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With the advancement of education system towards online platforms, there has been ease of accessibility to the best of educational content but there has been a challenge to access and address students’ level of understanding of the lecture. It has been proved over years of research that electroencephalography (EEG) can be used to classify the subject’s mental state throughout a specific length of time. This paper seeks to determine the cognitive state of subject learning through an online lecture with the support vector machine (SVM) classifier and the EEG signals. The SVM technique has recorded an accuracy of 92% (approx.) on the distance learning dataset.

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EEG-Based Cognitive State Classification on Distance Learning Dataset Using SVM

  • 会议论文

  • 发表日期：

    2026年

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  • Shweta Sharma,
  • Rajneet Kaur Bijral,
  • Ayushi Kotwal,
  • Jatinder Manhas,
  • Vinod Sharma

摘要

With the advancement of education system towards online platforms, there has been ease of accessibility to the best of educational content but there has been a challenge to access and address students’ level of understanding of the lecture. It has been proved over years of research that electroencephalography (EEG) can be used to classify the subject’s mental state throughout a specific length of time. This paper seeks to determine the cognitive state of subject learning through an online lecture with the support vector machine (SVM) classifier and the EEG signals. The SVM technique has recorded an accuracy of 92% (approx.) on the distance learning dataset.