@article{reichert_p63._2018,
	title = {P63. {Detection} of error potentials from {EEG} and {MEG} recordings and its value for {BMI} control},
	volume = {129},
	issn = {1388-2457},
	url = {http://www.sciencedirect.com/science/article/pii/S1388245718310083},
	doi = {https://doi.org/10.1016/j.clinph.2018.04.698},
	abstract = {Objective Brain-Machine Interfaces (BMIs) can help to regain communication and mobility in severely disabled persons. Especially spelling devices, rehabilitation of stroke patients and prosthesis control are fields of application. However, noninvasive BMIs, commonly using electroencephalography (EEG), suffer from poor signal quality, resulting in erroneous commands. In order to detect such erroneous commands, error potentials (ErrPs) generated in the brain after a user perceived a negative feedback can be decoded. The aim of this study was to investigate how accurate the presence of ErrPs can be detected from simultaneously recorded EEG and magnetoencephalography (MEG). Methods In a BMI experiment involving 19 participants, the selection of a covertly attended object was decoded from EEG/MEG and presented as feedback (Reichert et al., 2017). To facilitate investigation of ErrPs, we artificially presented negative feedback to achieve at least 40\% incorrect feedback. Using spatial filtering and SVM classification, we determined the probability of successfully detecting an ErrP. While an accurate error detection permits a reduction of errors made by the covert attention detector (i.e. rejection of potentially erroneous commands), the error rate of the ErrP classification inevitably also introduces accidental rejection of correct commands. In order to evaluate the potential benefit of ErrP detection in a BMI, we define a probability measure that takes into account errors of both the covert attention detector and the error detector. Results The components extracted by the data-driven spatial filter showed a positive deflection between 200 and 500 ms after feedback presentation, mainly driving the ErrP decoding. The correctness of perceived feedback could be decoded reliably (EEG: 71.9\% SE: 1.5\%; MEG: 72.7\%, SE: 1.2\%). However, the actual BMI revealed higher accuracies (EEG: 87.9\%, SE: 2.2\%; MEG: 95.8\%, SE: 1.0\%) compared to the ErrP detector. Thus, when applying ErrP detection, the number of erroneous selections was reduced but concurrently an even higher number of correct selections was rejected, which significantly reduced the information transfer rate. Probability theory suggests that ErrP detection only is advantageous if error detection rates exceed the accuracy of the feedback generating BMI itself. Conclusions Our results indicate that EEG and MEG are comparably suitable to detect the perception of erroneous feedback from brain activity recordings. The achieved prediction rate is in accordance with other approaches reported in the literature using EEG. However, those prediction rates only are advantageous, if the performance of the BMI is lower than that of the ErrP detector. Thus, highly accurate detection of errors would be required to efficiently correct errors made by a BMI.},
	number = {8},
	journal = {Clinical Neurophysiology},
	author = {Reichert, Christoph and Heinze, Nicolai and Pfeiffer, Tim and Dürschmid, Stefan and Hinrichs, Hermann},
	year = {2018},
	pages = {e93}
}