A Review of Motor Brain-Computer Interfaces using Intracranial Electroencephalography based on Surface Electrodes and Depth Electrodes

Xiaolong Wu, Benjamin Metcalfe, Shenghong He, Huiling Tan, Dingguo Zhang

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2 Citations (SciVal)

Abstract

Brain-computer interfaces (BCIs) provide a communication interface between the brain and external devices and have the potential to restore communication and control in patients with neurological injury or disease. For the invasive BCIs, most studies recruited participants from hospitals requiring invasive device implantation. Three widely used clinical invasive devices that have the potential for BCIs applications include surface electrodes used in electrocorticography (ECoG) and depth electrodes used in Stereo-electroencephalography (SEEG) and deep brain stimulation (DBS). This review focused on BCIs research using surface (ECoG) and depth electrodes (including SEEG, and DBS electrodes) for movement decoding on human subjects. Unlike previous reviews, the findings presented here are from the perspective of the decoding target or task. In detail, five tasks will be considered, consisting of the kinematic decoding, kinetic decoding,identification of body parts, dexterous hand decoding, and motion intention decoding. The typical studies are surveyed and analyzed. The reviewed literature demonstrated a distributed motor-related network that spanned multiple brain regions. Comparison between surface and depth studies demonstrated that richer information can be obtained using surface electrodes. With regard to the decoding algorithms, deep learning exhibited superior performance using raw signals than traditional machine learning algorithms. Despite the promising achievement made by the open-loop BCIs, closed-loop BCIs with sensory feedback are still in their early stage, and the chronic implantation of both ECoG surface and depth electrodes has not been thoroughly evaluated.
Original languageEnglish
Pages (from-to)2408-2431
Number of pages24
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume32
Early online date1 Jul 2024
DOIs
Publication statusPublished - 1 Jul 2024

Funding

This work is supported by EPSRC New Horizons Grant (EP/X018342/1). (Corresponding authors(*): Dingguo Zhang (email: [email protected]).) Xiaolong Wu, Xin Gao, Benjamin Metcalfe, and Dingguo Zhang are with the Department of Electronic & Electrical Engineering, University of Bath, UK Shenghong He and Huiling Tan are with MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford.

FundersFunder number
EPSRC - EUEP/X018342/1

    Keywords

    • Brain-computer interface (BCI)
    • deep brain stimulation (DBS)
    • electrocorticography (ECoG)
    • intracranial electroencephalography (iEEG)
    • stereo-electroencephalography (SEEG)

    ASJC Scopus subject areas

    • Internal Medicine
    • General Neuroscience
    • Biomedical Engineering
    • Rehabilitation

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