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

Daniel J L L Pinheiro; Jean Faber; Silvestro Micera; Solaiman Shokur

doi:10.3389/fnbot.2023.1154427

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

Front Neurorobot. 2023 Jun 5:17:1154427. doi: 10.3389/fnbot.2023.1154427. eCollection 2023.

Authors

Daniel J L L Pinheiro^{1

2}, Jean Faber^{1

3}, Silvestro Micera^{2

4}, Solaiman Shokur²

Affiliations

¹ Division of Neuroscience, Department of Neurology and Neurosurgery, Neuroengineering and Neurocognition Laboratory, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil.
² Translational Neural Engineering Lab, Institute Neuro X, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland.
³ Neuroengineering Laboratory, Division of Biomedical Engineering, Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo, São José dos Campos, Brazil.
⁴ Department of Excellence in Robotics and AI, Institute of BioRobotics Interdisciplinary Health Center, Scuola Superiore Sant'Anna, Pisa, Italy.

Abstract

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 naïve 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.

Keywords: Neuroprosthetics; auricular muscle; human-machine interface; motor decoding; steering control.

Grants and funding

This project has been funded partly by the Bertarelli Foundation, by the NCCR Robotics, a National Centre of Competence in Research and funded by the Swiss National Science Foundation (grant number: 51NF40_185543), the CHRONOS Project funded by the Swiss National Science Foundation (grant number: 184847) and Coordenação de perfeiçoamento e Pessoal de Nível Superior - Brazil (CAPES) - Financial Code 001, and the CAPES PrInt initiative.