Fuzzy temporal convolutional neural networks in P300-based Brain–computer interface for smart home interaction

• Published in: Applied soft computing Vol. 117; p. 108359
• Main Authors: Vega, Christian Flores, Quevedo, Jonathan, Escandón, Elmer, Kiani, Mehrin, Ding, Weiping, Andreu-Perez, Javier
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2022
• Subjects: Convolutional neural networks; EEG-based BCI; Fuzzy neural networks; P300; Smart home interaction; Temporal neural networks; Temporal neural networks; EEG-based BCI; Smart home interaction; Fuzzy neural networks; Convolutional neural networks; P300
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2021.108359

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), 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. •EEG-TCFNet is proposed for P300 BCI of smart home interaction in natural environments.•An assessment of Fuzzy Neural Block (FNB) with different deep learning architectures.•The study contains both healthy and patients (post-stroke) in the considered BCI scenario.•The results indicate higher decoding accuracy of the proposed model.