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Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
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Wavelet Entropy-Based Inter-subject Associative Cortical Source Localization for Sensorimotor BCI.

Simanto Saha1,2, Md Shakhawat Hossain2, Khawza Ahmed2

  • 1School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia.

Frontiers in Neuroinformatics
|August 10, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for estimating brain activity from electroencephalography (EEG) for subject-independent brain-computer interfaces (BCI). The approach enhances motor imagery prediction accuracy by selecting relevant EEG channels.

Keywords:
brain computer interfaceelectroencephalographyinter-subject sensorimotor dynamicsmotor imagerywavelet based maximum entropy on the mean

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Brain-computer interfaces (BCI) enable communication and control through brain activity.
  • Subject-independent BCI systems are challenging due to inter-individual variability in neural signals.
  • Motor imagery tasks are commonly used in BCI research for control signals.

Purpose of the Study:

  • To develop and evaluate a subject-independent method for estimating event-related cortical sources from electroencephalography (EEG) for motor imagery BCI.
  • To improve prediction accuracy in motor imagery BCI by selecting task-specific EEG channels.

Main Methods:

  • Wavelet-based maximum entropy on the mean (wMEM) was used for channel selection.
  • Common Spatial Pattern (CSP) and Regularized Common Spatial Pattern (RCSP) algorithms were employed for feature extraction.
  • A two-layer feed-forward neural network was used for classification, trained and tested on data from different subjects.

Main Results:

  • The proposed method using selected EEG channels achieved higher prediction accuracies compared to using all channels.
  • Highest mean prediction accuracy reached (90.36±5.59)% using selected channels, outperforming all channels (86.07 ± 10.71)%.
  • RCSP with selected channels yielded slightly better results (71.41±6.65)% than with all channels (71.20±5.32)%.

Conclusions:

  • Spatially projected cortical sources approximated using wMEM can capture inter-subject sensorimotor brain dynamics.
  • The proposed channel selection method enhances the performance of subject-independent motor imagery BCI.
  • This approach shows promise for developing more robust and effective subject-independent BCI systems.