Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

Abstract

Motor Imagery (MI) promotes motor learning in activities, like developing professional motor skills, sports gestures, and patient rehabilitation. However, up to 30% of users may not develop enough coordination skills after training sessions because of inter and intra-subject variability. Here, we develop a data-driven estimator, termed Deep Regression Network (DRN), which jointly extracts and performs the regression analysis in order to assess the efficiency of the individual brain networks in practicing MI tasks. The proposed double-stage estimator initially learns a pool of deep patterns, extracted from the input data, in order to feed a neural regression model, allowing for infering the distinctiveness between subject assemblies having similar variability. The results, which were obtained on real-world MI data, prove that the DRN estimator fosters pre-training neural desynchronization and initial training synchronization to predict the bi-class accuracy response, thus providing a better understanding of the Brain–Computer Interface inefficiency of subjects.

Image-Based Learning Using Gradient Class Activation Maps for Enhanced Physiological Interpretability of Motor Imagery Skills

“…Pre-training indicator: We estimate the pre-training prediction ability of the activation maps extracted from the pre-cue interval (τ 1 ) for anticipating the subject's accuracy produced by the D&W CNN classifier in distinguishing either MI class. Thus, we obtain an r-squared value of r2 = 0.36 comparable with the one reported in [61] implemented via a similar D&W Neural-Network regression, implying that the activation maps may help in pre-screening participants for the ability to learn regulation of brain activity.…”

“…However, the short-time window selected to encode the latency of brain responses must be adjusted to extract temporal EEG dynamics accurately [69]. For example, as [61] suggests, using shorter window values may increase the performance of poor-performing subjects.…”

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“…datasets that are also used by Lee et al (2020) and Velasquez-Martinez et al (2020). Merging public datasets with similar BCI paradigms could be a cost-effective way of examining the relationships between and within studies even further.…”

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