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Session: Poster session II
Session starts: Thursday 24 January, 16:00

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Jinne Geelen (TU Delft)

Abstract:
The decoding abilities for brain-computer interface (BCI) systems should be improved to allow for daily use by patients in a home environment. One area of interest for decoding is the sensorimotor cortex. Studying the representation of hand and finger movement on the cortex provides insight into the relationship between biomechanics and neurophysiology. An analysis with the recently developed musculoskeletal model of the hand can support this research. The anatomical parameters of this model are based on the data from one cadaver study and it has not yet been validated for general use. The main research question is: Can the hand model describe muscle activity during individual finger movements for a general population? The intrinsic and the extrinsic hand muscle activity will be measured with surface electromyography (sEMG). Two distinct tasks will be performed both with the help of the finger perturbation robot (FPR). For the first tasks, the participant holds the finger handles of the FPR with the thumb and one of the other fingers in a pinching posture. A trajectory on the screen has to be followed by moving the finger handles. The depicted path consists of an increasing force level (ramp) followed by a constant force level. The second tasks consist of force and position perturbation trials again performed with the FPR. The participants will be asked to keep their position or force constant while a perturbation is applied with the FPR. Predictions of muscle contractions, based on the model, are compared with electromyography (EMG) measurements in the forearm and hand. Our results provide an indication of the required optimization steps to create a valid general hand model. The first improvement that we want to implement is the scaling of the model. The scaling will enhance the prediction of the muscle activity during individual finger movements in a general population.