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Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques

Yıl 2019, Cilt: 31 Sayı: 1, 47 - 52, 31.03.2019
https://doi.org/10.7240/jeps.459420

Öz

Extraction
of the information hidden in the brain electrical signal enhance the
classification of the current mental status. 
In this study, 16 channel EEG data were collected from 15 volunteers under
three conditions. Participants were asked to rest with eyes open and eyes
closed states each with a duration of three minutes. Finally, a task has been
imposed to increase mental workload. EEG data were epoched with a duration of
one second and power spectrum was computed for each time window. The power
spectral features of all channels in traditional bands were calculated for all
subjects and the results were concatanated to form the input data to be used in
classification. Decision tree, K-nearest neighbor and Support Vector Machine
techniques were implemented in order to classify the one second epochs. The
accuracy value obtained from KNN was found to be 0.94 while it was 0.88 for decision
tree and SVM. KNN was found to outperform the two methods when all channel and
power spectral features were used. In can be concluded that, even with the use
of input features formed by concatanating all subject’s data, high
classification accuracies can be obtained in the determination of the increased
mental workload state.

Kaynakça

  • Mumtaz W, Ali SSA, Yasin MAM, Malik AS. A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD). Med Biol Eng Comput. 2018 Feb;56(2):233-246. doi: 10.1007/s11517-017-1685-z.
  • Paulo Afonso Medeiros Kanda , Lucas R. Trambaiolli, Ana C. Lorena, Francisco J. Fraga, Luis Fernando I. Basile, Ricardo Nitrini, and Renato Anghinah, Clinician’s Road Map to Wavelet EEG as an Alzheimer’s disease Biomarker, Clinical EEG and Neuroscience, DOI: 10.1177/1550059413486272
  • Babiloni Claudio, Triggiani Antonio I., Lizio Roberta, Cordone Susanna, Tattoli Giacomo, Bevilacqua Vitoantonio, Soricelli Andrea, Ferri Raffaele, Nobili Flavio, Gesualdo Loreto, Millán-Calenti José C., Buján Ana, Tortelli Rosanna, Cardinali Valentina, Barulli Maria Rosaria, Giannini Antonio, Spagnolo Pantaleo, Armenise Silvia, Buenza Grazia, Scianatico Gaetano, Logroscino Giancarlo, Frisoni Giovanni B., del Percio Claudio, Classification of Single Normal and Alzheimer's Disease Individuals from Cortical Sources of Resting State EEG Rhythms, Frontiers in Neuroscience, Vol 10, 2016, 47
  • Bellotti R, De Carlo F, Massafra R, de Tommaso M, Sciruicchio V., Topographic classification of EEG patterns in Huntington's disease. Neurol Clin Neurophysiol. 2004 Nov 30;2004:37.
  • Cruz-Garza Jesus G., Hernandez Zachery R., Nepaul Sargoon, Bradley Karen K., Contreras-Vidal Jose L. Neural decoding of expressive human movement from scalp electroencephalography (EEG), Frontiers in Human Neuroscience Vol 8, 2014,pp188
  • B. OberMaier, C. Guger, C. Neuper, G. Pfurtscheller, Hidden Markov models for online classification of single trial EEG data, Pattern Recognition Letters, 22 (2001), 1299-1309
  • Cheng-Jian Lin, Ming-HuaHsieh, Classification of mental task from EEG data using neural networks based on particle swarm optimization, Neurocomputing, 72 (2009), 1121-1130
  • Dan Nemrodov, Matthias Niemeier, Ashutosh Patel, Adrian Nestor. The Neural Dynamics of Facial Identity Processing: insights from EEG-Based Pattern Analysis and Image Reconstruction. eNeuro 29 January 2018, ENEURO.0358-17.2018; DOI: 10.1523/ENEURO.0358-17.2018
  • Iñaki Iturrate, Ricardo Chavarriaga, Luis Montesano, Javier Minguez & José del R. Millán, Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control, Scientific Reports volume 5, (2015)
  • H. U. Amin, W. Mumtaz, A. R. Subhani, M. N. M. Saad and A. S. Malik, Classification of EEG Signals Based on Pattern Recognition Approach, Front. Comput. Neurosci. 11:103. doi: 10.3389/fncom.2017.00103
  • V. N. Vapnik, An Overview of Statistical Learning Theory, IEEE Transactions on Neural Networks Vol(10) No. 5, 1999
  • J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81–106, 1986
  • Xiao-Wei Wang , Dan Nie, Bao-Liang Lu, Emotional state classification from EEG data using machine learning approach, Neurocomputing 129 (2014) 94–106
  • David Gutierrez, Diana I. Escalona-Vargas, EEG data classification through signal spatial redistribution and optimized linear discriminants, Computer Methods and Programs in Biomedicine 97 (2010) 39–47
  • E.Parvinnia, M.Sabeti, M.Zolghadri Jahromi, R.Boostani, Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm, Journal of King Saud University - Computer and Information Sciences, Volume 26, Issue 1, January 2014, Pages 1-6

Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques

Yıl 2019, Cilt: 31 Sayı: 1, 47 - 52, 31.03.2019
https://doi.org/10.7240/jeps.459420

