Plos One, ss.1-18, 2022 (SCI-Expanded)
The event related P300 potentials, positive waveforms in
electroencephalography (EEG) signals, are often utilized in brain computer interfaces
(BCI). Many studies have been carried out to improve the performance of P300
speller systems either by developing signal processing algorithms and
classifiers with different architectures or by designing new paradigms. In this study, a new paradigm is proposed for this
purpose. The proposed paradigm combines two remarkable properties of being a 3D
animation and utilizing column-only flashings as opposed to classical paradigms
which are based on row-column flashings in 2D manner. The new paradigm is
utilized in a traditional two-layer artificial neural networks model with a
single output neuron, and numerous experiments are conducted to evaluate and
compare the performance of the proposed paradigm with that of the classical
approach. The experimental results, including statistical significance tests,
are presented for single and multiple EEG electrode usage combinations in 1, 3
and 15 flashing repetitions to detect P300 waves as well as to recognize target
characters. Using the proposed paradigm, the best average classification
accuracy rates on the test data are improved from 89.97% to 93.90% (an
improvement of 4.36%) for 1 flashing, from 97.11% to 98.10% (an improvement of
1.01%) for 3 flashings and from 99.70% to 99.81% (an improvement of 0.11%) for
15 flashings when all electrodes, included in the study, are utilized. On the
other hand, the accuracy rates are improved by 9.69% for 1 flashing, 4.72% for
3 flashings and 1.73% for 15 flashings when the proposed paradigm is utilized
with a single EEG electrode (P8). It is observed that the proposed speller paradigm is
especially useful in BCI systems designed for few EEG electrodes usage, and
hence, it is more suitable for practical implementations. Moreover, all
participants, given a subjective test, declared that the proposed paradigm is
more user-friendly than classical ones.