Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
A Study of the Correlation Between Internet Addiction and Aggressive Behaviour Among the Namibian University Students Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Emotional State Recognition with EEG Signals Using Subject Independent Approach

Authors : Pallavi Pandey, K. R. Seeja

Published in: Data Science and Big Data Analytics

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

EEG signals vary from human to human and hence it is very difficult to create a subject independent emotion recognition system. Even though subject dependent methodologies could achieve good emotion recognition accuracy, the subject-independent approaches are still in infancy. EEG is reliable than facial expression or speech signal to recognize emotions, since it can not be fake. In this paper, a Multilayer Perceptron neural network based subject-independent emotion recognition system is proposed. Performance evaluation of the proposed system, on the benchmark DEAP dataset shows good accuracy compared to the state of the art subject independent methods.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Effective Healthcare Services by IoT-Based Model of Voluntary Doctors
next chapter Development of Early Prediction Model for Epileptic Seizures
Literature
1.
Paul S, Mazumder A, Ghosh P, Tibarewala DN, Vimalarani G (2015) EEG based emotion recognition system using MFDFA as feature extractor. In: International conference on robotics, automation, control and embedded systems (RACE) IEEE, pp 1–5
2.
Lahane P, Sangaiah AK (2015) An approach to EEG based emotion recognition and classification using kernel density estimation. In: International conference on intelligent computing, communication and convergence (ICCC-2015), Odisha, India. Procedia Comput Sci 48:574–581
3.
Singh M, Sing M, Gangwar S (2013) Emotion recognition using electroencephalography (EEG): a review. IJITKM 7(1):1–5
4.
Soleymani M, Asghari-Esfeden S, Pantic M, Fu Y (2014) Continuous emotion detection using EEG signals and facial expressions. In: IEEE international conference on multimedia and expo (ICME), pp 1–6
5.
Abhang P, Rao S, Gawali BW, Rokade P (2011) Emotion recognition using speech and EEG signal—a review. Int J Comput Appl 15(3):0975–8887
6.
Chen J, Hu B, Moore P, Zhang X, Ma X (2015) Electroencephalogram based emotion assessment system using ontology and data mining technique. Appl Soft Comput 30:663–674CrossRef
7.
Gupta R, Laghari KR, Falk TH (2016) Relevance vector classifier decision fusion and EEG graph-theoretic features for automatic affective state characterization. Neurocomputing 174:875–884CrossRef
8.
Atkinson J, Campos D (2016) Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers. Expert Syst Appl 47:35–41CrossRef
9.
Bozhkov L, Georgieva P, Santos I, Pereira A, Silva C (2015) EEG-based subject independent affective computing models. In: INNS conference on big data. Procedia Comput Sci 53:375–382
10.
Lan Z, Sourina O, Wang L, Liu Y (2016) Real-time EEG-based emotion monitoring using stable features. Vis Comput 32(3):347–358CrossRef
11.
Purnamasari PD, Ratna AAP, Kusumoputro B (2017) Development of filtered bispectrum for EEG signal feature extraction in automatic emotion recognition using artificial neural networks. Algorithms 10(2):63MathSciNetCrossRef
12.
Gómez A, Quintero L, López N, Castro J, Villa L, Mejía G (2017) An approach to emotion recognition in single-channel EEG signals using stationary wavelet transform. In: VII Latin American congress on biomedical engineering CLAIB 2016, Bucaramanga, Santander, Colombia. Springer, Singapore, pp 654–657
13.
Zhuang N, Zeng Y, Tong L, Zhang C, Zhang H, Yan B (2017) Emotion recognition from EEG signals using multidimensional information in EMD domain. BioMed Res Int
14.
Yohanes RE, Ser W, Huang GB (2012) Discrete wavelet transform coefficients for emotion recognition from EEG signals. In: 2012 annual international conference of the engineering in medicine and biology society (EMBC). IEEE, pp 2251–2254
15.
Nakate A, Bahirgonde PD (2015) Feature extraction of EEG signal using wavelet transform. Int J Comput Appl 124(2):0975–8887
16.
Koelstra S, Muhl C, Soleymani M, Lee J, Yazdani A, Ebrahimi T, Pun T, NIjhilt A, Patras I (2012) DEAP: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comput 3(1):18–31
17.
Russell JA (1980) A circumplex model of affect. J Pers Soc Psychol 39(6):1161–1178CrossRef
18.
Bradley MM, Lang PJ (1994) Measuring emotion: the self-assessment manikin and the semantic differential. J Behav Ther Exp Psychiatry 25(1):49–59CrossRef
19.
20.
Zhang J, Chen M, Zhao S, Hu S, Shi Z, Cao Y (2016) ReliefF-based EEG sensor selection methods for emotion recognition. Sensors 16(10):1558CrossRef
21.
Jirayucharoensak S, Pan-Ngum S, Israsena P (2014) EEG-based emotion recognition using deep learning network with principal component based covariate shift adaption. World Sci J 10 pp. Article ID: 627892
Metadata
Title
Emotional State Recognition with EEG Signals Using Subject Independent Approach
Authors
Pallavi Pandey
K. R. Seeja
Copyright Year
2019
Publisher
Springer Singapore
Book
Data Science and Big Data Analytics

Print ISBN: 978-981-10-7640-4

Electronic ISBN: 978-981-10-7641-1

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-10-7641-1_10

Premium Partner

    Image Credits