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Electroencephalography Signal Analysis for Human Activities Classification: A Solution Based on Machine Learning and

Tarciana C de Brito Guerra1, Taline Nóbrega1, Edgard Morya2

  • 1Graduate Program in Electrical and Computer Engineering (PPgEEC), Federal University of Rio Grande do Norte, Natal 59078-970, Brazil.

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|May 13, 2023
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Summary
This summary is machine-generated.

This study developed a Random Forest machine learning model to classify electroencephalography (EEG) signals for brain-computer interfaces (BCIs). The model effectively distinguishes real and imagined motor activities, even with consumer-grade EEG devices.

Keywords:
EEGV-AMPmachine learningmindwavemotor imageryrandom forest

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Electroencephalography (EEG) measures brain electrical activity, crucial for understanding motor functions.
  • Brain-Computer Interfaces (BCIs) leverage EEG signals for assistive technologies, particularly for individuals with motor impairments.
  • Extracting meaningful patterns from complex EEG signals often requires advanced algorithms like machine learning (ML).

Purpose of the Study:

  • To develop and evaluate a Random Forest (RF) based ML algorithm for classifying EEG signals during real and imagined motor activities.
  • To assess the performance of the RF algorithm using both consumer-grade and research-grade EEG systems.
  • To explore the potential of cognitive process-controlled tools through accurate EEG signal interpretation.

Main Methods:

  • Implementation of a Random Forest (RF) machine learning algorithm.
  • Classification of EEG signals recorded during real and imagery motor tasks.
  • Evaluation of the RF algorithm's efficacy using data from both consumer and research-grade EEG devices.

Main Results:

  • The Random Forest algorithm demonstrated efficiency in distinguishing between real and imagined motor activities.
  • The algorithm could accurately identify the body part associated with the motor activity, even when using a consumer-grade EEG system.
  • Interpersonal variability in EEG signals was identified as a factor negatively impacting classification accuracy.

Conclusions:

  • The developed Random Forest ML model shows promise for BCI applications, enabling the classification of motor imagery and real motor execution.
  • Consumer-grade EEG systems can be viable for BCI development with appropriate ML algorithms like Random Forest.
  • Addressing interpersonal variability in EEG signals is essential for further improving BCI performance and reliability.