A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection
Open accessAbstract
Reliable detection of error from electroencephalography (EEG) signals as feedback while performing a discrete target selection task across sessions and subjects has a huge scope in real-time rehabilitative application of Brain-computer Interfacing (BCI). Error Related Potentials (ErrP) are EEG signals which occur when the participant observes an erroneous feedback from the system. ErrP holds significance in such closed-loop system, as BCI is prone to error and we need an effective method of systematic error detection as feedback for correction. In this paper, we have proposed a novel scheme for online detection of error feedback directly from the EEG signal in a transferable environment (i.e., across sessions and across subjects). For this purpose, we have used a P300-speller dataset available on a BCI competition website. The task involves the subject to select a letter of a word which is followed by a feedback period. The feedback period displays the letter selected and, if the selection is wrong, the subject perceives it by the generation of ErrP signal. Our proposed system is designed to detect ErrP present in the EEG from new independent datasets, not involved in its training. Thus, the decoder is trained using EEG features of 16 subjects for single-trial classification and tested on 10 independent subjects. The decoder designed for this task is an ensemble of linear discriminant analysis, quadratic discriminant analysis, and logistic regression classifier. The performance of the decoder is evaluated using accuracy, F1-score, and Area Under the Curve metric and the results obtained is 73.97, 83.53, and 73.18%, respectively.
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Calibration-free cross-subject ErrP classification has been demonstrated with generalized LDA, an xDAWN+RG pipeline with a support vector machine classifier, ensemble classifiers, and online PCA-based pipelines, though without inherent interpretability.
“Calibration-free cross-subject ErrP classification has been demonstrated with generalized LDA [16], an xDAWN+RG pipeline with a support vector machine classifier [17], ensemble classifiers [18], and online PCA-based pipelines [19], though without inherent interpretability.”
From ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification (2026) · cites this paper as [18]
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Method
Flagged first, then by citations
Research works printed but not linked: 3
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Sorted by citations
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“Finally, we evaluated all the considered methods’ performance using accuracy, F1-score, and area under the curve (AUC), offering a comprehensive assessment of their effectiveness [56].”
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“Some works already explored the development of generic classifiers for error-related potentials and other event-related potentials in the context of discrete tasks [7]–[11].”
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Usama, N., Niazi, I. K., Dremstrup, K., & Jochumsen, M. (2021) · Sensors · doi:10.3390/s21186274 · Cited by 10 · see citing works
“The accuracies above chance level for the between-day and across-participant calibration schemes are in agreement with several other studies that have reported that ErrPs can be detected using these approaches [17,22,30–33, 35, 36 , 54 ].”
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A Multi-Channel Ensemble Method for Error-Related Potential Classification Using 2D EEG Images
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“the analysis of error-related potential events [17], [18].”
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A neuronal theta band signature of error monitoring during integration of facial expression cues
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“We analyzed the event-related potentials in FCz and Pz on both correct and error trials because these are the electrodes where the ERN and Pe are more evident, respectively (Bhattacharyya et al., 2017; Chavarriaga, Sobolewski & Millán, 2014; Pavone et al., 2016; Völker et al., 2018).”
MethodCustomizing the human-avatar mapping based on EEG error related potentials
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“Furthermore, we also evaluated to what extent the decoder trained with multiple participants generalizes to a new group of participants (non-personalized decoder) [48, 49].”
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“F1-score (Bhattacharyya et al. 2017) is an indicator used to measure the accuracy of the two-class model in statistics.”
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“In their work, Bhattacharyya et al. (2017) developed a decoder utilizing LDA, quadratic discriminant analysis, and logistic regression.”
Method[Classification algorithms of error-related potentials in brain-computer interface].
Sun, J., Jung, T., Xiao, X., Meng, J., Xu, M., & Ming, D. (2021) · PubMed Central · doi:10.7507/1001-5515.202012013 · Cited by 4 · see citing works
HeartRhythm: ECG-Based Music Preference Classification in Popular Music
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“Our analysis focused on F1-score owing to imbalanced classes and claimed in [20] that F1-score is more meaningful than accuracy because it accepts both false positives and negatives.”
MethodA generic error-related potential classifier based on simulated subjects
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“We have compared the performance of proposed ATPE algorithm with the three common feature extraction algorithms by using Friedman test [20].”
MethodXu, R., Wang, Y., Shi, X., et al. (2020) · Sensors · doi:10.3390/s20164475 · Cited by 2 · see citing works
“, P300-BCIs with ErrPs correction to improve accuracies [9,23], human-robot co-adaptation using ErrPs [16], and implicit cursor control based on ErrPs [15].”
