Volume 23, Issue 1 (Spring 2021)                   Advances in Cognitive Sciences 2021, 23(1): 73-84 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Jalalkamali H, Tajik A, Nezamabadi-Pour H. Classification of auditory event-related potentials in a time discrimination task based on the oddball paradigm. Advances in Cognitive Sciences 2021; 23 (1) :73-84
URL: http://icssjournal.ir/article-1-1179-en.html
1- Assistant Professor in Cognitive Neuroscience, Computer Engineering Group, Higher Educational Complex of Zarand, Zarand, Iran
2- Graduate Student, Electronic Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
3- Professor in Electrical Engineering, Electrical Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran
Abstract:   (3810 Views)
Introduction: Despitemore than a century of research on how the brain perceives time, research on EEG pattern recognition related to time perception has been rare. A deficit in time perception has been demonstrated in many mental and neurological disorders such as Parkinson's, ADHD, depression, autism, and schizophrenia. Classification of event-related potentials in time discrimination tasks could be used as a screening tool for such diseases. This study aims to recognize whether the duration is judged as short or long by a person based on his electroencephalography (EEG) signal.
Methods: For this purpose, the oddball paradigm was used. 24 male and female students of Tabriz University took part in the experiment. From which 18 were selected after artifact rejection. Written informed consent was obtained from all the participants. EEG of the participants was recorded simultaneously with the task presentations. In an oddball auditory task, after two or four presentation of a pure tone of frequency 1000Hz, each one lasting for 500ms (standard stimuli) at a constant interstimulus interval of 300ms, a tone of a different frequency (500hz,700hz,1300hz, or 1500hz) and with the duration of 400ms or 600ms was presented (oddball stimulus). Participants were asked to compare the duration of the short (400ms) or long (600ms) oddball stimulus to the duration of its preceding standard stimuli (500ms). After the required preprocessing steps, averaging was performed to obtain the ERP segments at each of the experimental conditions.  The Cz electrode was selected to reduce further analysis among the 64 channels used for recording since most of the factors were significant at Cz, and the highest amplitude of ERP components was seen over there. The labeled target samples (ERP segments related to either 400ms or 600ms oddball stimuli) and labeled non-target samples (ERP segments related to the standard stimuli) were fed to the classifiers as the training set.  After training the classifiers with a subset of this dataset, the test was executed on the reminder.
Results: Behavioral results revealed that in the four repetition conditions, participants significantly perceived the oddball stimulus as more extended than in the two repetition condition, which implies that repetition causes time overestimation.  Electroencephalography results indicated that the repetition suppression effect was evident throughout the ERP waveform. Repeating a standard stimulus causes reduction in the amplitude of ERP components elicited by it. Whereas, presentation of a novel stimulus –oddball- leads to an increase in the corresponding ERP components amplitude. Finally, classification results showed that SVM (RBF) classifier could recognize the target samples (400ms intervals) from non-target samples (500ms and 600ms intervals) with the highest accuracy of 94.25% among decision tree and multilayer perceptron (MLP) classifiers. Also, SVM (RBF) yielded the highest accuracy of 93.98% when classifying 600ms related samples (target) versus 500ms and 600ms related samples (non-target). Finally, it achieved the best accuracy of 87.95% when classifying the test samples into multiclass of 400ms, 600ms, and 500ms classes separately.
Conclusion: The study results demonstrate that machine learning could accurately detect patterns related to short and long perceived intervals based on the peoples’ electroencephalography (EEG) signal.
Full-Text [PDF 997 kb]   (551 Downloads)    
Type of Study: Research |
Received: 2020/09/5 | Accepted: 2020/12/10 | Published: 2021/03/14

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Designed & Developed by : Yektaweb