in case of MI, or fatigue induction in
users in case of ERP amd SSVEP paradigm. This study presents a new approach to
designing BCI systems called paradigm-independent BCI, which solves these problems
by allowing the subjects to use any of the three paradigms at any time. For each
paradigm, the system processes EEG signals and feeds them through a deep learning
model to get a representation vector, these vectors are then concatenated and fed to a
final classifier that is used to decide which paradigm is currently being used on the
fly. Average classification accuracy for three-class between-paradigm decoding was
97.51%(±3.39) in subject-independent training. Classification accuracy for seven-class
within-paradigm decoding resulted in 87.9%(±4.51) accuracy. The results show that
this framework works equally great in both subject-dependent and subject-independent
contexts.
Description
Keywords
BCI, Brain-Computer Interfaces, Motor Imagery, MI, Research Subject Categories::TECHNOLOGY, Type of access: Open Access
Citation
Alimanov, K. (2021). Paradigm-Independent Classification on Multidimensional Neuroimaging Dataset Using Convolutional Neural Networks (Unpublished master's thesis). Nazarbayev University, Nur-Sultan, Kazakhstan