| dc.description.abstracteng | Neuroprosthetics aim to restore a paralyzed patient’s ability to interact with the environment, e.g., by enabling the patient to control a robot arm and hand with his or her brain activity by decoding movement intentions from motor related brain activity. Previously, activity from primary motor cortex has primarily been used to predict movements. However, it has been shown that area F5 in the ventral premotor cortex and the anterior intraparietal area (AIP) also carry information about grip type, wrist orientation, and hand shape during movement planning and execution.
In this thesis, we investigated whether AIP and F5, in addition to this categorical information, also encode continuous movements. We performed two different kinds of decodings: first, we investigated the possibility of decoding complete hand, wrist, and arm movements that were represented by 27 DOF from single and multiunit activity in primate areas M1, F5, and AIP in a comparative way. Second, we performed a detection of kinematic states, namely “resting” and “movement”, from the spiking activity in the same three cortical areas.
To our knowledge, this is the first study that combines and compares areas M1, F5, and AIP for prediction of versatile, continuous hand kinematics as well as detection of respective behavioural states. Simultaneous recordings of population activity from the three areas by multi-electrode arrays gave us the possibility to examine the differences between the areas and evaluate the information content with respect to hand and arm kinematics in these areas.
We found that continuous trajectories of 27 joint angles could be reconstructed accurately over time by using single and multiunit activity from M1, F5, or AIP. The highest performance was achieved when using M1 for decoding, followed by F5 and area AIP. The same order of decoding accuracy was also true for the detection of kinematic states. All three areas were able to predict both joint angles and kinematic states significantly better than chance level. Furthermore, performances were similar to or even higher than reported in previous studies.
When combining activity from two or more areas for decoding of 27 joint angles, no significant increase of decoding performance was achieved. Furthermore, the differences in decoding performance between the three areas did not primarily depend on the number of neurons available for decoding, but strongly reflected the type of information encoded in these areas.
For the decoding of joint angles, we found neural data from very short time bins preceding the kinematics to be suited best for decoding. However, the bin needed to be combined with an adequate time lag to find the optimal relation between brain activity and movement execution. These optimal gap lengths differed substantially between areas. Shorter gap lengths were ideal when decoding from M1, whereas optimal time gaps for F5 were longer than for M1, indicating that information about grasping is present earlier in F5 than in primary motor cortex. When decoding from area AIP, best decoding performance was obtained for gap lengths close to zero (with neural activity preceding kinematics) supporting the idea that AIP could represent a copy of the motor plan in F5 in order to compare it to the visual properties. Similar results were obtained for the decoding of kinematic states, however, optimal bin lengths were longer than when predicting 27 joint angles.
“Resting” state could be detected with higher accuracy than “movement”. In addition, there was a difference in decoding accuracy between “passive resting”, when the monkey was waiting to perform the next movement, and “active resting”, when the animal held its hand still while holding the object: “passive resting” was detected by the classifier with higher precision than “active resting”. Nevertheless, movement onset was on average detected very precisely and the time lag between true and predicted movement onset lay within the resolution accuracy of the data when decoding from M1 and F5. When AIP was used for classification, movement onsets were detected shortly after real movement beginnings.
In conclusion, we found that all three areas were suited for decoding of both high-dimensional kinematics and kinematic states. Our findings contribute to the development of more sophisticated neuroprosthetics that will operate in a natural and intuitive way. | de |