A Review of Error-Related Potential-Based Brain–Computer Interfaces for Motor Impaired People

Abstract

Regaining the lost functionality of limbs is the top priority for people with motor skills impairment as it directly affects their ability to execute activities of daily living and hence, worsens their quality of life. In the last two decades, a great deal of research has focused on error-related potential (ErrP) based brain-computer interfaces (BCIs). Many applications have been developed to assist motor-impaired people in their rehabilitation and among these are robots, spellers, gesture recognition systems, and braincontrolled wheelchairs. In this paper, we present a review of various ErrP based BCI that can potentially aid motor-disabled people in their rehabilitation and execution of their daily activities. First, we describe the ErrP phenomenon and its characteristics followed by a comprehensive application-driven discussion on ErrP based rehabilitation and assistive strategies for motor-impaired people, including studies conducted since the inception of ErrP to the current state-of-the-art applications. Lastly, we discuss the potential issues and challenges being faced by current state-of-the-art applications as well as important future pathways and research directions that might be adopted for advanced ErrP-BCIs used in clinical settings.

The influence of psychological and cognitive states on error-related negativity evoked during post-stroke rehabilitation movements

“…In training-ERN signal, an unaffected P100 and stronger N135 peaks occur; whereas in a typical-ERN signal, unaffected Ne and stronger Pe peaks occur, in response to greater awareness of error [ 34 – 36 ]. Notably, Kumar et al [ 7 ] observed a reverse polarity in the ERN signal associated with the training-ErrP signal in comparison to the polarity observed in typical-ERN signals in the literature [ 11 , 34 – 36 ], which supports the opposite behavior observed in the present study. This result suggests the attribution of P100 deflection of the training-ERN signal with Ne deflection seen in typical-ERN signals, as well as the attribution of N135 deflection of the training-ERN signal with the Pe deflection seen in typical-ERN signals [ 7 , 34 – 36 ].…”

“…The decrease in the amplitude of the N400 peak with an increase in awareness of error score can be attributed to the methodological differences in relation to the training-ERN signals. The observed relationship between the training-ERN signal and the typical-ERN signal can assist in understanding the neural mechanism behind the training-ERN signal and in comparing ERN signals of various task modalities [ 11 ].…”

“…After that, the continuous data were segmented into epochs ranging from 0 to 450 ms relative to the onset of Fig. 1 e. Only error trials were kept for further processing, as they represent the ERN signals [ 11 ]. Individual epoch means were subtracted from the respective epochs to remove any DC offset left.…”

“…The amount of time spent in actively performing the rehabilitation exercises is positively linked with the rate and amount of recovery from stroke disabilities [ 8 ]. As adaptive rehabilitation programs have shown to promote prolonged training sessions and engagement in the rehabilitation programs [ 9 , 10 ], a training-ErrP-led human-in-the-loop AAN robotic stroke rehabilitation program can provide a higher rate of recovery to stroke patients from their motor impairments in comparison to the existing state-of-the-art AAN robotic stroke rehabilitation programs and standard stroke rehabilitation care [ 4 , 7 , 11 ].…”

“…Moreover, a number of studies have also demonstrated the association between the effort in performing the rehabilitation exercises and the rate of recovery from motor impairment [ 23 , 24 ]. Hence, if parameters of the physical rehabilitation program, such as the exercise type, its difficulty level, and assistance level given, are modified not only based on the presence and absence of ERN signal associated with a training-ErrP signal (named training-ERN onwards), as proposed in [ 7 , 11 ], but also on patients’ engagement and effort level, a dynamically adaptive stroke rehabilitation system can be developed which will keep stroke patients engaged in the rehabilitation program. Furthermore, any change in the training-ERN signal due to the change in the state-of-mind of a participant can increase the inter-participant as well as inter-trial variability, which can reduce the detection rate of the training-ERN signal.…”