PT  - JOURNAL ARTICLE
AU  - Stefan Schaffelhofer
AU  - Andres Agudelo-Toro
AU  - Hansjörg Scherberger
TI  - Decoding a Wide Range of Hand Configurations from Macaque Motor, Premotor, and Parietal Cortices
AID  - 10.1523/JNEUROSCI.3594-14.2015
DP  - 2015 Jan 21
TA  - The Journal of Neuroscience
PG  - 1068--1081
VI  - 35
IP  - 3
4099  - http://www.jneurosci.org/content/35/3/1068.short
4100  - http://www.jneurosci.org/content/35/3/1068.full
SO  - J. Neurosci.2015 Jan 21; 35
AB  - Despite recent advances in decoding cortical activity for motor control, the development of hand prosthetics remains a major challenge. To reduce the complexity of such applications, higher cortical areas that also represent motor plans rather than just the individual movements might be advantageous. We investigated the decoding of many grip types using spiking activity from the anterior intraparietal (AIP), ventral premotor (F5), and primary motor (M1) cortices. Two rhesus monkeys were trained to grasp 50 objects in a delayed task while hand kinematics and spiking activity from six implanted electrode arrays (total of 192 electrodes) were recorded. Offline, we determined 20 grip types from the kinematic data and decoded these hand configurations and the grasped objects with a simple Bayesian classifier. When decoding from AIP, F5, and M1 combined, the mean accuracy was 50% (using planning activity) and 62% (during motor execution) for predicting the 50 objects (chance level, 2%) and substantially larger when predicting the 20 grip types (planning, 74%; execution, 86%; chance level, 5%). When decoding from individual arrays, objects and grip types could be predicted well during movement planning from AIP (medial array) and F5 (lateral array), whereas M1 predictions were poor. In contrast, predictions during movement execution were best from M1, whereas F5 performed only slightly worse. These results demonstrate for the first time that a large number of grip types can be decoded from higher cortical areas during movement preparation and execution, which could be relevant for future neuroprosthetic devices that decode motor plans.