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<p>This study explores how to improve the detection of Error-Related Potentials (ErrPs), namely brain signals generated when a person perceives an unexpected action performed by an interacting agent. ErrPs are promising for improving interactions between humans and robots because they offer a way for robots to understand the user’s needs and expectations without explicit input. The proposed method aims at characterizing ErrP signals using a wide set of features extracted from electroencephalography (EEG) data, collected from subjects performing different tasks. This feature-based method results more accurate and efficient than traditional approaches, especially when applied to multiple users, or across different experimental setups. This work paves the way to feature-based ErrP detection to enhance human-robot interaction in dynamic environments.</p>

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Error-related potentials in EEG signals: feature-based detection for human-robot interaction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月8日

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  • Alessandra Fava,
  • Valeria Villani,
  • Lorenzo Sabattini

摘要

This study explores how to improve the detection of Error-Related Potentials (ErrPs), namely brain signals generated when a person perceives an unexpected action performed by an interacting agent. ErrPs are promising for improving interactions between humans and robots because they offer a way for robots to understand the user’s needs and expectations without explicit input. The proposed method aims at characterizing ErrP signals using a wide set of features extracted from electroencephalography (EEG) data, collected from subjects performing different tasks. This feature-based method results more accurate and efficient than traditional approaches, especially when applied to multiple users, or across different experimental setups. This work paves the way to feature-based ErrP detection to enhance human-robot interaction in dynamic environments.