Hybrid and model based approaches for new BCI spellers
Author(s)
Advisor
Date
2019-07Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
Electroencephalography (EEG) based brain-computer interfaces (BCIs), due to
their non-invasive, portable and temporal resolution properties, are widely used
in the field of neural engineering. In order to make BCI paradigms more practical
and feasible for real life applications, new approaches are being tested such as
hybrid BCIs and model based BCIs. In the first phase of this study, a novel
hybrid speller BCI is proposed, incorporating P300 and code-modulated visual
evoked potential (c-VEP) paradigms, with the objective of improving the spelling
accuracy and information transfer rate (ITR), compared to individual P300 and
c-VEP paradigms. Moreover, fusion techniques have been applied in order to
effectively combine the information of P300 and c-VEP at the score level. We
have implemented and compared two different approaches, linear discriminant
analysis (LDA) and maximum probability estimation (MPE), in order to identify
which one works best for this hybrid BCI. The proposed BCI consists of 36 targets
presented as 6x6 matrix on screen with a refresh rate of 120 Hz. Seven healthy
subjects participated in experiments where each subject performed a training
session followed by five test sessions. The P300 and c-VEP signals are obtained
by using bandpass filters of 0.5-6 Hz and 6-41 Hz respectively, on the raw hybrid
data. For P300, stepwise linear discriminant analysis (SWLDA) is performed
on training data from all the 10 EEG channels to obtain the feature vector.
For c-VEP, canonical correlation analysis (CCA) is performed on training data
to obtain the reference templates for all 36 symbols. In comparison with the
accuracy and ITR values of c-VEP alone, that is without simultaneously making
use of the P300 data obtained during the hybrid experiments, MPE-based hybrid
improved only by 1.1% and 2.1 bits/min, on average, respectively, whereas the
values worsened by 12.3% and 19.8 bits/min in the case of LDA-based hybrid.
Moreover, the statistical tests on the mean accuracy and ITR values of all the
subjects showed that the results of MPE-based hybrid and of c-VEP alone are
not statistically different (p=0.293). Although the MPE-based hybrid is not
statistically better than the c-VEP alone, it can be highly effective if the primary
goal is to only increase the accuracy, using a range of improvements in P300
methods as discussed in conclusion. However, it would not be useful if the purpose
is to increase the speed of the speller since the individual c-VEP paradigm, when
optimized for timing, has the capability of giving an average ITR of 114.9bits/min
or higher, on its own. In the second phase of this study, model based c-VEP BCI
is implemented, aimed at improving the training time compared to the case where
all the targets are assigned arbitrary pseudorandom binary sequences and training
is required for all the symbols separately. For this purpose, moving average model
has been implemented to simulate the responses for c-VEP visual stimulation
patterns, for 60Hz and 120Hz monitor refresh rate respectively. The average of
the correlation between measured response and modeled response for 60Hz and
120Hz is 0.357 and 0.396 respectively. The average accuracy and ITR obtained
for model based c-VEP BCI is 87.1% and 76.4 bits/min for 60Hz respectively and
82.1% and 72.4 bits/min for 120Hz respectively. Modeling results suggest that it
is possible to perform a training on a single visual stimulus pattern and achieve
a good fit model.
Keywords
Brain-computer interface (BCI)Electroencephalogram (EEG)
P300
Code-modulated visual evoked potential (c-VEP)
Stepwise linear discriminant analysis (SWLDA)
Canonical correlation analysis (CCA)
Maximum probability estimation (MPE)
Modeling
Moving average