Fuzzy temporal convolutional neural networks in P300-based Brain-computer interface for smart home interaction
Christian Flores Vega, Jonathan Quevedo, Elmer Escand\'on, Mehrin, Kiani, Weiping Ding, Javier Andreu-Perez

TL;DR
This paper introduces EEG-TCFNet, a fuzzy temporal convolutional neural network that improves P300 signal classification in noisy environments for smart home BCI applications, demonstrating high accuracy and robustness.
Contribution
It proposes a novel neural network architecture combining TCNs, LSTM, and fuzzy neural blocks to enhance EEG signal classification in noisy, real-world settings.
Findings
Achieved up to 98.6% accuracy in subject-dependent classification.
Outperformed existing methods with fuzzy neural block integration.
Demonstrated robustness in noisy, natural environments.
Abstract
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…
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Taxonomy
MethodsTanh Activation · Sigmoid Activation · Long Short-Term Memory
