PMID- 34603433
OWN - NLM
STAT- MEDLINE
DCOM- 20211005
LR  - 20211005
IS  - 1687-5273 (Electronic)
IS  - 1687-5265 (Print)
VI  - 2021
DP  - 2021
TI  - EEG-Based Personality Prediction Using Fast Fourier Transform and DeepLSTM Model.
PG  - 6524858
LID - 10.1155/2021/6524858 [doi]
LID - 6524858
AB  - In this paper, a deep long short term memory (DeepLSTM) network to classify 
      personality traits using the electroencephalogram (EEG) signals is implemented. 
      For this research, the Myers-Briggs Type Indicator (MBTI) model for predicting 
      personality is used. There are four groups in MBTI, and each group consists of 
      two traits versus each other; i.e., out of these two traits, every individual 
      will have one personality trait in them. We have collected EEG data using a 
      single NeuroSky MindWave Mobile 2 dry electrode unit. For data collection, 40 
      Hindi and English video clips were included in a standard database. All clips 
      provoke various emotions, and data collection is focused on these emotions, as 
      the clips include targeted, inductive scenes of personality. Fifty participants 
      engaged in this research and willingly agreed to provide brain signals. We 
      compared the performance of our deep learning DeepLSTM model with other 
      state-of-the-art-based machine learning classifiers such as artificial neural 
      network (ANN), K-nearest neighbors (KNN), LibSVM, and hybrid genetic programming 
      (HGP). The analysis shows that, for the 10-fold partitioning method, the DeepLSTM 
      model surpasses the other state-of-the-art models and offers a maximum 
      classification accuracy of 96.94%. The proposed DeepLSTM model was also applied 
      to the publicly available ASCERTAIN EEG dataset and showed an improvement over 
      the state-of-the-art methods.
CI  - Copyright (c) 2021 Harshit Bhardwaj et al.
FAU - Bhardwaj, Harshit
AU  - Bhardwaj H
AUID- ORCID: 0000-0001-9184-5073
AD  - CSE Department, Gautam Buddha University, Greater Noida, India.
FAU - Tomar, Pradeep
AU  - Tomar P
AUID- ORCID: 0000-0002-7565-0708
AD  - CSE Department, Gautam Buddha University, Greater Noida, India.
FAU - Sakalle, Aditi
AU  - Sakalle A
AUID- ORCID: 0000-0003-1556-8937
AD  - CSE Department, Gautam Buddha University, Greater Noida, India.
FAU - Ibrahim, Wubshet
AU  - Ibrahim W
AUID- ORCID: 0000-0003-2281-8842
AD  - Department of Mathematics, Ambo University, Ambo, Ethiopia.
LA  - eng
PT  - Journal Article
DEP - 20210920
PL  - United States
TA  - Comput Intell Neurosci
JT  - Computational intelligence and neuroscience
JID - 101279357
SB  - IM
MH  - Algorithms
MH  - *Electroencephalography
MH  - Fourier Analysis
MH  - Humans
MH  - *Neural Networks, Computer
MH  - Personality
PMC - PMC8481053
COIS- The authors declare that they have no conflicts of interest.
EDAT- 2021/10/05 06:00
MHDA- 2021/10/06 06:00
PMCR- 2021/09/20
CRDT- 2021/10/04 05:58
PHST- 2021/08/02 00:00 [received]
PHST- 2021/08/30 00:00 [revised]
PHST- 2021/09/03 00:00 [accepted]
PHST- 2021/10/04 05:58 [entrez]
PHST- 2021/10/05 06:00 [pubmed]
PHST- 2021/10/06 06:00 [medline]
PHST- 2021/09/20 00:00 [pmc-release]
AID - 10.1155/2021/6524858 [doi]
PST - epublish
SO  - Comput Intell Neurosci. 2021 Sep 20;2021:6524858. doi: 10.1155/2021/6524858. 
      eCollection 2021.