Personality Prediction with Hybrid Genetic Programming using Portable EEG Device
Authors: 
Harshit Bhardwaj, Pradeep Tomar, Aditi Sakalle, Maneesha Sakalle, Rishi Asthana, Arpit Bhardwaj, Wubshet Ibrahim Academic Editor: Vijay KumarAuthors Info & Claims
Abstract
This work suggests a method to identify personality traits regarding the targeted film clips in real-time. Such film clips elicit feelings in people while capturing their brain impulses using the electroencephalogram (EEG) devices and examining personality traits. The Myers–Briggs Type Indicator (MBTI) paradigm for determining personality is employed in this study. The fast Fourier transform (FFT) approach is used for feature extraction, and we have used hybrid genetic programming (HGP) for EEG data classification. We used a single-channel NeuroSky MindWave 2 dry electrode unit to obtain the EEG data. In order to collect the data, thirty Hindi and English video clips were placed in a conventional database. Fifty people volunteered to participate in this study and willingly provided brain signals. Using this dataset, we have generated four two-class HGP classifiers (HGP1, HGP2, HGP3, and HGP4), one for each group of MBTI traits overall classification accuracy of the HGP classifier as 82.25% for 10-fold cross-validation partition.
References
[1]
W. T. Norman, “Toward an adequate taxonomy of personality attributes: replicated factor structure in peer nomination personality ratings,” Journal of Abnormal and Social Psychology, vol. 66, no. 6, pp. 574–583, 1963.
[2]
D. Simpson, “Phrenology and the neurosciences: contributions of f j. gall and j. g. spurzheim,” ANZ Journal of Surgery, vol. 75, no. 6, pp. 475–482, 2005.
[3]
H. Meredith, “Physiological and somatological norms,” Contemporary Psychology: Journalism Review, vol. 16, no. 4, pp. 252–254, 1971.
[4]
N. O. Rule and N. Ambady, “Democrats and republicans can be differenti- ated from their faces,” PLoS One, vol. 5, no. 1, 2010.
[5]
C. Y. Olivola and A. Todorov, “Fooled by first impressions? reexam- ining the diagnostic value of appearance-based inferences,” Journal of Experimental Social Psychology, vol. 46, no. 2, pp. 315–324, 2010.
[6]
G. W. Allport and H. S. Odbert, “Trait-names: a psycho-lexical study,” Psychological Monographs, vol. 47, 1936.
[7]
R. R. McCrae and O. P. John, “An introduction to the five-factor model and its applications,” Journal of Personality, vol. 60, no. 2, pp. 175–215, 1992.
[8]
P. T. Costa and R. R. McCrea, “Revised neo personality inventory (neo pi-r) and neo five-factor inventory (neo-ffi),” Psychological Assessment Resources, 1992.
[9]
J. B. Campbell and J. F. Heller, “Correlations of extraversion, impulsivity and sociability with sensation seeking and mbti-introversion,” Personality and Individual Differences, vol. 8, no. 1, pp. 133–136, 1987.
[10]
J. Hunsley, C. M. Lee, J. M. Wood, and W. Taylor, “Controversial and questionable assessment techniques,” Science and Pseudoscience in Clinical Psychology, Guilford Press, New York, NY, USA, 2015.
[11]
S. Tiwari, S. Goel, and A. Bhardwaj, “Machine learning approach for the classification of eeg signals of multiple imagery tasks,” in Proceedings of the 11th In ternational Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–7, IEEE, Kharagpur, India, July 2020.
[12]
A. McGirr, F. Van den Eynde, E. Chachamovich, M. P. Fleck, and M. T. Berlim, “Personality dimensions and deep repetitive transcranial magnetic stimulation (dtms) for treatment-resistant depression: a pilot trial on five-factor prediction of antidepressant response,” Neuroscience Letters, vol. 563, pp. 144–148, 2014.
[13]
D. Shepherd, J. Mulgrew, and M. J. Hautus, “Exploring the autonomic correlates of personality,” Autonomic Neuroscience, vol. 193, 2015.
