A condition-independent framework for the classification of error-related brain activity.
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...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 3; pp. 573 - 588 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Mar2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142105125&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142105125 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Mar2020 vid: 58 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142105125 142105125 NLM31919721 142105125 10.1007/s11517-019-02116-5 NLM31919721 142105125 ppf: 573 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A condition-independent framework for the classification of error-related brain activity. aug: au: Kakkos, Ioannis Ventouras, Errikos M. Asvestas, Pantelis A. Karanasiou, Irene S. Matsopoulos, George K. affil: School of Electrical and Computer Engineering, National Technical University of Athens, 9, Iroon Polytechniou Str, Zografos, 15780, Athens, Greece sug: subj: Brain Physiology Algorithms Adult Female Pharmacokinetics Electroencephalography Male Electrodes Human Funding Source Adult: 19-44 years Female Male ab: 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. Graphical abstract 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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