|Publication type:||Article in scientific journal|
|Type of review:||Peer review (publication)|
|Title:||Invariability of EEG error-related potentials during continuous feedback protocols elicited by erroneous actions at predicted or unpredicted states|
Millán, José del R.
|Published in:||Journal of Neural Engineering|
|Publisher / Ed. Institution:||IOP Publishing|
|Subjects:||Asynchronous decoding; Continuous feedback protocol; Error-related potential; Transfer learning|
|Subject (DDC):||006: Special computer methods|
|Abstract:||Objective: When humans perceive an erroneous action, an EEG error-related potential (ErrP) is elicited as a neural response. ErrPs have been largely investigated in discrete feedback protocols, where actions are executed at discrete steps, to enable seamless brain-computer interaction. However, there are only a few studies that investigate ErrPs in continuous feedback protocols. The objective of the present study is to better understand the differences between two types of ErrPs elicited during continuous feedback protocols, where errors may occur either at predicted or unpredicted states. We hypothesize that ErrPs of the unpredicted state is associated with longer latency as it requires higher cognitive workload to evaluate actions compared to the predicted states. Approach: Participants monitored the trajectory of an autonomous cursor that occasionally made erroneous actions on its way to the target in two conditions, namely, predicted or unpredicted states. After characterizing the ErrP waveform elicited by erroneous actions in the two conditions, we performed single-trial decoding of ErrPs in both synchronous (i.e. time-locked to the onset of the erroneous action) and asynchronous manner. Furthermore, we explored the possibility to transfer decoders built with data of one of the conditions to the other condition. Main results: As hypothesized, erroneous actions at unpredicted states gave rise to ErrPs with higher latency than erroneous actions at predicted states, a correlate of higher cognitive effort in the former condition. Moreover, ErrP decoders trained in a given condition successfully transferred to the other condition with a slight loss of classification performance. This was the case for synchronous as well as asynchronous ErrP decoding, showing the invariability of ErrPs across conditions. Significance: These results advance the characterization of ErrPs during continuous feedback protocols, enlarging the potential use of ErrPs during natural operation of brain-controlled devices as it is not necessary to have different decoders for each kind of erroneous conditions.|
|Fulltext version:||Published version|
|License (according to publishing contract):||Licence according to publishing contract|
|Departement:||School of Engineering|
|Organisational Unit:||Centre for Artificial Intelligence (CAI)|
|Appears in collections:||Publikationen School of Engineering|
Files in This Item:
There are no files associated with this item.
Show full item record
Iwane, F., Iturrate, I., Chavarriaga, R., & Millán, J. d. R. (2021). Invariability of EEG error-related potentials during continuous feedback protocols elicited by erroneous actions at predicted or unpredicted states. Journal of Neural Engineering, 18(4), 46044. https://doi.org/10.1088/1741-2552/abfa70
Iwane, F. et al. (2021) ‘Invariability of EEG error-related potentials during continuous feedback protocols elicited by erroneous actions at predicted or unpredicted states’, Journal of Neural Engineering, 18(4), p. 046044. Available at: https://doi.org/10.1088/1741-2552/abfa70.
F. Iwane, I. Iturrate, R. Chavarriaga, and J. d. R. Millán, “Invariability of EEG error-related potentials during continuous feedback protocols elicited by erroneous actions at predicted or unpredicted states,” Journal of Neural Engineering, vol. 18, no. 4, p. 046044, 2021, doi: 10.1088/1741-2552/abfa70.
Iwane, Fumiaki, et al. “Invariability of EEG Error-Related Potentials during Continuous Feedback Protocols Elicited by Erroneous Actions at Predicted or Unpredicted States.” Journal of Neural Engineering, vol. 18, no. 4, 2021, p. 46044, https://doi.org/10.1088/1741-2552/abfa70.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.