The Language of Brain Signals: Natural Language Processing of Electroencephalography Reports

摘要

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 their clinical correlations. Even with the impetus provided by the BRAIN initiative (brainitititive.nih.gov), there are no annotations available in texts that use natural 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 method used to predict concept, attribute and relation annotations in the EEG report corpus, we discuss the promising results of automatic annotations, hoping that our effort will intbrm the design of novel knowledge capture techniques that will include the language of brain signals.