Low-frequency Motor Cortex EEG Predicts Four Rates of Force Development

O'Keeffe R, Shirazi SY, Del Vecchio A, Ibanez J, Mrachacz-Kersting N, Bighamian R, Rizzo JR, Farina D, Atashzar SF (2024)

Publication Type: Journal article

Publication year: 2024

Journal

IEEE Transactions on Haptics Institute of Electrical and Electronics Engineers

Pages Range: 1-12

DOI: 10.1109/TOH.2024.3428308

Abstract

The movement-related cortical potential (MRCP) is a low-frequency component of the electroencephalography (EEG) signal that originates from the motor cortex and surrounding cortical regions. As the MRCP reflects both the intention and execution of motor control, it has the potential to serve as a communication interface between patients and neurorehabilitation robots. In this study, we investigated the EEG signal recorded centered at the Cz electrode with the aim of decoding four rates of force development (RFD) during isometric contractions of the tibialis anterior muscle. The four levels of RFD were defined with respect to the maximum voluntary contraction (MVC) of the muscle as follows: Slow (20% MVC/s), Medium (30% MVC/s), Fast (60% MVC/s), and Ballistic (120% MVC/s). Three feature sets were assessed for describing the EEG traces in the classification process. These included: (i) MRCP Morphological Characteristics in the $\delta$-band, such as timing and amplitude; (ii) MRCP Statistical Characteristics in the $\delta$-band, such as standard deviation, mean, and kurtosis; and (iii) Wideband Time-frequency Features in the 0.1-90 Hz range. The four levels of RFD were accurately classified using a support vector machine. When utilizing the Wideband Time-frequency Features, the accuracy was 83% ± 9% (mean ± SD). Meanwhile, when using the MRCP Statistical Characteristics, the accuracy was 78% ± 12% (mean ± SD). The analysis of the MRCP waveform revealed that it contains highly informative data on the planning, execution, completion, and duration of the isometric dorsiflexion task. The temporal analysis emphasized the importance of the $\delta$-band in translating to motor command, and this has promising implications for the field of neural engineering systems.

Authors with CRIS profile

Alessandro Del Vecchio Department Artificial Intelligence in Biomedical Engineering (AIBE)

Involved external institutions

New York University Tandon School of Engineering US United States (USA) (US) Universidad de Zaragoza ES Spain (ES) Center for Devices and Radiological Health, Food and Drug Administration US United States (USA) (US) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) Albert-Ludwigs-Universität Freiburg DE Germany (DE) New York University (NYU) US United States (USA) (US)

How to cite

APA:

O'Keeffe, R., Shirazi, S.Y., Del Vecchio, A., Ibanez, J., Mrachacz-Kersting, N., Bighamian, R.,... Atashzar, S.F. (2024). Low-frequency Motor Cortex EEG Predicts Four Rates of Force Development. IEEE Transactions on Haptics, 1-12. https://doi.org/10.1109/TOH.2024.3428308

MLA:

O'Keeffe, Rory, et al. "Low-frequency Motor Cortex EEG Predicts Four Rates of Force Development." IEEE Transactions on Haptics (2024): 1-12.

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