Error-related potentials (Primary and secondary ErrP) and P300 event related potentials – BCI-Double-ErrP-Dataset

Citation Author(s):
Institute of Systems and Robotics of the University of Coimbra
Institute of Systems and Robotics of the University of Coimbra and Polytechnic Institute of Tomar
Urbano J.
Institute of Systems and Robotics of the University of Coimbra
Submitted by:
Gabriel Pires
Last updated:
Fri, 03/20/2020 - 08:13
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BCI-Double-ErrP-Dataset is an EEG dataset recorded while participants used a P300-based BCI speller. This speller uses a P300 post-detection based on Error-related potentials (ErrPs) to detect and correct errors (i.e. when the detected symbol does not match the user’s intention). After the P300 detection, an automatic correction is made when an ErrP is detected (this is called a “Primary ErrP”). The correction proposed by the system is also evaluated, eventually eliciting a “Secondary ErrP” if the correction is wrong. The overall approach is called “Double-ErrP detection and correction” [1]. The recorded datasets are useful to research the use of ErrPs to increase BCI reliability and to research how “Secondary ErrPs” can be used to increase the naturalness of Human-machine interaction. The datasets are also useful to research ErrP variability across subjects and across sessions in different days.

The EEG signals were recorded from nine able-bodied participants (S1-S9) and one tetraplegic participant (P1) with medullar injury (C4/C5 level) with ages between 24 and 43 years old. However, the data set comprises only 8 subjects because two participants (S7 and S8) took part only in the first session of the experiments. Scalp EEG was recorded at 256 Hz with a g.USBamp bioamplifier (g.tec, Austria).

A detailed description of the approach and experiments is given in [1] and a video in [2].



[1] A. Cruz, G. Pires, U. Nunes (2018), "Double ErrP Detection for Automatic Error Correction in an ERP-Based BCI Speller", IEEE Transactions on Neural Systems and Rehabilitation Engineering, 99, January 2018, doi: 10.1109/TNSRE.2017.2755018.


[2] Demonstrative video in


Acknowledgement: This data collection was supported in part by Project B-RELIABLE funded by FEDER/FNR/OE through programs CENTRO2020 and Portuguese foundation for science and technology (FCT), under grant SAICT/30935/2017. 


A detailed description of the data is given in “BCI-Double-ErrP-Dataset-instructions.pdf” and a Matlab code example is provided to extract P300 and ErrPs (primary and secondary).


There are 4 folders, one with the datasets of the P300 calibration (session 1), one with the datasets of the ErrP calibration (session 1), one with the datasets of the testing session (session 2), and a folder with the Matlab code to run the example.