[14]
J. Leclerc, M. Rahn, and W. Linden, “Does personality predict blood pressure over a 10-year period?” Personality and Individual Differences, vol. 40, no. 6, pp. 1313–1321, 2006.
[15]
A. Sakalle, P. Tomar, H. Bhardwaj, D. Acharya, and A. Bhardwaj, “An analysis of machine learning algorithm for the classification of emotionrecognition,” in Soft Computing for Problem Solving, Springer, Berlin, Germany, 2021.
[16]
H. Bhardwaj, P. Tomar, A. Sakalle, D. Acharya, T. Badal, and A. Bhardwaj, “A deeplstm model for personality traits classification using eeg signals,” IETE Journal of Research, pp. 1–9, 2022.
[17]
H. Bhardwaj, P. Tomar, A. Sakalle, and W. Ibrahim, “Eeg-based personality prediction using fast fourier transform and deeplstm model,” Computational Intelligence and Neuroscience, vol. 2021, 10 pages, 2021.
[18]
H. Bhardwaj, P. Tomar, A. Sakalle, D. Acharya, and A. Bhardwaj, “Personality prediction using eeg signals and machine learning algo- rithms,” in Soft Computing for Problem Solving, Springer, Berlin, Germany, 2021.
[19]
H. Bhardwaj, P. Tomar, A. Sakalle, and A. Bhardwaj, “Classification of extraversion and introversion personality trait using electroencephalo- gram signals,” in Proceedings of the International Conference on Artificial Intelligence and Sustainable Computing, pp. 31–39, Springer, Greater Noida, India, March 2021.
[20]
T. Takahashi, T. Murata, T. Hamada, M. Omori, H. Kosaka, M. Kikuchi, H. Yoshida, and Y. Wada, “Changes in eeg and autonomic nervous activity during meditation and their association with personality traits,” International Journal of Psychophysiology, vol. 55, no. 2, pp. 199–207, 2005.
[21]
A. Bhardwaj, A. Tiwari, M. V. Varma, and M. R. Krishna, “Classifi- cation of eeg signals using a novel genetic programming approach,” in Proceedings of the Companion Publication of the 2014 Annual Confer- ence on Genetic and Evolutionary Computation, pp. 1297–1304, ACM, New York, NY, USA, July 2014.
[22]
A. Bhardwaj, A. Tiwari, M. V. Varma, and M. R. Krishna, “An analysis of integration of hill climbing in crossover and mutation operation for eeg signal classification,” in Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 209–216, ACM, Madrid, Spain, July 2015.
[23]
D. Acharya, R. Jain, S. S. Panigrahi, R. Sahni, S. Jain, S. P. Deshmukh, and A. Bhardwaj, “Multi-class emotion classification using eeg sig- nals,” in Proceedings of the International Advanced Computing Conference, pp. 474–491, Springer, Singapore, February 2020.
[24]
W. T. Cochran, J. W. Cooley, D. L. Favin, H. D. Helms, R. A. Kaenel, W. W. Lang, G. C. Maling, D. E. Nelson, C. M. Rader, and P. D. Welch, “What is the fast fourier transform?” Proceedings of the IEEE, vol. 55, no. 10, pp. 1664–1674, 1967.
[25]
C. Yaacoub, G. Mhanna, and S. Rihana, “A genetic-based feature selec- tion approach in the identification of left/right hand motor imagery for a brain-computer interface,” Brain Sciences, vol. 7, 2017.
[26]
H. Ocak, “Automatic detection of epileptic seizures in eeg using discrete wavelet transform and approximate entropy,” Expert Systems with Applications, vol. 36, no. 2, pp. 2027–2036, 2009.
[27]
A. T. Tzallas, M. G. Tsipouras, and D. I. Fotiadis, “The use of time- frequency distributions for epileptic seizure detection in eeg recordings,” in Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3–6, IEEE, Lyon, France, August 2007.
[28]
N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998.
[29]
N. Saito and R. R. Coifman, “Local discriminant bases and their appli-cations,” Journal of Mathematical Imaging and Vision, vol. 5, no. 4, pp. 337–358, 1995.
