2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2015
DOI: 10.1109/embc.2015.7318560
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Decoding fast-paced error-related potentials in monitoring protocols

Abstract: Abstract-Error-related EEG potentials (ErrP) can be used for brain-machine interfacing (BMI). Decoding of these signals, indicating subject's perception of erroneous system decisions or actions can be used to correct these actions or to improve the overall interfacing system. Multiple studies have shown the feasibility of decoding these potentials in single-trial using different types of experimental protocols and feedback modalities. However, previously reported approaches are limited by the use of long inter… Show more

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Cited by 9 publications
(11 citation statements)
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“…Noticeably these signals, linked to cognitive monitoring processes, have also been reported to be rather stable across different recording days [11], and feedback characteristics [12], [9]. As a matter of fact, they are not strongly modulated by the stimulus presentation rate [13], although they may vary depending on factors such as the predictability of the stimulus [14] These studies typically compare the signal across different conditions without assessing the classification performance across different experimental conditions (i.e., generalisation across days or feedback presentation speeds). In these studies, both the pre-processing steps and the classifier parameters are specifically suited for a given experimental condition.…”
Section: Introductionmentioning
confidence: 94%
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“…Noticeably these signals, linked to cognitive monitoring processes, have also been reported to be rather stable across different recording days [11], and feedback characteristics [12], [9]. As a matter of fact, they are not strongly modulated by the stimulus presentation rate [13], although they may vary depending on factors such as the predictability of the stimulus [14] These studies typically compare the signal across different conditions without assessing the classification performance across different experimental conditions (i.e., generalisation across days or feedback presentation speeds). In these studies, both the pre-processing steps and the classifier parameters are specifically suited for a given experimental condition.…”
Section: Introductionmentioning
confidence: 94%
“…The second experiment follows the same approach where the user monitors the cursor movements. In this case the cursor can move in four directions within a 5x5 2D grid shown in Figure 1(b) [13]. Besides cursor movements, there was a fifth possible cursor action to denote that the system considered that it had reached the target, see Figure 1(c).…”
Section: A Experimental Protocolsmentioning
confidence: 99%
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