Classifying brain activities in perception of shapeanalogous english letters based on EEG signal

Rohit Bose, Sim Kuan Goh, Kian F. Wong, Nitish Thakor, Anastasios Bezerianos, Junhua Li

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

Abstract

Brain computer interface (BCI) technique has been demonstrated that human intentions or stimulus perception can be recognized using EEG signal recorded from the human scalp. When an intention is initiated in the brain or an external stimulus is perceived, the underlying relevant processing alters brain activity. This alteration in brain activity can be reflected in EEG signal. The intention or stimulus perception is therefore classified based on the alteration in brain activity. It might be difficult to classify brain activities in the perception of shape-analogous English letters because the similar shape could lead to less difference in brain activity. In order to explore classification feasibility and classification performance of shape-analogous letters using EEG signal, we performed an experiment of shape-analogous letter perception, in where participants perceived four letters (i.e., O', O', O' and O') while EEG signal was recorded. The F-score method was employed to assess the discriminative power for each feature, and a subgroup of features with high discriminative powers was then selected and fed into classifiers. Five classifiers (i.e., k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF) and AdaBoost (ADA)), which are either pervasive or advanced in the field of machine learning, were utilized to classify brain activities in perception of shape-analogous letters. For each classifier, its parameters and the number of used features were optimized. Based on the performance comparison among the classifiers, Random Forest (RF) classifier achieved a maximal accuracy of 74.1%, but it was not statistically significantly better than the SVM. Our study demonstrated that brain activities in perception of shape-analogous English letters can be classified based on EEG signal and showed that random forest classifier outperformed other classifiers according to the results of comparison.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 International Conference on Cyberworlds, CW 2018
EditorsAlexei Sourin, Olga Sourina, Marius Erdt, Christophe Rosenberger
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages184-190
Number of pages7
ISBN (Electronic)9781538673157
DOIs
StatePublished - Dec 26 2018
Event17th International Conference on Cyberworlds, CW 2018 - Singapore, Singapore
Duration: Oct 3 2018Oct 5 2018

Publication series

NameProceedings - 2018 International Conference on Cyberworlds, CW 2018

Conference

Conference17th International Conference on Cyberworlds, CW 2018
CountrySingapore
CitySingapore
Period10/3/1810/5/18

Keywords

  • AdaBoost
  • Classification
  • EEG
  • Fscore
  • K-nearest neighbors
  • Linear discriminant classifier
  • Random Forest
  • Shape analogous letters
  • Support vector machine

ASJC Scopus subject areas

  • Signal Processing
  • Modeling and Simulation
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Classifying brain activities in perception of shapeanalogous english letters based on EEG signal'. Together they form a unique fingerprint.

Cite this