Biology subjects

Amorim, M.-A.

Publications and source records attributed to Amorim, M.-A..

3 recordsLinked to original sources

bioRxiv2024

EMG-to-torque models for exoskeleton assistance: a framework for the evaluation of in situ calibration

In the field of robotic exoskeleton control, it is critical to accurately predict the intention of the user. While surface electromyography (EMG) holds the potential for such precision, current limitations arise from the absence of robust EMG-to-torque model calibration procedures and a universally accepted model. This paper introduces a practical framework for calibrating and evaluating EMG-to-torque models, accompanied by a novel nonlinear model. The framework includes an in situ procedure that involves generating calibration trajectories and subsequently evaluating them using standardized criteria. A comprehensive assessment on a dataset with 17 participants, encompassing single-joint and multi-joint conditions, suggests that the novel model outperforms the others in terms of accuracy while conserving computational efficiency. This contribution introduces an efficient model and establishes a versatile framework for EMG-to-torque model calibration and evaluation, complemented by a dataset made available. This further lays the groundwork for future advancements in EMG-based exoskeleton control and human intent detection. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

neuroscience↗

bioRxiv2023

EEG anticipatory activity depends on sensory modality

Perceptual decision-making is a combination of sensory information and prior beliefs. In order to perform actions in a timely fashion, it is necessary to anticipate the timing at which events occur, but also what event is more likely than the other. While EEG signatures of anticipation have been identified it is less clear whether classifiers trained on informative (cued) trials generalize to uncued trials and whether such decoded templates predict trial-by-trial shifts in decision strategy (e.g., drift-rate or starting-point changes in a Diffusion Decision Model). This study aimed to determine whether human participants anticipated a visual or auditory stimulus at the single-trial level in both cued and uncued trials. We found that pre-stimulus brain activity contains information about the expected upcoming stimulus and that this information can be successfully extracted from single-trial brain activity. Behavioral analyses revealed a connection between correct anticipation and shifts in decision strategy, while also validating the classification of uncued trials. Importantly, the classification of uncued trials confirms that expectations build even in the absence of triggers. These findings highlight the presence of single-trial, stimulus-specific neural signatures of anticipation, offering new insights into trial-to-trial variability in decision-making and advancing our understanding of cognitive processes. HighlightsO_LIDistributed low-frequency pre-stimulus EEG encodes modality-specific expectations. C_LIO_LIAnticipatory neural states are evoked even in the absence of explicit cues. C_LIO_LIDecoded anticipation predict response speed and accuracy. C_LI

neuroscience↗

bioRxiv2022

nl-DDM: a non-linear drift-diffusion model accounting for the dynamics of single-trial perceptual decisions

The Drift-Diffusion Model (DDM) is widely accepted for two-alternative forced-choice decision paradigms thanks to its simple formalism and close fit to behavioral and neurophysiological data. However, this formalism presents strong limitations in capturing inter-trial dynamics at the single-trial level and endogenous influences. We propose a novel model, the non-linear Drift-Diffusion Model (nl-DDM), that addresses these issues by allowing the existence of several trajectories to the decision boundary. We show that the non-linear model performs better than the drift-diffusion model for an equivalent complexity. To give better intuition on the meaning of nl-DDM parameters, we compare the DDM and the nl-DDM through correlation analysis. This paper provides evidence of the functioning of our model as an extension of the DDM. Moreover, we show that the nl-DDM captures time effects better than the DDM. Our model paves the way toward more accurately analyzing across-trial variability for perceptual decisions and accounts for peri-stimulus influences.

neuroscience↗