  The processing and classification of electroencephalographic signals (EEG)
are increasingly performed using deep learning frameworks, such as
convolutional neural networks (CNNs), to generate abstract features from brain
data, automatically paving the way for remarkable classification prowess.
However, EEG patterns exhibit high variability across time and uncertainty due
to noise. It is a significant problem to be addressed in P300-based Brain
Computer Interface (BCI) for smart home interaction. It operates in a
non-optimal natural environment where added noise is often present. In this
work, we propose a sequential unification of temporal convolutional networks
(TCNs) modified to EEG signals, LSTM cells, with a fuzzy neural block (FNB),
which we called EEG-TCFNet. Fuzzy components may enable a higher tolerance to
noisy conditions. We applied three different architectures comparing the effect
of using block FNB to classify a P300 wave to build a BCI for smart home
interaction with healthy and post-stroke individuals. Our results reported a
maximum classification accuracy of 98.6% and 74.3% using the proposed method of
EEG-TCFNet in subject-dependent strategy and subject-independent strategy,
respectively. Overall, FNB usage in all three CNN topologies outperformed those
without FNB. In addition, we compared the addition of FNB to other
state-of-the-art methods and obtained higher classification accuracies on
account of the integration with FNB. The remarkable performance of the proposed
model, EEG-TCFNet, and the general integration of fuzzy units to other
classifiers would pave the way for enhanced P300-based BCIs for smart home
interaction within natural settings.