[30]
S. Valipour, A. Shaligram, and G. Kulkarni, “Spectral analysis of eeg signal for detection of alpha rhythm with open and closed eyes,” International Journal of Engineering and Innovative Technology (IJEIT), vol. 3, pp. 1–4, 2013.
[31]
T. Tandera, D. Suhartono, R. Wongso, and Y. L. Prasetio, “Person- ality prediction system from facebook users,” Procedia Computer Science, vol. 116, pp. 604–611, 2017.
[32]
D. Acharya, A. Billimoria, N. Srivastava, S. Goel, and A. Bhardwaj, “Emotion recognition using fourier transform and genetic programming,” Applied Acoustics, vol. 164, 2020.
[33]
D. Acharya, N. Varshney, A. Vedant, Y. Saxena, P. Tomar, S. Goel, and A. Bhardwaj, “An enhanced fitness function to recognize unbal- anced human emotions data,” Expert Systems with Applications, vol. 166, 2021.
[34]
D. Acharya, S. Goel, R. Asthana, and A. Bhardwaj, “A novel fitness function in genetic programming to handle unbalanced emotion recog- nition data,” Pattern Recognition Letters, vol. 133, pp. 272–279, 2020.
[35]
A. Bhardwaj, A. Sakalle, H. Chouhan, and H. Bhardwaj, “Controlling the problem of bloating using stepwise crossover and double mutation technique,” Advanced Computing: International Journal, vol. 2, no. 6, pp. 59–68, 2011.
[36]
A. Bhardwaj, A. Tiwari, R. Krishna, and V. Varma, “A novel genetic programming approach for epileptic seizure detection,” Computer Methods and Programs in Biomedicine, vol. 124, pp. 2–18, 2016.
[37]
J. Koza, “On the programming of computers by means of natural selection,” Genetic Programming, vol. 4, pp. 87–112, 1992.
[38]
H. Bhardwaj and P. Dashore, “A novel genetic programming approach to control bloat using crossover and mutation with intelligence technique,” in Proceedings of the International Conference on Computer, Communication and Control (IC4), pp. 1–6, IEEE, Jabalpur, India, December 2015.
[39]
A. Bhardwaj and A. Tiwari, “Performance improvement in genetic pro- gramming using modified crossover and node mutation,” in Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, pp. 1721–1722, New York, NY, USA, July 2013.
[40]
A. Bhardwaj and A. Tiwari, “A novel genetic programming based clas- sifier design using a new constructive crossover operator with a local search technique,” in Proceedings of the International Conference on Intelligent Comput- ing, pp. 86–95, Springer, Nanning, China, July 2013.
[41]
D. Devarriya, C. Gulati, V. Mansharamani, A. Sakalle, and A. Bhardwaj, “Unbalanced breast cancer data classification using novel fitness functions in genetic programming,” Expert Systems with Applications, vol. 140, 2020.
[42]
H. Bhardwaj, A. Sakalle, A. Bhardwaj, and A. Tiwari, “Classification of electroencephalogram signal for the detection of epilepsy using inno- vative genetic programming,” Expert Systems, vol. 36, no. 1, 2019.
[43]
A. Bhardwaj, A. Tiwari, D. Chandarana, and D. Babel, “A genetically optimized neural network for classification of breast cancer disease,” in Proceedings of the 7th International Conference on Biomedical Engineering and In- formatics, pp. 693–698, IEEE, Dalian, China, October 2014.
[44]
A. Sakalle, P. Tomar, H. Bhardwaj, A. Iqbal, M. Sakalle, A. Bhardwaj, and W. Ibrahim, “Genetic programming-based feature selection for emotion classification using eeg signal,” Journal of Healthcare Engineer- ing, vol. 2022, 6 pages, 2022.
[45]
A. Sakalle, P. Tomar, H. Bhardwaj, and A. Bhardwaj, “Emotion recog- nition using portable eeg device,” in Proceedings of the International Conference on Artifi- cial Intelligence and Sustainable Computing, pp. 17–30, Springer, Greater Noida, India, March 2021.
