SEE: Semantically Aligned EEG-to-Text Translation

• URL: http://arxiv.org/abs/2409.16312v1
• Date: Sat, 14 Sep 2024 05:37:15 GMT
• Title: SEE: Semantically Aligned EEG-to-Text Translation
• Authors: Yitian Tao, Yan Liang, Luoyu Wang, Yongqing Li, Qing Yang, Han Zhang,
• Abstract summary: Decoding neurophysiological signals into language is of great research interest within brain-computer interface (BCI) applications. Current EEG-to-Text decoding approaches face challenges due to the huge domain gap between EEG recordings and raw texts. We propose SEE: Semantically Aligned EEG-to-Text Translation, a novel method aimed at improving EEG-to-Text decoding.
• Score: 5.460650382586978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decoding neurophysiological signals into language is of great research interest within brain-computer interface (BCI) applications. Electroencephalography (EEG), known for its non-invasiveness, ease of use, and cost-effectiveness, has been a popular method in this field. However, current EEG-to-Text decoding approaches face challenges due to the huge domain gap between EEG recordings and raw texts, inherent data bias, and small closed vocabularies. In this paper, we propose SEE: Semantically Aligned EEG-to-Text Translation, a novel method aimed at improving EEG-to-Text decoding by seamlessly integrating two modules into a pre-trained BART language model. These two modules include (1) a Cross-Modal Codebook that learns cross-modal representations to enhance feature consolidation and mitigate domain gap, and (2) a Semantic Matching Module that fully utilizes pre-trained text representations to align multi-modal features extracted from EEG-Text pairs while considering noise caused by false negatives, i.e., data from different EEG-Text pairs that have similar semantic meanings. Experimental results on the Zurich Cognitive Language Processing Corpus (ZuCo) demonstrate the effectiveness of SEE, which enhances the feasibility of accurate EEG-to-Text decoding.

Related papers

• Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs) [4.720913027054481]
  This paper presents Thought2Text, which uses instruction-tuned Large Language Models (LLMs) fine-tuned with EEG data to achieve this goal. Experiments on a public EEG dataset collected for six subjects with image stimuli demonstrate the efficacy of multimodal LLMs. This approach marks a significant advancement towards portable, low-cost "thoughts-to-text" technology with potential applications in both neuroscience and natural language processing (NLP)
  arXiv  Detail & Related papers  (2024-10-10T00:47:59Z)
• BELT-2: Bootstrapping EEG-to-Language representation alignment for multi-task brain decoding [24.54436986074267]
  We introduce BELT-2, a pioneering multi-task model designed to enhance both encoding and decoding performance from EEG signals. BELT-2 is the first work to innovatively 1) adopt byte-pair encoding (BPE)-level EEG-language alignment and 2) integrate multi-task training and decoding in the EEG domain. These innovative efforts make BELT-2 a pioneering breakthrough, making it the first work in the field capable of decoding coherent and readable sentences from non-invasive brain signals.
  arXiv  Detail & Related papers  (2024-08-28T12:30:22Z)
• Towards Linguistic Neural Representation Learning and Sentence Retrieval from Electroencephalogram Recordings [27.418738450536047]
  We propose a two-step pipeline for converting EEG signals into sentences. We first confirm that word-level semantic information can be learned from EEG data recorded during natural reading. We employ a training-free retrieval method to retrieve sentences based on the predictions from the EEG encoder.
  arXiv  Detail & Related papers  (2024-08-08T03:40:25Z)
• 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)
• Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder [69.7813498468116]
  We propose Contrastive EEG-Text Masked Autoencoder (CET-MAE), a novel model that orchestrates compound self-supervised learning across and within EEG and text. We also develop a framework called E2T-PTR (EEG-to-Text decoding using Pretrained Transferable Representations) to decode text from EEG sequences.
  arXiv  Detail & Related papers  (2024-02-27T11:45:21Z)
• Cross-modality Data Augmentation for End-to-End Sign Language Translation [66.46877279084083]
  End-to-end sign language translation (SLT) aims to convert sign language videos into spoken language texts directly without intermediate representations. It has been a challenging task due to the modality gap between sign videos and texts and the data scarcity of labeled data. We propose a novel Cross-modality Data Augmentation (XmDA) framework to transfer the powerful gloss-to-text translation capabilities to end-to-end sign language translation.
  arXiv  Detail & Related papers  (2023-05-18T16:34:18Z)
• Code-Switching Text Generation and Injection in Mandarin-English ASR [57.57570417273262]
  We investigate text generation and injection for improving the performance of an industry commonly-used streaming model, Transformer-Transducer (T-T) We first propose a strategy to generate code-switching text data and then investigate injecting generated text into T-T model explicitly by Text-To-Speech (TTS) conversion or implicitly by tying speech and text latent spaces. Experimental results on the T-T model trained with a dataset containing 1,800 hours of real Mandarin-English code-switched speech show that our approaches to inject generated code-switching text significantly boost the performance of T-T models.
  arXiv  Detail & Related papers  (2023-03-20T09:13:27Z)
• Revamping Multilingual Agreement Bidirectionally via Switched Back-translation for Multilingual Neural Machine Translation [107.83158521848372]
  multilingual agreement (MA) has shown its importance for multilingual neural machine translation (MNMT) We present textbfBidirectional textbfMultilingual textbfAgreement via textbfSwitched textbfBack-textbftranslation (textbfBMA-SBT) It is a novel and universal multilingual agreement framework for fine-tuning pre-trained MNMT models.
  arXiv  Detail & Related papers  (2022-09-28T09:14:58Z)
• The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding [65.34601470417967]
  We describe a hybrid architecture for dialogue response generation that combines the strengths of neural language modeling and rule-based generation. Our experiments show that this system outperforms both rule-based and learned approaches in human evaluations of fluency, relevance, and truthfulness.
  arXiv  Detail & Related papers  (2022-09-16T09:00:49Z)
• 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)

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.