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        <title>The Language of Brain Signals: Natural Language Processing of Electroencephalography Reports</title>
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            <title>Proceedings of the 12th Language Resources and Evaluation Conference</title>
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    <abstract>Brain signals are captured by clinical electroencephalography (EEG) which is an excellent tool for probing neural function. When EEG tests are performed, a textual EEG report is generated by the neurologist to document the findings, thus using language that describes the brain signals and its clinical correlations. Even with the impetus provided by the BRAIN initiative (brainitititive.nih.gov), there are no annotations available in texts that capture language describing the brain activities and their correlations with various pathologies. In this paper we describe an annotation effort carried out on a large corpus of EEG reports, providing examples of EEG-specific and clinically relevant concepts. In addition, we detail our annotation schema for brain signal attributes. We also discuss the resulting annotation of long-distance relations between concepts in EEG reports. By exemplifying a self-attention joint-learning to predict similar annotations in the EEG report corpus, we discuss the promising results, hoping that our effort will inform the design of novel knowledge capture techniques that will include the language of brain signals.</abstract>
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        <url>https://aclanthology.org/2020.lrec-1.276</url>
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        <date>2020-may</date>
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%0 Conference Proceedings
%T The Language of Brain Signals: Natural Language Processing of Electroencephalography Reports
%A Maldonado, Ramon
%A Harabagiu, Sanda
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F maldonado-harabagiu-2020-language
%X Brain signals are captured by clinical electroencephalography (EEG) which is an excellent tool for probing neural function. When EEG tests are performed, a textual EEG report is generated by the neurologist to document the findings, thus using language that describes the brain signals and its clinical correlations. Even with the impetus provided by the BRAIN initiative (brainitititive.nih.gov), there are no annotations available in texts that capture language describing the brain activities and their correlations with various pathologies. In this paper we describe an annotation effort carried out on a large corpus of EEG reports, providing examples of EEG-specific and clinically relevant concepts. In addition, we detail our annotation schema for brain signal attributes. We also discuss the resulting annotation of long-distance relations between concepts in EEG reports. By exemplifying a self-attention joint-learning to predict similar annotations in the EEG report corpus, we discuss the promising results, hoping that our effort will inform the design of novel knowledge capture techniques that will include the language of brain signals.
%U https://aclanthology.org/2020.lrec-1.276
%P 2268-2275

Markdown (Informal)

[The Language of Brain Signals: Natural Language Processing of Electroencephalography Reports](https://aclanthology.org/2020.lrec-1.276) (Maldonado & Harabagiu, LREC 2020)

• The Language of Brain Signals: Natural Language Processing of Electroencephalography Reports (Maldonado & Harabagiu, LREC 2020)

ACL

• Ramon Maldonado and Sanda Harabagiu. 2020. The Language of Brain Signals: Natural Language Processing of Electroencephalography Reports. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 2268–2275, Marseille, France. European Language Resources Association.