Conference Paper2017

Effect of EMG artifacts on video category classification from EEG

Mohammad Raihanul Bashar, Rayhan Sardar Tipu, Aunnoy K Mutasim, M Ashraful Amin

2017 6th International Conference on Informatics, Electronics and Vision & 2017 7th International Symposium in Computational Medical and Health Technology (ICIEV-ISCMHT)

IEEE, pp. 1–4

DOI: 10.1109/iciev.2017.8338603URL

Abstract

EEG (electroencephalography) signals are highly susceptible to noise. Mixture of artifacts like EOG (electrooculography) and EMG (electromyography) with the EEG signals are inevitable. In this paper, we present our findings of the effects of EMG artifacts on EEG signals to categorize videos. The experiments suggest that for video category classification using EEG, signals with EMG artifacts have more discriminating capacity.

Keywords

  • EMG artifacts
  • Electrooculography
  • EEG signals
  • EEG classification

CCDS Authors

Mohammad Raihanul Bashar
Mohammad Raihanul Bashar

Mohammad Raihanul Bashar

  • Research Assistant, CCDS

MS Student

Concordia University

Research Interests

Extended Reality, Human-Computer Interaction, 3D user interface

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

References

  1. 1.Tzyy‐Ping Jung, Scott Makeig, Colin Humphries, Te‐Won Lee, Martin J. McKeown, Vicente J. Iragui, Terrence J. Sejnowski. (2000). Removing electroencephalographic artifacts by blind source separation. Psychophysiology, 37(2), 163–178[10.1111/1469-8986.3720163]
  2. 2.José Antonio Urigüen, Begonya García-Zapirain. (2015). EEG artifact removal—state-of-the-art and guidelines. Journal of Neural Engineering, 12(3), 031001[10.1088/1741-2560/12/3/031001]
  3. 3.Tzyy‐Ping Jung, Scott Makeig, Colin Humphries, TE-WON LEE, Martin J. McKeown, VICENTE IRAGUI, Terrence J. Sejnowski. (2000). Removing electroencephalographic artifacts by blind source separation. Psychophysiology, 37(2), 163–178[10.1017/s0048577200980259]
  4. 4.Irina I. Goncharova, Dennis J. McFarland, Theresa M. Vaughan, Jonathan R. Wolpaw. (2003). EMG contamination of EEG: spectral and topographical characteristics. Clinical Neurophysiology, 114(9), 1580–1593[10.1016/s1388-2457(03)00093-2]
  5. 5.Andrew Myrden, Tom Chau. (2015). Effects of user mental state on EEG-BCI performance. Frontiers in Human Neuroscience, 9, 308[10.3389/fnhum.2015.00308]
  6. 6.Hoang-Anh The Nguyen, J. Musson, Feng Li, Wei Wang, Guangfan Zhang, Roger Xu, Carl Richey, Tom Schnell, Frederic D. McKenzie, Jiang Li. (2012). EOG artifact removal using a wavelet neural network. Neurocomputing, 97, 374–389[10.1016/j.neucom.2012.04.016]
  7. 7.Giuseppe Giglia, Filippo Brighina, Silvia Rizzo, Angela Puma, Serena Indovino, Simona Maccora, Roberta Baschi, Giuseppe Cosentino, Brigida Fierro. (2015). Anodal transcranial direct current stimulation of the right dorsolateral prefrontal cortex enhances memory-guided responses in a visuospatial working memory task.. PubMed, 29(3), 189–93[link]
  8. 8.Maher Abujelala, Cheryl Abellanoza, Aayush Sharma, Fillia Makedon. (2016). Brain-EE. , 1–5[10.1145/2910674.2910691]
  9. 9.Vangelis P. Oikonomou, Georgios Liaros, Kostantinos Georgiadis, Elisavet Chatzilari, Katerina Adam, Spiros Nikolopoulos, Ioannis Kompatsiaris. (2016). Comparative Evaluation Of State-Of-The-Art Algorithms For Ssvep-Based Bcis. arXiv (Cornell University)[10.5281/zenodo.581153]
  10. 10.Thrasyvoulos Karydis, Filipe Aguiar, Simmie L. Foster, Andreas Mershin. (2015). Performance characterization of self-calibrating protocols for wearable EEG applications. , 1–7[10.1145/2769493.2769533]
  11. 11.Shuvo Kumar Paul, Md. S. Q. Zulkar Nine, Mahady Hasan, M. Ashraful Amin. (2015). Cognitive Task Classificaiton from Wireless EEG. Lecture notes in computer science, 13–22[10.1007/978-3-319-23344-4_2]
  12. 12.Hassan Albalawi, Xiaomu Song. (2012). A study of kernel CSP-based motor imagery brain computer interface classification. , 1–4[10.1109/spmb.2012.6469465]
  13. 13.Samuel Boudet, Laurent Peyrodie, Philippe Gallois, Christian Vasseur. (2006). A global approach for automatic artifact removal for standard EEG record. , 2, 5719–5722[10.1109/iembs.2006.259237]
  14. 14.Vangelis P. Oikonomou, Georgios Liaros, Kostantinos Georgiadis, Elisavet Chatzilari, Katerina Adam, Spiros Nikolopoulos, Ioannis Kompatsiaris. (2016). Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs. arXiv (Cornell University)[10.48550/arxiv.1602.00904]
← All publications