Microneurography and microstimulation of single tactile afferents in the human hand.
Biological and bionic hands: Natural neural coding and artificial perception
Neuroscience of touch and its relevance to prosthetics.
Tactile Mechanics Could Impact the Design of Prosthetics
Wearable haptics and role of touch in telepresence
Development of a proprioceptive BCI: Confronting the challenge of mimicking a complex limb-state representation in somatosensory cortex
Relative to the sense of touch, proprioception remains quite poorly understood. Furthermore, efforts to replace proprioception via peripheral or central stimulation lag well behind those of touch. Our conscious sense of proprioception is mostly focused on where are hands are with respect to our body. This is also the basis of the classic model of how single neurons in somatosensory cortex represent limb state. We used this model to design experiments testing intracortical microstimulation (ICMS) as a means to convey a sense of limb movement to monkeys. While the experiment worked well in one monkey, we were subsequently unable to replicate it.
This limited success has led us to re-evaluate the simple hand-based model in somatosensory cortical area 2, and in the cuneate nucleus (CN). Prior to our recent work, single neurons had never been recorded from CN in an awake animal, largely due to its inaccessible location in the base of the dorsal brainstem. In our recording, we could drive CN neurons strongly by muscle vibration (which activates muscle spindles), but only rarely by more than one muscle. We were able to predict CN firing rates more accurately by changes in muscle length than by the kinematics of arm movements. Area 2 neurons, in contrast, receive convergent input not only from multiple muscles, but also from tactile receptors, yielding rather complex receptive fields. The well localized, punctate activation in tactile regins of cortex produced by simple skin indentation can be mimicked relatively easily by ICMS. This is not the case in area 2, where even very simple limb movements cause widespread activation, and there is no evidence of an organized map of limb movement direction. We have begun to test more complex, multi-electrode stimulation paradigms with the hope that they may be more readily interpretable as a source of artificial afferent input than is single-electrode ICMS.
Artificial touch in brain-computer interfaces
Over the past decade, several groups have implanted microelectrode arrays into the sensorimotor cortex of paralyzed individuals. At the University of Pittsburgh, we have worked with two volunteers and have shown that recording and decoding the activity of a few hundred neurons in motor cortex enables a person to control a prosthetic arm with up to 10 degrees-of-freedom continuously and simultaneously. However, we know that the somatosensory system is essential to regulate ongoing movement and enable controlled interactions with the environment. Five years ago, we implanted electrodes into area 1, a tactile region of somatosensory cortex, to try and restore artificial touch and create a bidirectional brain-computer interface. In this talk I will describe how cutaneous sensations can be restored through microstimulation of the somatosensory cortex. I will focus on what they feel like, how stable they can be, how these experiments can uncover basic organizational principles of the somatosensory cortex and how these tactile percepts can substantially improve task performance. Our ultimate goal is to understand the structure and function of the sensorimotor cortex and use biomimetic design principles to restore dexterous hand and arm movements, complete with the appropriate sensory experiences, to people who have lost their limbs or are unable to use them because of injury or disease.
Commercialization of Nerve-Computer interfaces
Human-machine (interaction,interfaces): it isn’t about machine learning
After introducing and motivating the talk, I’ll define my personal ideal human-machine interface for the disabled, revolving around the concept of natural control and sensori-motor schemes, in the constructivist sense. Optimising the interaction between a user and a machine is not about machine learning, although we cannot do without it, nor it has to do with big data. Rather, it is a matter of collecting good data from the user and/or pushing the user to produce good data, monitoring and exploiting reciprocal adaptation and building appropriate interaction strategies. I will then sketch the current achievements of natural control via adaptive biological HMIs, going into some detail about how to model the relationship between biological signals, the user’s intent to move and control signals for a robotic artefact (in teleoperation, prosthetics, virtual reality). Lastly, I’ll throw some open question and issues to the audience, hoping to stimulate an articulated discussion.
Early Stage Researchers Presentations
During the event our PhD students will present the work carried out so far.
Soft Skills 101
How we communicate our information it is as important as the content. Together we’ll reflect on how to produce high impact scientific presentation, from the storyboard to the speech. We’ll discover how brain works to understand the deep meaning of our communication strategies. We’ll also reflect on how to be present and the mechanism of human attention. Virtual workshop will be supported by video and group discussion.
