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A condition-independent framework for the classification of error-related brain activity

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Abstract

The cognitive processing and detection of errors is important in the adaptation of the behavioral and learning processes. This brain activity is often reflected as distinct patterns of event-related potentials (ERPs) that can be employed in the detection and interpretation of the cerebral responses to erroneous stimuli. However, high-accuracy cross-condition classification is challenging due to the significant variations of the error-related ERP components (ErrPs) between complexity conditions, thus hindering the development of error recognition systems. In this study, we employed support vector machines (SVM) classification methods, based on waveform characteristics of ErrPs from different time windows, to detect correct and incorrect responses in an audio identification task with two conditions of different complexity. Since the performance of the classifiers usually depends on the salience of the features employed, a combination of the sequential forward floating feature selection (SFFS) and sequential forward feature selection (SFS) methods was implemented to detect condition-independent and condition-specific feature subsets. Our framework achieved high accuracy using a small subset of the available features both for cross- and within-condition classification, hence supporting the notion that machine learning techniques can detect hidden patterns of ErrP-based features, irrespective of task complexity while additionally elucidating complexity-related error processing variations.

A schematic of the proposed approach. (a) EEG recordings in an auditory experiment in two conditions of different complexity. (b) Characteristic event related activity feature extraction. (c) Selection of feature vector subsets for easy and hard conditions corresponding to correct (Class1) and incorrect (Class2) responses. (d) Performance for individual and cross-condition classification.

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Funding

This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning» in the context of the project “Strengthening Human Resources Research Potential via Doctorate Research” (MIS-5000432), implemented by the State Scholarships Foundation (ΙΚΥ).

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, National Technical University of Athens, 9, Iroon Polytechniou Str, Zografos, 15780, Athens, Greece

    Ioannis Kakkos & George K. Matsopoulos

  2. Department of Biomedical Engineering, University of West Attica, Athens, Greece

    Errikos M. Ventouras & Pantelis A. Asvestas

  3. Department of Mathematics and Engineering Sciences, Hellenic Military University, Athens, Greece

    Irene S. Karanasiou

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  1. Ioannis Kakkos
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  2. Errikos M. Ventouras
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  3. Pantelis A. Asvestas
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  4. Irene S. Karanasiou
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Correspondence to Ioannis Kakkos.

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Kakkos, I., Ventouras, E.M., Asvestas, P.A. et al. A condition-independent framework for the classification of error-related brain activity. Med Biol Eng Comput 58, 573–587 (2020). https://doi.org/10.1007/s11517-019-02116-5

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