Invariance and variability in interaction error-related potentials and their consequences for classification

Abu-Alqumsan, Mohammad; Kapeller, Christoph; Hintermüller, Christoph; Guger, Christoph; Peer, Angelika

doi:10.1088/1741-2552/aa8416

Cited by 17 publications

(18 citation statements)

References 83 publications

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“…It has been shown in several studies that ErrPs can be decoded (see [ 9 , 10 ] for a review about decoding ErrPs) and how the performance of the decoder can be optimized using different types of features [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ], classifiers [ 11 , 12 , 17 , 18 , 22 , 23 ], time windows [ 24 , 25 , 26 ], and channels [ 17 , 25 , 27 , 28 ]. Moreover, it has been shown that generalized ErrP detectors can be transferred across different tasks and types of ErrPs such as observation and interaction ErrPs, across participants, and time which eliminates the need for calibration (see, e.g., [ 11 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]). This transfer may be successful due to the stability of the ErrP over time as good test/retest reliability has been reported for evoking the ErrP [ 37 , 38 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network

Usama

Niazi

Dremstrup

et al. 2021

Sensors

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Error-related potentials (ErrPs) have been proposed as a means for improving brain–computer interface (BCI) performance by either correcting an incorrect action performed by the BCI or label data for continuous adaptation of the BCI to improve the performance. The latter approach could be relevant within stroke rehabilitation where BCI calibration time could be minimized by using a generalized classifier that is continuously being individualized throughout the rehabilitation session. This may be achieved if data are correctly labelled. Therefore, the aims of this study were: (1) classify single-trial ErrPs produced by individuals with stroke, (2) investigate test–retest reliability, and (3) compare different classifier calibration schemes with different classification methods (artificial neural network, ANN, and linear discriminant analysis, LDA) with waveform features as input for meaningful physiological interpretability. Twenty-five individuals with stroke operated a sham BCI on two separate days where they attempted to perform a movement after which they received feedback (error/correct) while continuous EEG was recorded. The EEG was divided into epochs: ErrPs and NonErrPs. The epochs were classified with a multi-layer perceptron ANN based on temporal features or the entire epoch. Additionally, the features were classified with shrinkage LDA. The features were waveforms of the ErrPs and NonErrPs from the sensorimotor cortex to improve the explainability and interpretation of the output of the classifiers. Three calibration schemes were tested: within-day, between-day, and across-participant. Using within-day calibration, 90% of the data were correctly classified with the entire epoch as input to the ANN; it decreased to 86% and 69% when using temporal features as input to ANN and LDA, respectively. There was poor test–retest reliability between the two days, and the other calibration schemes led to accuracies in the range of 63–72% with LDA performing the best. There was no association between the individuals’ impairment level and classification accuracies. The results show that ErrPs can be classified in individuals with stroke, but that user- and session-specific calibration is needed for optimal ErrP decoding with this approach. The use of ErrP/NonErrP waveform features makes it possible to have a physiological meaningful interpretation of the output of the classifiers. The results may have implications for labelling data continuously in BCIs for stroke rehabilitation and thus potentially improve the BCI performance.

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Section: Introductionmentioning

confidence: 99%

Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network

Usama

Niazi

Dremstrup

et al. 2021

Sensors

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“…However, this classifier showed poor between-day and across-participant transfer [16], which suggests that calibration data for the classifier need to be collected every time it is going to be used. This can be a time-consuming process and it should be considered whether other, more generic approaches should be used [43,[49][50][51], that can be individualized/adapted [17,52] to the user while an error monitoring/correction system is in use. Alternatively, it should be considered or whether a type of ErrP should be used in which multiple trials can be obtained more rapidly, such as observational ErrPs [53], or with higher error/correct ratios [25,54].…”

