Online extraction and single trial analysis of regions contributing to erroneous feedback detection

Abstract

Understanding how the brain processes errors is an essential and active field of neuroscience. Real time extraction and analysis of error signals provide an innovative method of assessing how individuals perceive ongoing interactions without recourse to overt behaviour. This area of research is critical in modern Brain-Computer Interface (BCI) design, but may also open fruitful perspectives in cognitive neuroscience research. In this context, we sought to determine whether we can extract discriminatory error-related activity in the source space, online, and on a trial by trial basis from electroencephalography data recorded during motor imagery. Using a data driven approach, based on interpretable inverse solution algorithms, we assessed the extent to which automatically extracted error-related activity was physiologically and functionally interpretable according to performance monitoring literature. The applicability of inverse solution based methods for automatically extracting error signals, in the presence of noise generated by motor imagery, was validated by simulation. Representative regions of interest, outlining the primary generators contributing to classification, were found to correspond closely to networks involved in error detection and performance monitoring. We observed discriminative activity in non-frontal areas, demonstrating that areas outside of the medial frontal cortex can contribute to the classification of error feedback activity.

Single-Trial Classification of Error-Related Potentials in People with Motor Disabilities: A Study in Cerebral Palsy, Stroke, and Amputees

“…The findings agree with several other studies that have reported the midline electrodes (especially FCz and Cz) to contain the highest error-related activity [24,[28][29][30][31]34,35,39,40], but also Parietal areas [24,[26][27][28][31][32][33]35] and the Occipital cortex have been associated with error-related activity [27,28,33,35]. It has been reported that the ErrP has the highest amplitudes around the midline channels but is still visible in the channels in the periphery furthest away from the midline, with a smaller amplitude though [27,[33][34][35].…”

“…ErrPs have generally been detected using temporal waveform features of a bandpass filtered epoch (~0-1 s after the feedback of the outcome) from electrodes on the scalp in the proximity of the anterior cingulate cortex amongst other neural generators (roughly around FCz according to the 10-20 EEG system) [9,10]. ErrPs can be detected from a single or few electrodes around FCz [24,25], but studies have reported that additional discriminative information can be obtained from using more electrodes covering other parts of the brain [24,[26][27][28][29][30][31][32][33][34][35]. The aim of this study was twofold; first it was investigated whether ErrPs could be detected in individuals with motor disabilities after cerebral palsy, an amputation, or stroke in offline analysis, and secondly, how much discriminative information different brain regions bring to the detection of ErrPs.…”

Evidence for a general performance‐monitoring system in the human brain

“…Neural signals following the incorrect operation of a BCI system have been reported earlier, mainly in the context of using such responses as an additional control or learning signal for optimizing BCI performance (Buttfield, Ferrez, & Millán, 2006). A recent study demonstrated that neural sources contributing to brain responses to erroneous BCI performance indeed largely overlap with the putative sources of the FRN (Dyson, Thomas, Casini, & Burle, 2015). In addition, our cross‐condition generalization results indicate that these BCI ‐ related error responses and ERN share at least some of the underlying neural generators.…”

Single trial detection of error-related potentials in brain–machine interfaces: a survey and comparison of methods

“…These results suggest that error related brain activity is typically not bounded in the frontal area. Instead of using a single or few electrodes around FCz, additional discriminative information can be obtained by using more electrodes spatially distributed over other parts of the brain, thus contributing to the classification of ErrPs [64,118]. Testing all possible combinations of steps of a classification pipeline would lead to a very high number of combinations.…”