[46]
H. Bhardwaj, A. Sakalle, A. Bhardwaj, A. Tiwari, and M. Verma, “Breast cancer diagnosis using simultaneous feature selection and classi- fication: a genetic programming approach,” in Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), pp. 2186–2192, IEEE, Bangalore, India, November 2018.
[47]
A. Purohit, A. Bhardwaj, A. Tiwari, and N. S. Choudhari, “Removing code bloating in crossover operation in genetic programming,” in Proceedings of the International Conference on Recent Trends in Information Technology (ICRTIT), pp. 1126–1130, IEEE, Chennai, India, June 2011.
[48]
M. M. Tadesse, H. Lin, B. Xu, and L. Yang, “Personality predictions based on user behavior on the facebook social media platform,” IEEE Access, vol. 6, 2018.
[49]
G. Mohammadi and A. Vinciarelli, “Automatic personality perception: prediction of trait attribution based on prosodic features,” IEEE Transac- tions on Affective Computing, vol. 3, pp. 273–284, 2012.
[50]
B. Y. Pratama and R. Sarno, “Personality classification based on twitter text using naive bayes, knn and svm,” in Proceedings of the International Conference on Data and Software Engineering (ICoDSE), pp. 170–174, IEEE, Yogyakarta, Indonesia, June 2015.
[51]
K. H. Peng, L. H. Liou, C. S. Chang, and D. S. Lee, “Predicting person- ality traits of Chinese users based on facebook wall posts,” in Proceedings of the 24th Wireless and Optical Communication Conference (WOCC), pp. 9–14, IEEE, Taipei, Taiwan, October 2015.
[52]
G. Zhao, Y. Ge, B. Shen, X. Wei, and H. Wang, “Emotion analysis for personality inference from eeg signals,” IEEE transactions on affective computing, vol. 9, no. 3, pp. 362–371, 2018.
[53]
D. R. J. Moreno, J. C. Gomez, D. L. Almanza-Ojeda, and A. Ibarra-Manzano, “Prediction of personality traits in twitter users with latent features,” in Proceedings of the International Conference on Electronics, Communi- cations and Computers (CONIELECOMP), pp. 176–181, IEEE, Cholula, Mexico, February 2019.
[54]
V. Ong, A. D. Rahmanto, D. Suhartono, A. E. Nugroho, E. W. Andangsari, M. N. Suprayogi, and D. William, “Personality prediction based on twitter information in bahasa Indonesia,” in Proceedings of the Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 367–372, IEEE, Prague, Czech Republic, September 2017.
[55]
J. Shen, O. Brdiczka, and J. Liu, “Understanding email writers: per- sonality prediction from email messages,” in Proceedings of the International Conference on User Modeling, Adaptation, and Personalization, pp. 318–330, Springer, Rome, Italy, June 2013.
[56]
S. J. Rubenzer, T. R. Faschingbauer, and D. S. Ones, “Assessing the us presidents using the revised neo personality inventory,” Assessment, vol. 7, no. 4, pp. 403–419, 2000.
[57]
A. Sakalle, P. Tomar, H. Bhardwaj, D. Acharya, and A. Bhardwaj, “A lstm based deep learning network for recognizing emotions using wire- less brainwave driven system,” Expert Systems with Applications, vol. 173, 2021.
Recommendations
EEG based personality prediction using genetic programming
AbstractThe reliable correlation between personality and brain signal ensures that inferences from cognitive processes can be achieved. This research aims primarily to predict one's personality using brain signals. On grounds of Psychology, the inference ...
Using Personality Traits to Understand the Influence of Personality on Computer Programming: An Empirical Study
Computer programming is complex and all personality factors might influence it. Personality factors are comprehensive but broad and, therefore, lower level traits may help understanding the influence of personality on computer programming. The objective ...
EEG-Based Personality Prediction Using Fast Fourier Transform and DeepLSTM Model
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. ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © 2022 Harshit Bhardwaj et al.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher
Hindawi Limited
London, United Kingdom
Publication History
Published: 01 January 2022
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 14 Dec 2024