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Gaussian Process for a Single-channel EEG Decoder with Inconspicuous Stimuli and Eyeblinks

Nur Syazreen Ahmad*, Jia Hui Teo, Patrick Goh

School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, 14300, Malaysia

* Corresponding Author: Nur Syazreen Ahmad. Email: email

Computers, Materials & Continua 2022, 73(1), 611-628. https://doi.org/10.32604/cmc.2022.025823

Abstract

A single-channel electroencephalography (EEG) device, despite being widely accepted due to convenience, ease of deployment and suitability for use in complex environments, typically poses a great challenge for reactive brain-computer interface (BCI) applications particularly when a continuous command from users is desired to run a motorized actuator with different speed profiles. In this study, a combination of an inconspicuous visual stimulus and voluntary eyeblinks along with a machine learning-based decoder is considered as a new reactive BCI paradigm to increase the degree of freedom and minimize mismatches between the intended dynamic command and transmitted control signal. The proposed decoder is constructed based on Gaussian Process model (GPM) which is a nonparametric Bayesian approach that has the advantages of being able to operate on small datasets and providing measurements of uncertainty on predictions. To evaluate the effectiveness of the proposed method, the GPM is compared against other competitive techniques which include k-Nearest Neighbors, linear discriminant analysis, support vector machine, ensemble learning and neural network. Results demonstrate that a significant improvement can be achieved via the GPM approach with average accuracy reaching over 96% and mean absolute error of no greater than 0.8 cm/s. In addition, the analysis reveals that while the performances of other existing methods deteriorate with a certain type of stimulus due to signal drifts resulting from the voluntary eyeblinks, the proposed GPM exhibits consistent performance across all stimuli considered, thereby manifesting its generalization capability and making it a more suitable option for dynamic commands with a single-channel EEG-controlled actuator.

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APA Style
Ahmad, N.S., Teo, J.H., Goh, P. (2022). Gaussian process for a single-channel EEG decoder with inconspicuous stimuli and eyeblinks. Computers, Materials & Continua, 73(1), 611-628. https://doi.org/10.32604/cmc.2022.025823
Vancouver Style
Ahmad NS, Teo JH, Goh P. Gaussian process for a single-channel EEG decoder with inconspicuous stimuli and eyeblinks. Comput Mater Contin. 2022;73(1):611-628 https://doi.org/10.32604/cmc.2022.025823
IEEE Style
N.S. Ahmad, J.H. Teo, and P. Goh "Gaussian Process for a Single-channel EEG Decoder with Inconspicuous Stimuli and Eyeblinks," Comput. Mater. Contin., vol. 73, no. 1, pp. 611-628. 2022. https://doi.org/10.32604/cmc.2022.025823



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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