Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Decoding Selective Attention and Cognitive Control Processing Through Stroop Interference Effect: An Event-Related Electroencephalography-Derived Study Publisher



Kamaliardekani R1 ; Yoonessi A1 ; Neishabouri AT2 ; Rabiei M3 ; Mohammadreza A4 ; Shafaghi L1 ; Hadjighassem M1, 5
Authors
Show Affiliations
Authors Affiliations
  1. 1. Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. SimiaRoom Labs, SimiaRoom OU, Tallin, Estonia
  3. 3. Department of Engineering Physics, School of physics, College of Science, University of Tehran, Tehran, Iran
  4. 4. Department of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
  5. 5. Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran

Source: Iranian Journal of Psychiatry and Behavioral Sciences Published:2022


Abstract

Background: The process of cognitive control and resultant selective attention construct the shared root of a continuum of neu-rocognitive functions. Efficient inhibition of task-irrelevant information and unwanted attributes has been evaluated through various paradigms. Stroop tasks in different forms could provide a platform for detecting the state of this type of inhibition and selective attention. Computational modeling of electroencephalography (EEG) signals associated with attentional control could complement the investigations of this discipline. Methods: Ninety-six trials of a three-condition Color-Word Stroop task were performed while recording EEG. All subjects (9 par-ticipants) were right-handed (20-25 years), and half were male. Three-condition signal epochs were redefined as two conditions: (1) Differentiated incongruent epochs (DIe), which are incongruent epochs that their equivalent congruent epochs are subtracted from and (2) Neutral epochs, in which intervals of 150-300 ms and 350-500 ms post-stimulus were extracted. Preprocessed data were then analyzed, and the whole EEG epoch was considered the variable to be compared between conditions. An acceptably fitted support vector machine (SVM) algorithm classified the data. Results: For each individual, the comparison was made regarding DIe and neutral epochs for two intervals (150-300 and 350-500 ms). The SVM classification method provided acceptable accuracies at 59-65% for the 150-300 ms interval and 65-70% for the 350-500 ms interval within individuals. Regarding frequency domain assessments, the Delta frequency band for these two intervals showed no significant difference between the two conditions. Conclusions: The SVM models performed better for the late event-related epoch (350-500 ms) classification. Hence, selective attention-related features were more significant in this temporal interval. © 2022, Author(s).