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Processing of raw Electroencephalography (EEG) data is a crucial step in EEG signal processing and Machine Learning (ML) analysis. EEG signals are noisy and often contaminated by various artifacts. A standard preprocessing analysis of EEG for ML involves several key stages, which include: (i) prepare dataset, (ii) channel selection, (iii) Signal to Noise Ratio (SNR) measurements, etc. This chapter explains the key steps involved in data preparation for EEG-ML analysis.

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Dataset Preprocessing

  • 章节

  • 发表日期：

    2026年

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  • Ildar Rakhmatulin,
  • Ganesh R. Naik

摘要

Processing of raw Electroencephalography (EEG) data is a crucial step in EEG signal processing and Machine Learning (ML) analysis. EEG signals are noisy and often contaminated by various artifacts. A standard preprocessing analysis of EEG for ML involves several key stages, which include: (i) prepare dataset, (ii) channel selection, (iii) Signal to Noise Ratio (SNR) measurements, etc. This chapter explains the key steps involved in data preparation for EEG-ML analysis.