Neural Responses to Altered Visual Feedback in Computerized Interfaces Driven by Force- or Motion-Control

Sophie Dewil, Mingxiao Liu, Sean Sanford, Raviraj Nataraj

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Computerized interfaces, like virtual reality, are increasingly used to improve engagement in movement training tasks like in physical therapy. In this study, we examined how alterations in interface feedback can impact neural responses that affect motor learning. Neurotypical persons participated in simple motor training tasks (e.g., grasping, reaching) while visual performance feedback was systematically altered. We stratified neural response results across primarily force (grasp) and motion (reach) components for more fundamental analysis as complex movements typically require concurrent modulation of force and motion. Feedback alterations included adding noise to or automating the visual feedback in ways previously established to impair the sense of agency and performance. We analyzed the neural responses based on electroencephalography (EEG) recordings in two ways. First, we assessed EEG power changes in the alpha- and beta-band across the brain and in Brodmann area 6, given its role in planning and coordinating complex movements. Second, we did a preliminary analysis with neural networks to suggest how predictable motor errors were from neural response data. We observed significant increases in EEG power with noise-altered visual feedback in the force task, suggesting greater sensitivity of force tasks to training feedback. However, motion and force errors were both highly predictable (< 0.1% max target value) from neural response data, suggesting the potential for artificial intelligence tools to predict errors reliably and alter training feedback from computerized interfaces. In conclusion, computerized feedback may be optimized to leverage neural responses that accelerate movement outcomes.

Original languageEnglish
Title of host publicationAI Technologies and Virtual Reality - Proceedings of 7th International Conference on Artificial Intelligence and Virtual Reality AIVR 2023
EditorsKazumi Nakamatsu, Srikanta Patnaik, Roumen Kountchev
Pages299-312
Number of pages14
DOIs
StatePublished - 2024
Event7th International Conference on Artificial Intelligence and Virtual Reality, AIVR 2023 - Kumamoto, Japan
Duration: 21 Jul 202323 Jul 2023

Publication series

NameSmart Innovation, Systems and Technologies
Volume382
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference7th International Conference on Artificial Intelligence and Virtual Reality, AIVR 2023
Country/TerritoryJapan
CityKumamoto
Period21/07/2323/07/23

Keywords

  • Artificial intelligence
  • Motor activity
  • Rehabilitation
  • Virtual reality
  • Visual feedback

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