Solaiman Shokur

2019 - now : Senior Scientist, Translational neuroengineering Laboratory, EPFL(Geneva) Team leader: CHRONOS project, a multi-center European project that aims at developing the first chronically implanted prosthetic hand for transradial amputee patients with bidirectional communication capabilities. NCCR Robotics: Bi-directional control of supernumerary limbs.2014-2019: Research Coordinator, AASDAP (São Paulo, Brazil) Responsible for both the scientific production and the clinical protocol at the AASDAP neurorehabilitation laboratory. 2013-2014: Postdoctoral associate, Walk again project, Insituto Santos Dumond. (Natal, RN, Brazil)Head of the engineering team in charge of the system integration. 2010-2012: Visiting scientist, Nicolelis Lab (Duke University). Development and validation of a virtual-reality based brain-machine interface for rhesus monkeys. 2007 – 2010: Teaching assistant, Laboratory of Robotics Systems (EPFL)

Human-machine interface for two-dimensional steering control with the auricular muscles

Daniel Jose Lins Leal Pinheiro, Silvestro Micera, Solaiman Shokur

Human-machine interfaces (HMIs) can be used to decode a user's motor intention to control an external device. People that suffer from motor disabilities, such as spinal cord injury, can benefit from the uses of these interfaces. While many solutions can be found in this direction, there is still room for improvement both from a decoding, hardware, and subject-motor learning perspective. Here we show, in a series of experiments with non-disabled participants, a novel decoding and training paradigm allowing naive participants to use their auricular muscles (AM) to control two degrees of freedom with a virtual cursor. AMs are particularly interesting because they are vestigial muscles and are often preserved after neurological diseases. Our method relies on the use of surface electromyographic records and the use of contraction levels of both AMs to modulate the velocity and direction of a cursor in a two-dimensional paradigm. We used a locking mechanism to fix the current position of each axis separately to enable the user to stop the cursor at a certain location. A five-session training procedure (20-30 min per session) with a 2D center-out task was performed by five volunteers. All participants increased their success rate (Initial: 52.78 +/- 5.56%; Final: 72.22 +/- 6.67%; median +/- median absolute deviation) and their trajectory performances throughout the training. We implemented a dual task with visual distractors to assess the mental challenge of controlling while executing another task; our results suggest that the participants could perform the task in cognitively demanding conditions (success rate of 66.67 +/- 5.56%). Finally, using the Nasa Task Load Index questionnaire, we found that participants reported lower mental demand and effort in the last two sessions. To summarize, all subjects could learn to control the movement of a cursor with two degrees of freedom using their AM, with a low impact on the cognitive load. Our study is a first step in developing AM-based decoders for HMIs for people with motor disabilities, such as spinal cord injury.

FRONTIERS MEDIA SA2023

A modular strategy for next-generation upper-limb sensory-motor neuroprostheses

Silvestro Micera, Giuseppe Schiavone, Solaiman Shokur

Neuroprosthetics is a discipline that aims at restoring lost functions to people affected by a variety of neurological disorders or neuro-traumatic lesions. It combines the expertise of computer science and electrical, mechanical, and micro/nanotechnology with cellular, molecular, and systems neuroscience. Rapid breakthroughs in the field during the past decade have brought the hope that neuroprostheses can soon become a clinical reality, in particular-as we will detail in this review-for the restoration of hand functions. We argue that any neuroprosthesis relies on a set of hardware and algorithmic building elements that we call the neurotechnological modules (NTs) used for motor decoding, movement restoration, or sensory feedback. We will show how the modular approach is already present in current neuroprosthetic solutions and how we can further exploit it to imagine the next generation of neuroprosthetics for sensory-motor restoration.

ELSEVIER2021

Human-machine interface for two-dimensional steering control with the auricular muscles

Daniel Jose Lins Leal Pinheiro, Silvestro Micera, Solaiman Shokur

FRONTIERS MEDIA SA2023

Human-machine interface for two-dimensional steering control with the auricular muscles

Editorial: Getting Neuroprosthetics Out of the Lab: Improving the Human-Machine Interactions to Restore Sensory-Motor Functions

A modular strategy for next-generation upper-limb sensory-motor neuroprostheses

Editorial: Getting Neuroprosthetics Out of the Lab: Improving the Human-Machine Interactions to Restore Sensory-Motor Functions

A modular strategy for next-generation upper-limb sensory-motor neuroprostheses

Human-machine interface for two-dimensional steering control with the auricular muscles