Section: Discussionmentioning

confidence: 99%

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

Usama

Niazi

Dremstrup

et al. 2022

Sensors

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Brain-computer interface performance may be reduced over time, but adapting the classifier could reduce this problem. Error-related potentials (ErrPs) could label data for continuous adaptation. However, this has scarcely been investigated in populations with severe motor impairments. The aim of this study was to detect ErrPs from single-trial EEG in offline analysis in participants with cerebral palsy, an amputation, or stroke, and determine how much discriminative information different brain regions hold. Ten participants with cerebral palsy, eight with an amputation, and 25 with a stroke attempted to perform 300–400 wrist and ankle movements while a sham BCI provided feedback on their performance for eliciting ErrPs. Pre-processed EEG epochs were inputted in a multi-layer perceptron artificial neural network. Each brain region was used as input individually (Frontal, Central, Temporal Right, Temporal Left, Parietal, and Occipital), the combination of the Central region with each of the adjacent regions, and all regions combined. The Frontal and Central regions were most important, and adding additional regions only improved performance slightly. The average classification accuracies were 84 ± 4%, 8 7± 4%, and 85 ± 3% for cerebral palsy, amputation, and stroke participants. In conclusion, ErrPs can be detected in participants with motor impairments; this may have implications for developing adaptive BCIs or automatic error correction.

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“…Simple discrete and controlled tasks enable a clear distinction between correct and erroneous events [5], [7], [11], [14]. This allows for a straightforward assessment of ErrP variations from the perspective of classification and visualization of grand average waveforms.…”

Section: Experiments Design For Analysismentioning

confidence: 99%

“…Nevertheless, common ErrP variations (i.e., augmentations in the observed ErrP signal structure) have been able to be detected, likely due to the underlying invariant features for these ErrPs [11]. For instance, observation are elicited in response to observed erroneous action [5]; feedback are elicited when feedback indicates an erroneous choice or action was made [4]; outcome are elicited when the goal of an action is not achieved [6], [12]; execution or interaction 1 are elicited when an action is input to an interface and the interface outputs an erroneous action instead [5], [6], [12].…”

Section: Introductionmentioning

confidence: 99%

“…A variety of causes are thought to be connected to these variations, such as the influence of cognitive processes elicited due to a particular task [11], [13], [14], changes in task complexity [15], repeated elicitation of the ErrP signal [5], and changes in the way a task is interacted with [16]. In more general terms, the notion of awareness, embodiment, and predictability seem to account for some of the noticeable differences in ErrP variations.…”

Section: Introductionmentioning

confidence: 99%

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Error-related Potential Variability: Exploring the Effects on Classification and Transferability

Poole¹,

Lee²

2023

Preprint

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Brain-Computer Interfaces (BCI) have allowed for direct communication from the brain to external applications for the automatic detection of cognitive processes such as error recognition. Error-related potentials (ErrPs) are a particular brain signal elicited when one commits or observes an erroneous event. However, due to the noisy properties of the brain and recording devices, ErrPs vary from instance to instance as they are combined with an assortment of other brain signals, biological noise, and external noise, making the classification of ErrPs a non-trivial problem. Recent works have revealed particular cognitive processes such as awareness, embodiment, and predictability that contribute to ErrP variations. In this paper, we explore the performance of classifier transferability when trained on different ErrP variation datasets generated by varying the levels of awareness and embodiment for a given task. In particular, we look at transference between observational and interactive ErrP categories when elicited by similar and differing tasks. Our empirical results provide an exploratory analysis into the ErrP transferability problem from a data perspective.1 Execution ErrPs are elicited with continuous actions while interaction ErrPs are elicited with discrete actions [6]

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Invariance and variability in interaction error-related potentials and their consequences for classification

Abstract: The obtained results may be used to explain across-study variability of ErrPs, as well as to define guidelines for approaches to the ErrP classifier transferability problem.

Cited by 17 publications

References 83 publications

Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network

Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network

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

Error-related Potential Variability: Exploring the Effects on Classification and Transferability

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