Performance Analysis of Feature Extractors for Object Recognition from EEG Signals View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

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

ABSTRACT

Recognition of objects from EEG signals requires selection of appropriate feature extraction and classification techniques with best efficiency in terms of highest classification accuracy with lowest run time for its applications in real time. The objective of this paper is to analyze the performance of various feature extraction techniques and to choose that particular method which can be implemented in real time system with best efficiency. The EEG signals are acquired from subjects while they explored the objects visually and visuo-tactually. Thus acquired EEG signals are preprocessed followed by feature extraction using adaptive autoregressive (AAR) parameters, ensemble empirical mode decomposition (EEMD), approximate entropy (ApEn) and multi-fractal detrended fluctuation analysis (MFDFA). The performance of these features are analyzed in terms of their dimension, extraction time and also depending upon the classification results produced by three classifiers [Support Vector machine (SVM), Naïve Bayesian (NB), and Adaboost (Ada)] independently according to classification accuracy, sensitivity and classification times. The experimental results show that AAR parameter has an optimum dimension of 36 (not too large like EEMD i.e. 7,680 or too small like ApEn i.e. 6) and required minimum extraction as well as classification time of 0.59 and 0.008 s respectively. AAR also yielded highest maximum classification accuracy and sensitivity of 80.95 and 92.31 % respectively with NB classifier. Thus AAR parameters can be chosen for real time object recognition from EEG signal along with Naïve Bayesian classifier. More... »

PAGES

249-261

References to SciGraph publications

  • 2013. Object Shape Recognition from EEG Signals during Tactile and Visual Exploration in PATTERN RECOGNITION AND MACHINE INTELLIGENCE
  • 2005. Brain—Computer Interface in NEURAL ENGINEERING
  • Book

    TITLE

    Advancements of Medical Electronics

    ISBN

    978-81-322-2255-2
    978-81-322-2256-9

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-81-322-2256-9_23

    DOI

    http://dx.doi.org/10.1007/978-81-322-2256-9_23

    DIMENSIONS

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