PMID- 35848165
OWN - NLM
STAT- MEDLINE
DCOM- 20240403
LR  - 20240403
IS  - 1563-5279 (Electronic)
IS  - 0020-7454 (Linking)
VI  - 134
IP  - 4
DP  - 2024 Apr
TI  - Detecting how time is subjectively perceived based on event-related potentials 
      (ERPs): a machine learning approach.
PG  - 372-380
LID - 10.1080/00207454.2022.2103413 [doi]
AB  - Background and objective: Time perception is essential for the precise 
      performance of many of our activities and the coordination between different 
      modalities. But it is distorted in many diseases and disorders. Event-related 
      potentials (ERP) have long been used to understand better how the human brain 
      perceives time, but machine learning methods have rarely been used to detect a 
      person's time perception from his/her ERPs. Methods: In this study, EEG signals 
      of the individuals were recorded while performing an auditory oddball time 
      discrimination task. After features were extracted from ERPs, data balancing, and 
      feature selection, machine learning models were used to distinguish between the 
      oddball durations of 400 ms and 600 ms from standard durations of 500 ms. ERP 
      results showed that the P3 evoked by the 600 ms oddball stimuli appeared about 
      200 ms later than that of the 400 ms oddball tones. Classification performance 
      results indicated that support vector machine (SVM) outperformed K-nearest 
      neighbors (KNN), Random Forest, and Logistic regression models. Results: The 
      accuracy of SVM was 91.24, 92.96, and 89.9 for the three used labeling modes, 
      respectively. Another important finding was that most features selected for 
      classification were in the P3 component range, supporting the observed 
      significant effect of duration on the P3. Although all N1, P2, N2, and P3 
      components contributed to detecting the desired durations. Conclusion: Therefore, 
      results of this study suggest the P3 component as a potential candidate to detect 
      sub-second periods in future researches on brain-computer interface (BCI) 
      applications.
FAU - Jalalkamali, Hoda
AU  - Jalalkamali H
AD  - Computer Engineering Group, Higher Education Complex of Zarand, Kerman, Iran.
FAU - Tajik, Amirhossein
AU  - Tajik A
AD  - Department of Electrical Engineering, College of Engineering, Shahid Bahonar 
      University of Kerman, Kerman, Iran.
FAU - Hatami, Rashid
AU  - Hatami R
AD  - ICT Group, National Iranian Copper Industries Co. (NICICO), Sarcheshme, Kerman, 
      Iran.
FAU - Nezamabadipour, Hossein
AU  - Nezamabadipour H
AD  - Department of Electrical Engineering, College of Engineering, Shahid Bahonar 
      University of Kerman, Kerman, Iran.
LA  - eng
PT  - Journal Article
DEP - 20220725
PL  - England
TA  - Int J Neurosci
JT  - The International journal of neuroscience
JID - 0270707
SB  - IM
MH  - Humans
MH  - Male
MH  - Female
MH  - *Evoked Potentials, Auditory/physiology
MH  - *Evoked Potentials/physiology
MH  - Brain/physiology
MH  - Auditory Perception
MH  - Electroencephalography
MH  - Reaction Time/physiology
OTO - NOTNLM
OT  - Time perception
OT  - classification
OT  - event-related potentials (ERPs)
OT  - machine learning
EDAT- 2022/07/19 06:00
MHDA- 2024/04/03 06:44
CRDT- 2022/07/18 04:42
PHST- 2024/04/03 06:44 [medline]
PHST- 2022/07/19 06:00 [pubmed]
PHST- 2022/07/18 04:42 [entrez]
AID - 10.1080/00207454.2022.2103413 [doi]
PST - ppublish
SO  - Int J Neurosci. 2024 Apr;134(4):372-380. doi: 10.1080/00207454.2022.2103413. Epub 
      2022 Jul 25.