Authors - Khushi Tijare, Reena S. Satpute Abstract - Bio-signals such as eye movements, electroencephalogram (EEG), functional magnetic resonance imaging (fMRI), and pupil dilation are real-time reactions to language cues that provide more data than static text representations used in classical NLP. The aim of this study is to examine how bio signals could be employed in deep natural language processing (NLP) to enhance task effectiveness and interpretability during reading comprehension, sentiment analysis, named entity recognition (NER), and syntax parsing. The following sections will describe the important aspects of design, including. Pre-processing steps for EEG and eye-tracking datasets. Model architecture types such as feature injection, multi-task learning based on auxiliary tasks, attention mechanisms, and multimodal transformers. Experimental design and metrics used for model evaluation. Ethical considerations regarding the use of cognitive signals. All design decisions made by the authors have been verified through experiments involving open access benchmark datasets like ZuCo, ZuCo 2.0, and recently developed EEG datasets.
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