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A Survey on Emotion Recognition from EEG Signals for Autism Spectrum Disorder

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
N. Mohanapriya, L. Malathi, B. Revathi

N Mohanapriya, L Malathi and B Revathi. A Survey on Emotion Recognition from EEG Signals for Autism Spectrum Disorder. International Journal of Computer Applications 180(20):32-37, February 2018. BibTeX

	author = {N. Mohanapriya and L. Malathi and B. Revathi},
	title = {A Survey on Emotion Recognition from EEG Signals for Autism Spectrum Disorder},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2018},
	volume = {180},
	number = {20},
	month = {Feb},
	year = {2018},
	issn = {0975-8887},
	pages = {32-37},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2018916474},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Children with Autism Spectrum Disorder (ASD) cannot express their emotions explicitly; this makes it difficult for the parents and caretakers associated with these children to understand the child’s behavior, leading to a major setback in the child’s early developmental stages. To identify the autism for child initial stages can help early diagnosis. Delayed detection of child autism leads to incurable. This paper analysis the existing works on detection of autism spectrum disorder from EEG signal. Various filtering technique and classification are presented. The experiment for were conducted for support vector machine (SVM), k-nearest neighbor (KNN), linear discriminant analysis (LDA), deep learning, Naive Bayes, Random Forest, deep-learning classification algorithms. Here the deep learning algorithm gives better results for autism recognition with the emotions such as happy, calm, anger and scared. As the no of medical records increases the conventional techniques is not suitable for handle large number data.


  1. Niranjana Krupa, Karthik Anantharam, Manoj Sanker Sameer Datta John Vijay Sagar, “Recognition of emotions in autistic children uses physiological signals”, in Received: 21 July 2015 /Accepted: 8 March 2016.
  2. K. G. Smitha A. P. Vinod, “Facial emotion recognition system for autistic children: a feasible study based on FPGA implementation”, International Federation for Medical and Biological Engineering 2015.
  3. Felix Albu, Daniela Hagiescu, Liviu Vladutu, Mihaela-Alexandra Puica, “Neural Network Approaches For Children’s Emotion Recognition In Intelligent Learning Applications”, Proceedings of edulearn15 Conference 6th-8th July 2015.
  4. Paul Fergus, Basma Abdulaimma, Chris Carter, Sheena Round, “Interactive Mobile Technology for Children with Autism Spectrum Condition (ASC)”, in 2014.
  5. Keiran M. Rump, Joyce L. Giovannelli, Nancy J. Minshew, Mark S. Strauss, “The Development of Emotion Recognition in Individuals with Autism”, Child Development, September/October 2009, Volume 80, Number 5, Pages 1434–1447.
  6. Raja Majid Mehmood, Ruoyu Du and Hyo Jong Lee, “Optimal feature selection and Deep Learning Ensembles Method for emotion recognition from human brain EEG sensors”, journal of Citation information: DOI 10.1109/ACCESS.2017.2724555, IEEE.
  7. Yongbin Gaol, Hyo Jong Lee, Raja Majid Mehmood, “Deep Learning Of EEG Signals For Emotion Recognition”, in IEEE International Conference, 2015.
  8. Olga Sourina, Yisi Liu, “A Fractal-Based Algorithm of Motion Recognition from EEG Using Arousal-Alence ModeL”, in 2011.
  9. Panagiotis C. Petrantonakis, Leontios J. Hadjileontiadis, “Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis”, IEEE transactions on affective computing, vol. 1, no. 2, 2010.
  10. Pascal Ackermann, Christian Kohlschein, Jo´ A´ gila Bitschx, Klaus Wehrlex and Sabina Jeschke, “EEG-based Automatic Emotion Recognition: Feature Extraction, Selection and Classification Methods”, in InternationalConference,2016.
  11. Aravind E Vijayan, Deepak Sen, Sudheer A.P “EEG-based Emotion Recognition using Statistical measures and Auto-regressive modeling”, 2015 IEEE International Conference on Computational Intelligence & Communication Technology, 978-1-4799-6023-1/15 $31.00 © 2015 IEEE.


EEG Signal, Emotion Recognition, Autism Spectrum Disorder, Deep Learning, classification