META-CLASSIFICATION OF BRAIN SIGNALS IN P300 SPELLER
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Date
2021-05
Authors
Mussabayeva, Ayana
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
P300 Speller is a brain-computer interface (BCI) spelling system, which is used to enable
a human to communicate with the outer world by processing electroencephalography (EEG) sig-
nals. Such speller systems are usually used for patients with di
erent neural motor disorders, such
as amyotrophic lateral sclerosis (ALS). Human’s reaction to a visual stimuli can be detected using
event-related potential (ERP) components. A positive voltage deflection detected at about approxi-
mately 300 ms after the target visual stimulus, which is called P300 component, is usually used for
speller systems. The main problem is that di
erent people have di
erent latency and amplitude of
the P300 component, thus, P300 Speller has to be trained for each subject separately.
In order to achieve robust results for di
erent subjects, generic training (GT) approach is
proposed and tested on 20 healthy subjects data using linear discriminant analysis (LDA), support
vector machine (SVM), k-Nearest Neighbours (kNN) classifiers with di
erent hyperparameters. GT
training showed better performance than standard subject-specific training (SST) approach, and is
further used for ensemble learning experiments.
A set of ensemble models was proposed based on LDA, SVM, kNN, and convolutional
neural network (CNN). The proposed models were trained using multi-channel data from healthy
subjects and tested on both healthy subjects and ALS patients data. By trying di
erent number of
EEG channels, the best option of using four channels of parietal zone was found. The simulation
results show that the proposed ensemble model achieved better results compared to standalone clas-
sifiers. In order to achieve better user experience, the number of channels was decreased from 8 to 4
active channels. The obtained results show that using 4-channel EEG data is more e
cient. Single-
channel EEG data classification, however, provided bad performance. The fusion of LDA, kNN and
SVM provides the most accurate results, achieving the accuracy of 99.91% for healthy subjects and
almost 84.81% for ALS patients.The best results in terms of time complexity were provided by a
simple ensemble of LDA and kNN classifiers.
Description
Keywords
P300, amyotrophic lateral sclerosis, ALS, Research Subject Categories::TECHNOLOGY, event-related potential, ERPs, brain-computer interface, BCI, Type of access: Gated Access
Citation
Mussabayeva, A. (2021). Meta-Classification of Brain Signals in P300 Speller (Unpublished master's thesis). Nazarbayev University, Nur-Sultan, Kazakhstan