Low-quality training data detection method of EEG signals for motor imagery BCI system

Ouyang, Rui; Jin, Zihao; Tang, Shuhao; Fan, Cunhang; Wu, Xiaopei

doi:10.1016/j.jneumeth.2022.109607

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…One of the most commonly used algorithms at this stage is independent component analysis (ICA), for example in the [ 36 ] study they use ICA to optimize the characterization of subject-independent MI signals, based on the ERD/ERS paradigm, improving the accuracy between 6.9% and 7.9%. Another study is [ 37 ] which proposes a method to extract features from low-quality EEG signals in combination with ICA.…”

Section: Introductionmentioning

confidence: 99%

Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI

Rodríguez-Azar,

Mejía-Muñoz,

Cruz-Mejía

et al. 2023

Sensors

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Brain-computer interfaces use signals from the brain, such as EEG, to determine brain states, which in turn can be used to issue commands, for example, to control industrial machinery. While Cloud computing can aid in the creation and operation of industrial multi-user BCI systems, the vast amount of data generated from EEG signals can lead to slow response time and bandwidth problems. Fog computing reduces latency in high-demand computation networks. Hence, this paper introduces a fog computing solution for BCI processing. The solution consists in using fog nodes that incorporate machine learning algorithms to convert EEG signals into commands to control a cyber-physical system. The machine learning module uses a deep learning encoder to generate feature images from EEG signals that are subsequently classified into commands by a random forest. The classification scheme is compared using various classifiers, being the random forest the one that obtained the best performance. Additionally, a comparison was made between the fog computing approach and using only cloud computing through the use of a fog computing simulator. The results indicate that the fog computing method resulted in less latency compared to the solely cloud computing approach.

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Section: Introductionmentioning

confidence: 99%

Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI

Rodríguez-Azar,

Mejía-Muñoz,

Cruz-Mejía

et al. 2023

Sensors

View full text Add to dashboard Cite

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Editorial for the special issue “Visual evoked brain computer interface studies”

Jin

Chen

Zhang

et al. 2023

Journal of Neuroscience Methods

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An Analysis of Traditional Methods and Deep Learning Methods in SSVEP-Based BCI: A Survey

Wu,

Wang

2024

Electronics

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The brain–computer interface (BCI) is a direct communication channel between humans and machines that relies on the central nervous system. Neuroelectric signals are collected by placing electrodes, and after feature sampling and classification, they are converted into control signals to control external mechanical devices. BCIs based on steady-state visual evoked potential (SSVEP) have the advantages of high classification accuracy, fast information conduction rate, and relatively strong anti-interference ability, so they have been widely noticed and discussed. From k-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM) classification algorithms to the current deep learning classification algorithms based on neural networks, a wide variety of discussions and analyses have been conducted by numerous researchers. This article summarizes more than 60 SSVEP- and BCI-related articles published between 2015 and 2023, and provides an in-depth research and analysis of SSVEP-BCI. The survey in this article can save a lot of time for scholars in understanding the progress of SSVEP-BCI research and deep learning, and it is an important guide for designing and selecting SSVEP-BCI classification algorithms.

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Low-quality training data detection method of EEG signals for motor imagery BCI system

Cited by 3 publications

References 36 publications

Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI

Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI

Editorial for the special issue “Visual evoked brain computer interface studies”

An Analysis of Traditional Methods and Deep Learning Methods in SSVEP-Based BCI: A Survey

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