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) <italic>MRCP Morphological Characteristics</italic> in the <inline-formula><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula>-band, such as timing and amplitude; (ii) <italic>MRCP Statistical Characteristics</italic> in the <inline-formula><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula>-band, such as standard deviation, mean, and kurtosis; and (iii) <italic>Wideband Time-frequency Features</italic> 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 <inline-formula><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula>-band in translating to motor command, and this has promising implications for the field of neural engineering systems.
Original language | English (US) |
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Pages (from-to) | 1-12 |
Number of pages | 12 |
Journal | IEEE Transactions on Haptics |
DOIs | |
State | Accepted/In press - 2024 |
Keywords
- Accuracy
- Ankle
- BCI
- Electrodes
- Electroencephalography
- Force
- Force Decoding
- Motors
- NeuroHaptics
- Task analysis
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Science Applications