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Attentional load classification in multiple object tracking task using optimized support vector machine classifier: a

Sweeti1

  • 1Medical Electronics Engineering Department, M. S. Ramaiah Institute of Technology, Bangalore, India.

Journal of Medical Engineering & Technology
|November 26, 2021
PubMed
Summary

This study demonstrates classifying attentional load using electroencephalograph (EEG) signals from a multiple object tracking task. Support vector machine (SVM) classification achieved high accuracy, showing potential for cognitive brain-computer interfaces (cBCIs) in neurorehabilitation.

Keywords:
Multiple object tracking (MOT) taskattentional loadclassificationelectroencephalograph (EEG)

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Cognitive brain-computer interfaces (cBCIs) are emerging for neurorehabilitation and performance monitoring.
  • cBCIs utilize cognitive brain signals, requiring less effort than motor BCIs, but currently have lower accuracy.
  • Attention is a key cognitive signal for cBCI realization.

Purpose of the Study:

  • To explore the application of Support Vector Machine (SVM) for classifying attentional load.
  • To utilize the multiple object tracking (MOT) task for acquiring electroencephalograph (EEG) signals.
  • To assess the potential of spectral entropy EEG features for attention classification.

Main Methods:

  • Healthy subjects performed the multiple object tracking (MOT) task.
  • Electroencephalograph (EEG) signals were recorded during the MOT task.
  • Support Vector Machine (SVM) classifier was employed to classify attentional load using spectral entropy features.

Main Results:

  • Attentional load was classified with high performance using SVM.
  • Sensitivity, specificity, and accuracy reached 94.03%, 92.50%, and 93.28%, respectively.
  • Spectral entropy was identified as an effective EEG feature for this classification.

Conclusions:

  • The SVM classification approach shows significant promise for cBCI applications.
  • This method could be valuable for neurorehabilitation by monitoring attentional states.
  • Further development of this technique could enhance cBCI accuracy and utility.