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<p>In this paper, a BCI-based machine learning method is proposed to predict the attention lapse of the driver. An EEG headset is used to capture brain activity from different regions of the human brain. The data is further pre-processed and various machine learning classifiers such as KNN, decision tree, and naive Bayes are trained using the data to classify the attention state of the driver as in-attention, attention lapse or attention regain. The KNN model is the best performer showing 97% accuracy, wherein the true positive rate (TPR) of 85.5% is measured to predict attention lapse and 82% to predict attention regain states. Besides this, a sensor-based model is also proposed to avert the vehicle’s collision by alerting the driver about the attention lapse.</p>

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Method to avert vehicle collision based on driver’s attention lapse predicted using BCI

  • 期刊论文

  • 发表日期：

    2025年4月12日

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  • Unnati Mishra,
  • Hitesh Chauhan,
  • Madhuri Maru,
  • Saurin Parikh

摘要

In this paper, a BCI-based machine learning method is proposed to predict the attention lapse of the driver. An EEG headset is used to capture brain activity from different regions of the human brain. The data is further pre-processed and various machine learning classifiers such as KNN, decision tree, and naive Bayes are trained using the data to classify the attention state of the driver as in-attention, attention lapse or attention regain. The KNN model is the best performer showing 97% accuracy, wherein the true positive rate (TPR) of 85.5% is measured to predict attention lapse and 82% to predict attention regain states. Besides this, a sensor-based model is also proposed to avert the vehicle’s collision by alerting the driver about the attention lapse.