OBJECTIVE: Do movements made with an intracortical BCI (iBCI) have the same movement time properties as able-bodied movements? Able-bodied movement times typically obey Fitts' law: [Formula: see text] (where MT is movement time, D is target distance, R is target radius, and [Formula: see text] are parameters). Fitts' law expresses two properties of natural movement that would be ideal for iBCIs to restore: (1) that movement times are insensitive to the absolute scale of the task (since movement time depends only on the ratio [Formula: see text]) and (2) that movements have a large dynamic range of accuracy (since movement time is logarithmically proportional to [Formula: see text]). APPROACH: Two participants in the BrainGate2 pilot clinical trial made cortically controlled cursor movements with a linear velocity decoder and acquired targets by dwelling on them. We investigated whether the movement times were well described by Fitts' law. MAIN RESULTS: We found that movement times were better described by the equation [Formula: see text], which captures how movement time increases sharply as the target radius becomes smaller, independently of distance. In contrast to able-bodied movements, the iBCI movements we studied had a low dynamic range of accuracy (absence of logarithmic proportionality) and were sensitive to the absolute scale of the task (small targets had long movement times regardless of the [Formula: see text] ratio). We argue that this relationship emerges due to noise in the decoder output whose magnitude is largely independent of the user's motor command (signal-independent noise). Signal-independent noise creates a baseline level of variability that cannot be decreased by trying to move slowly or hold still, making targets below a certain size very hard to acquire with a standard decoder. SIGNIFICANCE: The results give new insight into how iBCI movements currently differ from able-bodied movements and suggest that restoring a Fitts' law-like relationship to iBCI movements may require non-linear decoding strategies.
OBJECTIVE: Do movements made with an intracortical BCI (iBCI) have the same movement time properties as able-bodied movements? Able-bodied movement times typically obey Fitts' law: [Formula: see text] (where MT is movement time, D is target distance, R is target radius, and [Formula: see text] are parameters). Fitts' law expresses two properties of natural movement that would be ideal for iBCIs to restore: (1) that movement times are insensitive to the absolute scale of the task (since movement time depends only on the ratio [Formula: see text]) and (2) that movements have a large dynamic range of accuracy (since movement time is logarithmically proportional to [Formula: see text]). APPROACH: Two participants in the BrainGate2 pilot clinical trial made cortically controlled cursor movements with a linear velocity decoder and acquired targets by dwelling on them. We investigated whether the movement times were well described by Fitts' law. MAIN RESULTS: We found that movement times were better described by the equation [Formula: see text], which captures how movement time increases sharply as the target radius becomes smaller, independently of distance. In contrast to able-bodied movements, the iBCI movements we studied had a low dynamic range of accuracy (absence of logarithmic proportionality) and were sensitive to the absolute scale of the task (small targets had long movement times regardless of the [Formula: see text] ratio). We argue that this relationship emerges due to noise in the decoder output whose magnitude is largely independent of the user's motor command (signal-independent noise). Signal-independent noise creates a baseline level of variability that cannot be decreased by trying to move slowly or hold still, making targets below a certain size very hard to acquire with a standard decoder. SIGNIFICANCE: The results give new insight into how iBCI movements currently differ from able-bodied movements and suggest that restoring a Fitts' law-like relationship to iBCI movements may require non-linear decoding strategies.
Authors: Wasim Q Malik; Wilson Truccolo; Emery N Brown; Leigh R Hochberg Journal: IEEE Trans Neural Syst Rehabil Eng Date: 2010-11-15 Impact factor: 3.802
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Authors: D Young; F Willett; W D Memberg; B Murphy; B Walter; J Sweet; J Miller; L R Hochberg; R F Kirsch; A B Ajiboye Journal: J Neural Eng Date: 2018-04 Impact factor: 5.379
Authors: Francis R Willett; Daniel R Young; Brian A Murphy; William D Memberg; Christine H Blabe; Chethan Pandarinath; Sergey D Stavisky; Paymon Rezaii; Jad Saab; Benjamin L Walter; Jennifer A Sweet; Jonathan P Miller; Jaimie M Henderson; Krishna V Shenoy; John D Simeral; Beata Jarosiewicz; Leigh R Hochberg; Robert F Kirsch; A Bolu Ajiboye Journal: Sci Rep Date: 2019-06-20 Impact factor: 4.379