BackgroundMethodGarrote, L., Perdiz, J., Yasemin, M., Pires, G., & Nunes, U. (2024) · IEEE RO-MAN · doi:10.1109/ro-man60168.2024.10731474 · Cited by 2 · see citing works
“In [10] authors propose an ensemble of classifiers to achieve user-independent classification of ErrPs, with an online accuracy of 73 .”
BackgroundWang, J., Yu, T., & Huang, Z. (2020) · IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) · doi:10.1109/icaica50127.2020.9182637 · Cited by 2 · see citing works
“Due to the design of the error detecting system, the leakage feature is the time interval from the fifth session which raises during the system detect and correct the error [11].”
BackgroundInfluentialThe value–complexity trade-off for reinforcement learning based brain–computer interfaces
Levi-Aharoni, H., & Tishby, N. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc8d8 · Cited by 2 · see citing works
Dias, C., Sousa, T., & Castelo-Branco, M. S. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00343 · Cited by 1 · see citing works
“…a midfrontal negative event-related potential (ERP) estimated to originate in the dorsal anterior cingulate cortex (dACC) ( Neta et al., 2015; Ullsperger et al., 2014) that arises 50-100 ms after erroneous actions ( Bhattacharyya et al., 2017; Chavarriaga et al., 2014; Estiveira et al., 2022).”
BackgroundSun, J., Meng, J., Wang, H., et al. (2025) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2025.3632096 · Cited by 1 · see citing works
The effect of static and dynamic visual stimulations on error-evoked brain responses
Xu, R., Wang, Y., Wang, N., et al. (2020) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc44109.2020.9175983 · Cited by 1 · see citing works
Bhattacharyya, S., & Hayashibe, M. (2021) · Brain Sciences · doi:10.3390/brainsci11111393 · Cited by 1 · see citing works
“ErrP signals are generally identified by a negative deflection occurring at 50–100 ms after the feedback response, which is immediately followed by a positive peak at approximately 200–500 ms after such a response [50].”
BackgroundSelf-citationERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification
Genevier Wyman, C., & Hirshfield, L. (2026) · Journal of Neural Engineering · doi:10.1088/1741-2552/ae9f95
“Calibration-free cross-subject ErrP classification has been demonstrated with generalized LDA [16], an xDAWN + RG pipeline with a support vector machine classifier [17], ensemble classifiers [18], and online PCA-based pipelines [19], though without inherent inter-pretability.”
Recognition of ErrP in P300 Speller Based on Time Series Pattern
Lin, Z., & Huang, Z. (2018) · International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · doi:10.1109/cisp-bmei.2018.8633092
“[16] proposed an ensemble decoder consisted of LDA, QDA, and LR for ErrP classification.”
MethodSubject-independent EEG classification of motor imagery based on a dual-branch feature fusionPreprint
Dong, Y., Han, D., Wen, X., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-2161783/v1
Ren, G., Mahmoud, S. S., Kumar, A., Fang, Q., & Yu, B. (2023) · 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas58349.2023.10389146
“The classical machine learning methods like linear discriminant analysis (LDA) [5][6] and support vector machines (SVM) [7] achieved some results in detection performance.”
MethodAn EEG Conformer Model for Error Feedback During Human-Robot Interaction
Han, J., Li, Y., Gu, X., & Faisal, A. A. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127307
“Earlier works [22] used traditional signal processing methods to extract the changes in EEG signal frequency bands and train a classifier.”
Sun, J., Wang, R., Wang, Q., Yao, H., Mu, Q., Wei, N. K., & Yu, H. (2026) · Advanced technology in neuroscience . · doi:10.4103/atn.atn-d-25-00034
Dias, C., Sousa, T., Ciarlo, A., Lührs, M., Goebel, R., & Castelo-Branco, M. S. (2026) · Research Square · doi:10.21203/rs.3.rs-11249829/v1
- Authors
- Bhattacharyya, S., Konar, A., Tibarewala, D. N., & Hayashibe, M.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Jadavpur University (IN)
- Corresponding author
- Bhattacharyya, Saugat
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2017-05-02
- Volume
- 11
- Pages
- 226-226
- ISSN
- 1662-453X (ISSN-L)
- PMC
- PMC5411431
- OpenAlex
- W2610581658
- Semantic Scholar
- cee742f32b15171e76b213ebcc2e28746ed87a01
- Cited by
- 43
- References
- 47
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 8 · 2023: 7 · 2022: 3 · 2021: 7 · 2020: 12 · 2018: 1 · 2017: 1
- FWCI
- 2.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Ulster University Research Portal (Ulster University) (publishedVersion, cc-by)
- HAL (submittedVersion, other-oa)
- Open Access at Essex (University of Essex) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission (2012-2648/001-001-EM)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- error-related potentials, EEG decoding, brain-computer interface, error detection, P300 speller, ensemble classifiers, linear discriminant analysis, quadratic discriminant analysis, logistic regression, single-trial classification, rehabilitation
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