Abstract
Objective. Error-related potentials (ErrP) are generated in the brain when humans perceive errors. These ErrP signals can be used to classify actions as erroneous or non-erroneous, using single-trial electroencephalography (EEG). A small number of studies have demonstrated the feasibility of using ErrP detection as feedback for reinforcement-learning-based brain-computer interfaces (BCI), confirming the possibility of developing more autonomous BCI. These systems could be made more efficient with specific information about the type of error that occurred. A few studies differentiated the ErrP of different errors from each other, based on direction or severity. However, errors cannot always be categorised in these ways. We aimed to investigate the feasibility of differentiating very similar error conditions from each other, in the absence of previously explored metrics. Approach. In this study, we used two data sets with 25 and 14 participants to investigate the differences between errors. The two error conditions in each task were similar in terms of severity, direction and visual processing. The only notable differences between them were the varying cognitive processes involved in perceiving the errors, and differing contexts in which the errors occurred. We used a linear classifier with a small feature set to differentiate the errors on a single-trial basis. Main results. For both data sets, we observed neurophysiological distinctions between the ErrPs related to each error type. We found further distinctions between age groups. Furthermore, we achieved statistically significant single-trial classification rates for most participants included in the classification phase, with mean overall accuracy of 65.2% and 65.6% for the two tasks. Significance. As a proof of concept our results showed that it is feasible, using single-trial EEG, to classify these similar error types against each other. This study paves the way for more detailed and efficient learning in BCI, and thus for a more autonomous human-machine interaction.
Highlights
Introduction ce cri ptTowards error categorisation in BCI pte metrics
We achieved statistically significant single-trial classification rates for most participants included in the classification phase, with mean overall accuracy of
Stepwise Linear Discriminant Analysis Implementation In order to classify the data based on the most pertinent subset of the extracted features, stepwise linear discriminant analysis (SWLDA) was chosen as our classification approach, since it has previously been shown to perform well in feature selection and classification of EEG data [23, 24, 25]
Summary
If we are trying to navigate to a target location we could either take a wrong turn on the way, or we could reach the target but pass it These two errors could be of the same direction and magnitude, and indistinguishable by currently explored metrics, but knowing which one had occurred would provide useful information. It is important to consider whether there are significant neurophysiological distinctions in EEG signals between the brain’s responses to very similar error conditions, even in cases where metrics explored in existing literature are not available. To address this question, we evaluated data from two tasks. All of the errors considered by this experiment were response errors committed by humans who failed to withhold responses to “no-go”
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