Decoding Visual Recognition of Objects from EEG Signals based on Attention-Driven Convolutional Neural Network

Jenifer Kalafatovich, Minji Lee, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The ability to perceive and recognize objects is fundamental for the interaction with the external environment. Studies that investigate them and their relationship with brain activity changes have been increasing due to the possible application in an intuitive brain-machine interface (BMI). In addition, the distinctive patterns when presenting different visual stimuli that make data differentiable enough to be classified have been studied. However, reported classification accuracy still low or employed techniques for obtaining brain signals are impractical to use in real environments. In this study, we aim to decode electroencephalography (EEG) signals depending on the provided visual stimulus. Subjects were presented with 72 photographs belonging to 6 different semantic categories. We classified 6 categories and 72 exemplars according to visual stimuli using EEG signals. In order to achieve a high classification accuracy, we proposed an attention driven convolutional neural network and compared our results with conventional methods used for classifying EEG signals. We reported an accuracy of 50.37 ± 6.56% and 26.75 ± 10.38% for 6-class and 72-class, respectively. These results statistically outperformed other conventional methods. This was possible because of the application of the attention network using human visual pathways. Our findings showed that EEG signals are possible to differentiate when subjects are presented with visual stimulus of different semantic categories and at an exemplar-level with a high classification accuracy; this demonstrates its viability to be applied it in a real-world BMI.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2985-2990
Number of pages6
Volume2020-October
ISBN (Electronic)9781728185262
DOIs
Publication statusPublished - 2020 Oct 11
Event2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada
Duration: 2020 Oct 112020 Oct 14

Conference

Conference2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
CountryCanada
CityToronto
Period20/10/1120/10/14

Keywords

  • attention
  • brain-machine interface
  • convolutional neural network (CNN)
  • electroencephalography (EEG)
  • visual recognition

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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