PMID-17694874[0] The muscle activation method: an approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm.
- First BMI that successfully predicted interactions between the arm and a force field.
- Previous BMIs are used to decode position, velocity, and acceleration, as each of these has been shown to be encoded in the motor cortex
- Hyun talks about stiff tasks, like writing on paper vs . pliant tasks, like handling an egg; both require a mixture of force and position control.
- Georgopoulous = velocity; Evarts = Force; Kalaska movement and force in an isometric task; [17-19] = joint dependence;
- Todorov "On the role of primary motor cortex in arm movement control" [20] = muscle activation, which reproduces Georgouplous and Schwartz ("Direct cortical representation of drawing".
- Kakei [19] "Muscle movement representations in the primary motor cortex" and Li [23] [1] show neurons correlate with both muscle activations and direction.
- Argues that MAM is the best way to extract impedance information -- direct readout of impedance requires a supervised BMI to be trained on data where impedance is explicitly measured.
- linear filter does not generalize to different force fields.
- algorithm activity highly correlated with recorded EMG.
- another interesting ref: [26] "Are complex control signals required for human arm movements?"
____References____
[0] Kim HK, Carmena JM, Biggs SJ, Hanson TL, Nicolelis MA, Srinivasan MA, The muscle activation method: an approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm.IEEE Trans Biomed Eng 54:8, 1520-9 (2007 Aug) |
[1] Li CS, Padoa-Schioppa C, Bizzi E, Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field.Neuron 30:2, 593-607 (2001 May) |
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