Sensory-Motor Interactions and the Manipulation of Movement Error

Parmar, Pritesh N.; Huang, Felix C.; Patton, James L.

doi:10.1007/978-3-031-08995-4_11

Cited by 2 publications

(4 citation statements)

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“…In particular, we softened probabilities towards a uniform distribution. This form of feedback manipulation is closely related to previous uses of error augmentation, also referred to as error amplification [23], [24], [25]. As mentioned, this form of feedback has been shown to hasten learning and improve the quality of selfevaluation [18], [26] and increase retention of learned skills [27], [23].…”

Section: Introductionmentioning

confidence: 88%

“…This form of feedback manipulation is closely related to previous uses of error augmentation, also referred to as error amplification [23], [24], [25]. As mentioned, this form of feedback has been shown to hasten learning and improve the quality of selfevaluation [18], [26] and increase retention of learned skills [27], [23]. We therefore hypothesized that error amplification by softening probabilities would increase subsequent gesture classification performance by enhancing human skill learning.…”

Section: Introductionmentioning

confidence: 89%

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User Training With Error Augmentation for sEMG-Based Gesture Classification

Bicer,

Smedemark-Margulies,

Celik

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wristband configuration. sEMG data were streamed into a machine-learning algorithm that classified hand gestures in real-time. After an initial model calibration, participants were presented with one of three types of feedback during a human-learning stage: veridical feedback, in which predicted probabilities from the gesture classification algorithm were displayed without alteration; modified feedback, in which we applied a hidden augmentation of error to these probabilities; and no feedback. User performance was then evaluated in a series of minigames, in which subjects were required to use eight gestures to manipulate their game avatar to complete a task. Experimental results indicated that relative to the baseline, the modified feedback condition led to significantly improved accuracy. Class separation also improved, though this trend was not significant. These findings suggest that real-time feedback in a gamified user interface with manipulation of feedback may enable intuitive, rapid, and accurate task acquisition for sEMG-based gesture recognition applications.

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Section: Introductionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 89%

User Training With Error Augmentation for sEMG-Based Gesture Classification

Bicer,

Smedemark-Margulies,

Celik

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

show abstract

“…In particular, we softened probabilities towards a uniform distribution. This form of feedback manipulation is closely related to previous uses of error augmentation, also referred to as error amplification [296]- [298]. As mentioned, this form of feedback has been shown to hasten learning and improve the quality of self-evaluation [291], [299] and increase retention of learned skills [296], [300].…”

Section: Introductionmentioning

confidence: 88%

“…This form of feedback manipulation is closely related to previous uses of error augmentation, also referred to as error amplification [296]- [298]. As mentioned, this form of feedback has been shown to hasten learning and improve the quality of self-evaluation [291], [299] and increase retention of learned skills [296], [300]. We therefore hypothesized that error amplification by softening probabilities would increase subsequent gesture classification performance by enhancing human skill learning.…”

Section: Introductionmentioning

confidence: 88%

Reducing calibration effort for brain-computer interfaces

Smedemark-Margulies

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This thesis represents the culmination of my PhD journey. To begin, I want to thank my parents, siblings, extended family, and most of all, my wife Krista, for endless support and encouragement through this marathon process. I feel extremely grateful to have such wonderful people in my life.During my PhD, I have had the great privilege of working with a large set of talented researchers across several institutions. As you may know, conducting an extended academic research project involves many challenges. Timelines are often tight, experiments that 'should' succeed often fail, and Reviewer #2 usually hasn't had enough coffee. Despite these obstacles, I have been fortunate to benefit from and learn from the patience, enthusiasm, and creative ideas of my mentors and co-authors.First, I want to thank my advisors Deniz Erdo gmus ¸, Robin Walters, and Jan-Willem van de Meent. Without their support, none of the work in this thesis would have been possible. This includes mundane elements like funding and organization, and more exciting elements such as iterating on research questions and experiments. I have learned a tremendous amount from each of them, and having the benefit of advice and mentorship from three fantastic researchers with different backgrounds and expertise has been an important advantage for me.Next, I want to thank my co-authors 1 from research described in this thesis and from other projects. People are listed here according to their affiliation when we worked together.

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Sensory-Motor Interactions and the Manipulation of Movement Error

Cited by 2 publications

References 138 publications

User Training With Error Augmentation for sEMG-Based Gesture Classification

User Training With Error Augmentation for sEMG-Based Gesture Classification

Reducing calibration effort for brain-computer interfaces

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