Object shape recognition from EEG signals during tactile and visual exploration

Anwesha Khasnobish, Amit Konar, D. N. Tibarewala, Saugat Bhattacharyya, R. Janarthanan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)

Abstract

Humans understand the world around us by visual and tactile exploration of the objects. The objective of this paper is to recognize the object-shapes from EEG signals while the subjects are exploring the same by visual and tactile means. The various object shapes are classified from electroencephalogram (EEG) signals that are stimulated by only tactile, only visual and by both means. EEG signals were acquired and analyzed from six electrodes, namely F3,F4,FC5,FC6,O1 and O2, where each pair of electrodes are located on frontal, somato-sensory and occipital region of the brain responsible for cognitive processing, tactile and visual perception. Mu-desynchronization in alpha and beta bands is used as the EEG modality for this purpose. Power spectral density (PSD) features are extracted and classified using support vector machine (SVM) classifiers in their corresponding object-shape classes. The results showed that object-shapes are best classified from EEG signals during only tactile exploration. The object shapes classified from EEG signals during only tactile exploration yielded highest mean classification accuracy of 88.34%. The average classification accuracy over all three object exploration modalities is 83.89%.

Original languageEnglish
Title of host publicationPattern Recognition and Machine Intelligence - 5th International Conference, PReMI 2013, Proceedings
Pages459-464
Number of pages6
ISBN (Electronic)978-3-642-45062-4
DOIs
Publication statusPublished (in print/issue) - 1 Dec 2013
Event5th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2013 - Kolkata, India
Duration: 10 Dec 201314 Dec 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8251 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2013
Country/TerritoryIndia
CityKolkata
Period10/12/1314/12/13

Keywords

  • Electroencephalogram
  • Object-shape recognition
  • Power spectral density
  • Support vector machine
  • Tactile perception
  • Visual perception

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