Beta synchrony in the cortico-basal ganglia network during regulation of force control on and off dopamine.

Beta power suppression in the basal ganglia is stronger during movements that require high force levels and high movement effort but it has been difficult to dissociate the two. We recorded scalp EEG and basal ganglia local field potentials in Parkinson's disease patients (11 STN, 7 GPi) ON and OFF dopaminergic medication while they performed a visually-guided force matching task using a pen on a force-sensitive graphics tablet. Force adjustments were accompanied by beta power suppression irrespective of whether the force was increased or reduced. Before the adjustment was completed, beta activity returned. High beta power was specifically associated with slowing of the force adjustment. ON medication, the peak force rate was faster and cortico-basal ganglia beta phase coupling was more readily modulated. In particular, phase decoupling was stronger during faster adjustments. The results suggest that beta power in the basal ganglia does not covary with force per se, but rather with a related factor, the absolute force rate, or a more general concept of movement effort. The results also highlight that beta activity reappears during stabilization of isometric contractions, and that dopamine-related suppression of cortico-basal ganglia beta coupling is linked to faster force adjustments.

Bio-inspired circular latent spaces to estimate objects' rotations.

This paper proposes a neural network model that estimates the rotation angle of unknown objects from RGB images using an approach inspired by biological neural circuits. The proposed model embeds the understanding of rotational transformations into its architecture, in a way inspired by how rotation is represented in the ellipsoid body of Drosophila. To effectively capture the cyclic nature of rotation, the network's latent space is structured in a circular manner. The rotation operator acts as a shift in the circular latent space's units, establishing a direct correspondence between shifts in the latent space and angular rotations of the object in the world space. Our model accurately estimates the difference in rotation between two views of an object, even for categories of objects that it has never seen before. In addition, our model outperforms three state-of-the-art convolutional networks commonly used as the backbone for vision-based models in robotics.

Force and touch make video games 'serious' for dexterity rehabilitation.

Computerized interfaces are able to represent 3D immersive simulations. Most of them make use of joystick, mouse, gloves, or grasp pressure transducers. Those have the drawback of 'filtering' the user interaction and/or de-locate the touch with respect to the visual stimulus. To overcome this we developed dexterity rehabilitation games on a novel touch interface that measures also force. The system allows dexterity training through 'direct' manipulation of virtual objects in 3D. Two dimensions via the touch screen, the third by the force channel. Tactile feedback is provided with a vibration device mounted on the screen back.

Distributed cognition for collaboration between human drivers and self-driving cars.

This paper focuses on the collaboration between human drivers and intelligent vehicles. We propose a collaboration mechanism grounded on the concept of distributed cognition. With distributed cognition, intelligence does not lie just in the single entity but also in the interaction with the other cognitive components in a system. We apply this idea to vehicle intelligence, proposing a system distributed into two cognitive entities-the human and the autonomous agent-that together contribute to drive the vehicle. This account of vehicle intelligence differs from the mainstream research effort on highly autonomous cars. The proposed mechanism follows one of the paradigm derived from distributed cognition, the rider-horse metaphor: just like the rider communicates their intention to the horse through the reins, the human influences the agent using the pedals and the steering wheel. We use a driving simulator to demonstrate the collaboration in action, showing how the human can communicate and interact with the agent in various ways with safe outcomes.

Target-distractor synchrony affects performance in a novel motor task for studying action selection.

The study of action selection in humans can present challenges of task design since our actions are usually defined by many degrees of freedom and therefore occupy a large action-space. While saccadic eye-movement offers a more constrained paradigm for investigating action selection, the study of reach-and-grasp in upper limbs has often been defined by more complex scenarios, not easily interpretable in terms of such selection. Here we present a novel motor behaviour task which addresses this by limiting the action space to a single degree of freedom in which subjects have to track (using a stylus) a vertical coloured target line displayed on a tablet computer, whilst ignoring a similarly oriented distractor line in a different colour. We ran this task with 55 subjects and showed that, in agreement with previous studies, the presence of the distractor generally increases the movement latency and directional error rate. Further, we used two distractor conditions according to whether the distractor's location changes asynchronously or synchronously with the location of the target. We found that the asynchronous distractor yielded poorer performance than its synchronous counterpart, with significantly higher movement latencies and higher error rates. We interpret these results in an action selection framework with two actions (move left or right) and competing 'action requests' offered by the target and distractor. As such, the results provide insights into action selection performance in humans and supply data for directly constraining future computational models therein.

Neuro-physical rehabilitation by means of novel touch technologies.

In this work, we present the results of collaboration between an engineering department and a rehabilitation hospital in using innovative touch interfaces properly designed for both neurocognitive and physical rehabilitation. The novel touch interface also measures force, thereby enabling dexterity training through 'direct' manipulation of virtual objects in 3D. Two dimensions are recorded via touch screen, the third by the force channel. We believe that this tool could increase the degree of effectiveness of traditional rehabilitation treatments thanks to its capability to merge physical and cognitive rehabilitation. Furthermore, the exergames implemented allow an easy personalization of the exercise structure and difficulty level. The effectiveness of the FP technology compared with more traditional methods of rehabilitation is measured according to specific parameters observed in an experimental group in comparison with a control group.