Abstract
This study is aimed at the detection of single-trial feedback, perceived as erroneous by the user, using a transferable classification system while conducting a motor imagery brain–computer interfacing (BCI) task. The feedback received by the users are relayed from a functional electrical stimulation (FES) device and hence are somato-sensory in nature. The BCI system designed for this study activates an electrical stimulator placed on the left hand, right hand, left foot, and right foot of the user. Trials containing erroneous feedback can be detected from the neural signals in form of the error related potential (ErrP). The inclusion of neuro-feedback during the experiments indicated the possibility that ErrP signals can be evoked when the participant perceives an error from the feedback. Hence, to detect such feedback using ErrP, a transferable (offline) decoder based on optimal transport theory is introduced herein. The offline system detects single-trial erroneous trials from the feedback period of an online neuro-feedback BCI system. The results of the FES-based feedback BCI system were compared to a similar visual-based (VIS) feedback system. Using our framework, the error detector systems for both the FES and VIS feedback paradigms achieved an F1-score of 92.66% and 83.10%, respectively, and are significantly superior to a comparative system where an optimal transport was not used. It is expected that this form of transferable and automated error detection system compounded with a motor imagery system will augment the performance of a BCI and provide a better BCI-based neuro-rehabilitation protocol that has an error control mechanism embedded into it.
Highlights
Group were correct for 66% (Standard deviation = 8.792) of all trials, with participant VIS04 achieving an accuracy of 78.125%, whereas the eight participants in the functional electrical stimulation (FES) group were correct for 74.32% of all tasks (Standard deviation = 6.553) with participant FES06 achieving the highest accuracy of 86.458%
When the participants observed or sensed an incorrect feedback, did they evoke some form of error related potential (ErrP) during the feedback period, and if so, did the FES feedback have an influence in a manner similar to the motor learning?
We found that our ErrP decoder performed significantly better than a similar decoder but without using optimal transport theory for the transfer learning
Summary
Brain–computer interfaces (BCIs) have led to numerous advances in neuro-rehabilitation by providing a communication and control channel that bypasses the muscular activation of the limbs and relies more on the intention of the users as decoded from their neural activities.This technology was initially conceived for the benefit of patients with neural disorders, such as post-stroke effects, amyotrophic lateral sclerosis, spinal injuries, and physical disabilities [1,2], but as research has progressed in this area so has the potential applications of this technology in communications [3], automation [4], the military [5], and gaming [6].Electroencephalography (EEG) is the most commonly used modality for the recording of neural signals [7,8]. Brain–computer interfaces (BCIs) have led to numerous advances in neuro-rehabilitation by providing a communication and control channel that bypasses the muscular activation of the limbs and relies more on the intention of the users as decoded from their neural activities. This technology was initially conceived for the benefit of patients with neural disorders, such as post-stroke effects, amyotrophic lateral sclerosis, spinal injuries, and physical disabilities [1,2], but as research has progressed in this area so has the potential applications of this technology in communications [3], automation [4], the military [5], and gaming [6]. We aim to recover a transport plan between the probability distribution of the source domain P(Fs )
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