Öz

Kaynakça

  • Mumtaz W, Ali SSA, Yasin MAM, Malik AS. A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD). Med Biol Eng Comput. 2018 Feb;56(2):233-246. doi: 10.1007/s11517-017-1685-z.
  • Paulo Afonso Medeiros Kanda , Lucas R. Trambaiolli, Ana C. Lorena, Francisco J. Fraga, Luis Fernando I. Basile, Ricardo Nitrini, and Renato Anghinah, Clinician’s Road Map to Wavelet EEG as an Alzheimer’s disease Biomarker, Clinical EEG and Neuroscience, DOI: 10.1177/1550059413486272
  • Babiloni Claudio, Triggiani Antonio I., Lizio Roberta, Cordone Susanna, Tattoli Giacomo, Bevilacqua Vitoantonio, Soricelli Andrea, Ferri Raffaele, Nobili Flavio, Gesualdo Loreto, Millán-Calenti José C., Buján Ana, Tortelli Rosanna, Cardinali Valentina, Barulli Maria Rosaria, Giannini Antonio, Spagnolo Pantaleo, Armenise Silvia, Buenza Grazia, Scianatico Gaetano, Logroscino Giancarlo, Frisoni Giovanni B., del Percio Claudio, Classification of Single Normal and Alzheimer's Disease Individuals from Cortical Sources of Resting State EEG Rhythms, Frontiers in Neuroscience, Vol 10, 2016, 47
  • Bellotti R, De Carlo F, Massafra R, de Tommaso M, Sciruicchio V., Topographic classification of EEG patterns in Huntington's disease. Neurol Clin Neurophysiol. 2004 Nov 30;2004:37.
  • Cruz-Garza Jesus G., Hernandez Zachery R., Nepaul Sargoon, Bradley Karen K., Contreras-Vidal Jose L. Neural decoding of expressive human movement from scalp electroencephalography (EEG), Frontiers in Human Neuroscience Vol 8, 2014,pp188
  • B. OberMaier, C. Guger, C. Neuper, G. Pfurtscheller, Hidden Markov models for online classification of single trial EEG data, Pattern Recognition Letters, 22 (2001), 1299-1309
  • Cheng-Jian Lin, Ming-HuaHsieh, Classification of mental task from EEG data using neural networks based on particle swarm optimization, Neurocomputing, 72 (2009), 1121-1130
  • Dan Nemrodov, Matthias Niemeier, Ashutosh Patel, Adrian Nestor. The Neural Dynamics of Facial Identity Processing: insights from EEG-Based Pattern Analysis and Image Reconstruction. eNeuro 29 January 2018, ENEURO.0358-17.2018; DOI: 10.1523/ENEURO.0358-17.2018
  • Iñaki Iturrate, Ricardo Chavarriaga, Luis Montesano, Javier Minguez & José del R. Millán, Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control, Scientific Reports volume 5, (2015)
  • H. U. Amin, W. Mumtaz, A. R. Subhani, M. N. M. Saad and A. S. Malik, Classification of EEG Signals Based on Pattern Recognition Approach, Front. Comput. Neurosci. 11:103. doi: 10.3389/fncom.2017.00103
  • V. N. Vapnik, An Overview of Statistical Learning Theory, IEEE Transactions on Neural Networks Vol(10) No. 5, 1999
  • J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81–106, 1986
  • Xiao-Wei Wang , Dan Nie, Bao-Liang Lu, Emotional state classification from EEG data using machine learning approach, Neurocomputing 129 (2014) 94–106
  • David Gutierrez, Diana I. Escalona-Vargas, EEG data classification through signal spatial redistribution and optimized linear discriminants, Computer Methods and Programs in Biomedicine 97 (2010) 39–47
  • E.Parvinnia, M.Sabeti, M.Zolghadri Jahromi, R.Boostani, Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm, Journal of King Saud University - Computer and Information Sciences, Volume 26, Issue 1, January 2014, Pages 1-6
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Adil Deniz Duru 0000-0003-3014-9626

Yayımlanma Tarihi 31 Mart 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 31 Sayı: 1

Kaynak Göster

APA Duru, A. D. (2019). Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques. International Journal of Advances in Engineering and Pure Sciences, 31(1), 47-52. https://doi.org/10.7240/jeps.459420
AMA Duru AD. Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques. JEPS. Mart 2019;31(1):47-52. doi:10.7240/jeps.459420
Chicago Duru, Adil Deniz. “Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques”. International Journal of Advances in Engineering and Pure Sciences 31, sy. 1 (Mart 2019): 47-52. https://doi.org/10.7240/jeps.459420.
EndNote Duru AD (01 Mart 2019) Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques. International Journal of Advances in Engineering and Pure Sciences 31 1 47–52.
IEEE A. D. Duru, “Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques”, JEPS, c. 31, sy. 1, ss. 47–52, 2019, doi: 10.7240/jeps.459420.
ISNAD Duru, Adil Deniz. “Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques”. International Journal of Advances in Engineering and Pure Sciences 31/1 (Mart 2019), 47-52. https://doi.org/10.7240/jeps.459420.
JAMA Duru AD. Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques. JEPS. 2019;31:47–52.
MLA Duru, Adil Deniz. “Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques”. International Journal of Advances in Engineering and Pure Sciences, c. 31, sy. 1, 2019, ss. 47-52, doi:10.7240/jeps.459420.
Vancouver Duru AD. Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques. JEPS. 2019;31(1):47-52.