ETS: Open Vocabulary Electroencephalography-To-Text Decoding and Sentiment Classification

• URL: http://arxiv.org/abs/2506.14783v1
• Date: Mon, 26 May 2025 10:58:13 GMT
• Title: ETS: Open Vocabulary Electroencephalography-To-Text Decoding and Sentiment Classification
• Authors: Mohamed Masry, Mohamed Amen, Mohamed Elzyat, Mohamed Hamed, Norhan Magdy, Maram Khaled,
• Abstract summary: We propose ETS, a framework that integrates EEG with eye-tracking data to address two critical tasks: open-vocabulary text generation and sentiment classification.<n>Our model achieves a superior performance on BLEU and Rouge score for EEG-To-Text decoding and up to 10% F1 score on EEG-based ternary sentiment classification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decoding natural language from brain activity using non-invasive electroencephalography (EEG) remains a significant challenge in neuroscience and machine learning, particularly for open-vocabulary scenarios where traditional methods struggle with noise and variability. Previous studies have achieved high accuracy on small-closed vocabularies, but it still struggles on open vocabularies. In this study, we propose ETS, a framework that integrates EEG with synchronized eye-tracking data to address two critical tasks: (1) open-vocabulary text generation and (2) sentiment classification of perceived language. Our model achieves a superior performance on BLEU and Rouge score for EEG-To-Text decoding and up to 10% F1 score on EEG-based ternary sentiment classification, which significantly outperforms supervised baselines. Furthermore, we show that our proposed model can handle data from various subjects and sources, showing great potential for high performance open vocabulary eeg-to-text system.

Related papers

• Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation [52.51005875755718]
  We focus on EEG-to-text decoding and address its hallucination issue through the lens of posterior collapse.<n>Acknowledging the underlying mismatch in information capacity between EEG and text, we reframe the decoding task as semantic summarization of core meanings.<n>Experiments on the public ZuCo dataset demonstrate that GLIM consistently generates fluent, EEG-grounded sentences.
  arXiv  Detail & Related papers  (2025-05-21T05:29:55Z)
• EEG2TEXT: Open Vocabulary EEG-to-Text Decoding with EEG Pre-Training and Multi-View Transformer [4.863362296028391]
  We propose a novel method to improve the accuracy of EEG-to-text decoding. EEG2 TEXTURE shows great potential for a high-performance open-vocabulary brain-to-text system to facilitate communication.
  arXiv  Detail & Related papers  (2024-05-03T15:14:19Z)
• Language Generation from Brain Recordings [68.97414452707103]
  We propose a generative language BCI that utilizes the capacity of a large language model and a semantic brain decoder. The proposed model can generate coherent language sequences aligned with the semantic content of visual or auditory language stimuli. Our findings demonstrate the potential and feasibility of employing BCIs in direct language generation.
  arXiv  Detail & Related papers  (2023-11-16T13:37:21Z)
• Deep Representation Learning for Open Vocabulary Electroencephalography-to-Text Decoding [6.014363449216054]
  We present an end-to-end deep learning framework for non-invasive brain recordings that brings modern representational learning approaches to neuroscience. Our model achieves a BLEU-1 score of 42.75%, a ROUGE-1-F of 33.28%, and a BERTScore-F of 53.86%, outperforming the previous state-of-the-art methods by 3.38%, 8.43%, and 6.31%, respectively.
  arXiv  Detail & Related papers  (2023-11-15T08:03:09Z)
• Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension [4.390968520425543]
  This study aims to provide insights into individuals' neural states during a semantic relation reading-comprehension task. We propose jointly analyzing LLMs, eye-gaze, and electroencephalographic (EEG) data to study how the brain processes words with varying degrees of relevance to a keyword during reading.
  arXiv  Detail & Related papers  (2023-09-27T15:12:08Z)
• Incorporating Class-based Language Model for Named Entity Recognition in Factorized Neural Transducer [50.572974726351504]
  We propose C-FNT, a novel E2E model that incorporates class-based LMs into FNT. In C-FNT, the LM score of named entities can be associated with the name class instead of its surface form. The experimental results show that our proposed C-FNT significantly reduces error in named entities without hurting performance in general word recognition.
  arXiv  Detail & Related papers  (2023-09-14T12:14:49Z)
• Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification [78.120927891455]
  State-of-the-art brain-to-text systems have achieved great success in decoding language directly from brain signals using neural networks. In this paper, we extend the problem to open vocabulary Electroencephalography(EEG)-To-Text Sequence-To-Sequence decoding and zero-shot sentence sentiment classification on natural reading tasks. Our model achieves a 40.1% BLEU-1 score on EEG-To-Text decoding and a 55.6% F1 score on zero-shot EEG-based ternary sentiment classification, which significantly outperforms supervised baselines.
  arXiv  Detail & Related papers  (2021-12-05T21:57:22Z)
• Model-based analysis of brain activity reveals the hierarchy of language in 305 subjects [82.81964713263483]
  A popular approach to decompose the neural bases of language consists in correlating, across individuals, the brain responses to different stimuli. Here, we show that a model-based approach can reach equivalent results within subjects exposed to natural stimuli.
  arXiv  Detail & Related papers  (2021-10-12T15:30:21Z)
• Be More with Less: Hypergraph Attention Networks for Inductive Text Classification [56.98218530073927]
  Graph neural networks (GNNs) have received increasing attention in the research community and demonstrated their promising results on this canonical task. Despite the success, their performance could be largely jeopardized in practice since they are unable to capture high-order interaction between words. We propose a principled model -- hypergraph attention networks (HyperGAT) which can obtain more expressive power with less computational consumption for text representation learning.
  arXiv  Detail & Related papers  (2020-11-01T00:21:59Z)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.