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Classification of Lactate Level Using Resting-State EEG Measurements.

Saad Abdulazeez Shaban1,2, Osman Nuri Ucan3, Adil Deniz Duru4

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Applied Bionics and Biomechanics
|February 25, 2021
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Summary
This summary is machine-generated.

This study uses Fast Fourier Transform (FFT) on electroencephalography (EEG) signals to accurately predict tiredness by estimating lactate enzyme levels. The method achieved over 98% accuracy in classifying athlete fatigue states.

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Area of Science:

  • Neuroscience and Biomedical Engineering
  • Application of signal processing in human physiology

Background:

  • Electroencephalography (EEG) signals are crucial for understanding brain dynamics.
  • Machine and deep learning techniques are increasingly applied to analyze complex neural data.
  • Assessing physiological states like fatigue is vital in sports and performance monitoring.

Purpose of the Study:

  • To develop and validate a system for classifying human tiredness using resting-state EEG signals.
  • To employ Fast Fourier Transform (FFT) as a feature extraction method for EEG signal analysis.
  • To predict lactate enzyme levels (high or low) as an indicator of fatigue.

Main Methods:

  • Resting-state EEG signals were recorded from elite athletes before and after acute exercise.
  • Fast Fourier Transform (FFT) was applied to extract band power features from EEG recordings.
  • Extracted features were used to train and evaluate three different classifiers, including K-nearest neighbor (KNN).

Main Results:

  • The proposed system successfully classified EEG signals with high accuracy.
  • The K-nearest neighbor (KNN) classifier achieved an accuracy exceeding 98%.
  • The method demonstrated the ability to accurately predict high or low lactate enzyme levels.

Conclusions:

  • The FFT-based feature extraction scheme is effective for classifying EEG signals and predicting fatigue-related lactate levels.
  • This approach holds potential for applications in Brain-Computer Interfaces (BCI) and the Internet of Things (IoT).
  • Accurate fatigue prediction can inform crucial decision-making in performance and health monitoring.