@inproceedings{katada-etal-2024-collecting,
    title = "Collecting Human-Agent Dialogue Dataset with Frontal Brain Signal toward Capturing Unexpressed Sentiment",
    author = "Katada, Shun  and
      Takeda, Ryu  and
      Komatani, Kazunori",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.312",
    pages = "3518--3528",
    abstract = "Multimodal information such as text and audiovisual data has been used for emotion/sentiment estimation during human-agent dialogue; however, user sentiments are not necessarily expressed explicitly during dialogues. Biosignals such as brain signals recorded using an electroencephalogram (EEG) sensor have been the subject of focus in affective computing regions to capture unexpressed emotional changes in a controlled experimental environment. In this study, we collect and analyze multimodal data with an EEG during a human-agent dialogue toward capturing unexpressed sentiment. Our contributions are as follows: (1) a new multimodal human-agent dialogue dataset is created, which includes not only text and audiovisual data but also frontal EEGs and physiological signals during the dialogue. In total, about 500-minute chat dialogues were collected from thirty participants aged 20 to 70. (2) We present a novel method for dealing with eye-blink noise for frontal EEGs denoising. This method applies facial landmark tracking to detect and delete eye-blink noise. (3) An experimental evaluation showed the effectiveness of the frontal EEGs. It improved sentiment estimation performance when used with other modalities by multimodal fusion, although it only has three channels.",
}

Multimodal information such as text and audiovisual data has been used for emotion/sentiment estimation during human-agent dialogue; however, user sentiments are not necessarily expressed explicitly during dialogues. Biosignals such as brain signals recorded using an electroencephalogram (EEG) sensor have been the subject of focus in affective computing regions to capture unexpressed emotional changes in a controlled experimental environment. In this study, we collect and analyze multimodal data with an EEG during a human-agent dialogue toward capturing unexpressed sentiment. Our contributions are as follows: (1) a new multimodal human-agent dialogue dataset is created, which includes not only text and audiovisual data but also frontal EEGs and physiological signals during the dialogue. In total, about 500-minute chat dialogues were collected from thirty participants aged 20 to 70. (2) We present a novel method for dealing with eye-blink noise for frontal EEGs denoising. This method applies facial landmark tracking to detect and delete eye-blink noise. (3) An experimental evaluation showed the effectiveness of the frontal EEGs. It improved sentiment estimation performance when used with other modalities by multimodal fusion, although it only has three channels.
Katada, Shun Takeda, Ryu Komatani, Kazunori
Collecting Human-Agent Dialogue Dataset with Frontal Brain Signal toward Capturing Unexpressed Sentiment