ArticleFrontiers in human neuroscience2021
Decoding EEG Brain Activity for Multi-Modal Natural Language Processing.
Article in Frontiers in human neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Large-scale foundation models and generative AI for BigData neuroscience.Neuroscience research · 2025Review
- A simultaneous EEG and eye-tracking dataset for remote sensing object detection.Scientific data · 2025Article
- Multi-branch convolutional neural network with cross-attention mechanism for emotion recognition.Scientific reports · 2025Article
- Brain-model neural similarity reveals abstractive summarization performance.Scientific reports · 2025Article
- Towards neuroadaptive chatbots: a feasibility study.Frontiers in neuroergonomics · 2025Article
- Reconstructing signal during brain stimulation with Stim-BERT: a self-supervised learning model trained on millions of iEEG files.Frontiers in artificial intelligence · 2025Article
- DERCo: A Dataset for Human Behaviour in Reading Comprehension Using EEG.Scientific data · 2024Article
- Lobish: Symbolic Language for Interpreting Electroencephalogram Signals in Language Detection Using Channel-Based Transformation and Pattern.Diagnostics (Basel, Switzerland) · 2024Article
- ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding.Scientific data · 2024Article
- The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data.Frontiers in psychology · 2022Article
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7 authors.
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Abstract
Until recently, human behavioral data from reading has mainly been of interest to researchers to understand human cognition. However, these human language processing signals can also be beneficial in machine learning-based natural language processing tasks. Using EEG brain activity for this purpose is largely unexplored as of yet. In this paper, we present the first large-scale study of systematically analyzing the potential of EEG brain activity data for improving natural language processing tasks, with a special focus on which features of the signal are most beneficial. We present a multi-modal machine learning architecture that learns jointly from textual input as well as from EEG features. We find that filtering the EEG signals into frequency bands is more beneficial than using the broadband signal. Moreover, for a range of word embedding types, EEG data improves binary and ternary sentiment classification and outperforms multiple baselines. For more complex tasks such as relation detection, only the contextualized BERT embeddings outperform the baselines in our experiments, which raises the need for further research. Finally, EEG data shows to be particularly promising when limited training data is available.
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