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. 2008 Feb;16(1):15-23.
doi: 10.1109/TNSRE.2007.916269.

Decoding individuated finger movements using volume-constrained neuronal ensembles in the M1 hand area

Soumyadipta Acharya  1 , Francesco Tenore, Vikram Aggarwal, Ralph Etienne-Cummings, Marc H Schieber, Nitish V Thakor
Affiliations

Decoding individuated finger movements using volume-constrained neuronal ensembles in the M1 hand area

Soumyadipta Acharya et al. IEEE Trans Neural Syst Rehabil Eng. 2008 Feb.

Abstract

Individuated finger and wrist movements can be decoded using random subpopulations of neurons that are widely distributed in the primary motor (M1) hand area. This work investigates 1) whether it is possible to decode dexterous finger movements using spatially-constrained volumes of neurons as typically recorded from a microelectrode array; and 2) whether decoding accuracy differs due to the configuration or location of the array within the M1 hand area. Single-unit activities were sequentially recorded from task-related neurons in two rhesus monkeys as they performed individuated movements of the fingers and the wrist. Simultaneous neuronal ensembles were simulated by constraining these activities to the recording field dimensions of conventional microelectrode array architectures. Artificial neural network (ANN) based filters were able to decode individuated finger movements with greater than 90% accuracy for the majority of movement types, using as few as 20 neurons from these ensemble activities. Furthermore, for the large majority of cases there were no significant differences (p < 0.01) in decoding accuracy as a function of the location of the recording volume. The results suggest that a brain-machine interface (BMI) for dexterous control of individuated fingers and the wrist can be implemented using microelectrode arrays placed broadly in the M1 hand area.

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Figures

Fig. 1
Fig. 1
Neural recording from the M1 hand area. (a) Approximate location of the M1 hand area, with respect to the central sulcus (dashed line). Actual locations of neurons recorded from (b) monkey C and (c) monkey K, where the depth is with respect to the cortical surface. Recording volumes for the different electrode arrays are shown in the bottom from left to right: (d) Caltech probe, (applied to monkey C), footprint size: 0.5×3×0.5 mm3; (e) Michigan array (monkey C), footprint size: 1×3×1 mm3; (f) modified Utah array, (monkey C), footprint size: 4×4×0.25 mm3; (g) Caltech array (monkey K), footprint size: 3×3×2 mm3.
Fig. 2
Fig. 2
Schematic representations of different microelectrode array architectures used for intra-cortical recording that have been developed at (a) University of Utah (Utah Array), (b) California Institute of Technology, and (c) University of Michigan. A modified version of the Utah Electrode Array with longer shanks is also shown (d). The insets show locations of the recording sites for each of the array architectures that have more than one recording site per shank.
Fig. 3
Fig. 3
Overall asynchronous decoding accuracy for both monkeys and different voxel configurations. Each subplot depicts accuracies from five distinct voxel placements within the M1 hand area. (a) monkey C: Caltech probe; (b) monkey C: Michigan array (c) monkey C: Modified Utah array probe (d) monkey K: Caltech array.
Fig. 4
Fig. 4
Overall decoding accuracy for the different voxel configurations, as a function of the movement type and the number of neurons. For each voxel configuration, results are shown for five distinct voxel placements within the M1 hand area. Significant pair-wise differences in decoding accuracy due to voxel placement are depicted with an asterisk (a) monkey C: Caltech probe (b) monkey C: Michigan array (c) monkey C: Modified Utah array (d) monkey K: Caltech array.
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