EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

By Natasha M. J. Padfield, Jaime Zabalza, Huimin Zhao, Valentin Masero Vargas, Jinchang Ren. Italian National Conference on Sensors, 2019

508 citations recorded by the source corpus.

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1f3d0722f9b4011a0022bc6de06fb3f7de89f890

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In Cognitive Neuroscience

Citations in the corpus

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, background): “Continuous use of BCIs makes frequent use of speciﬁc neural pathways (Padﬁeld et al., 2019), and additional study needs to ﬁgure out the possible health risks or variations in brain functionality due to such prolonged exposure.”
Progress in Non-Invasive Cognitive Brain-Computer Interface and Implications for Mind-Uploading (2024, background): “Not only that, low-cost computational methods may also be good for use in certain populations or tasks only [97].”