Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding into Text

• URL: http://arxiv.org/abs/2405.00726v1
• Date: Fri, 26 Apr 2024 21:18:05 GMT
• Title: Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding into Text
• Authors: Saydul Akbar Murad, Nick Rahimi,
• Abstract summary: Conversion of brain activity into text using electroencephalography (EEG) has gained significant traction in recent years. Many researchers are working to develop new models to decode EEG signals into text form. It's important to outline this area's recent developments and future research directions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The conversion of brain activity into text using electroencephalography (EEG) has gained significant traction in recent years. Many researchers are working to develop new models to decode EEG signals into text form. Although this area has shown promising developments, it still faces numerous challenges that necessitate further improvement. It's important to outline this area's recent developments and future research directions. In this review article, we thoroughly summarize the progress in EEG-to-text conversion. Firstly, we talk about how EEG-to-text technology has grown and what problems we still face. Secondly, we discuss existing techniques used in this field. This includes methods for collecting EEG data, the steps to process these signals, and the development of systems capable of translating these signals into coherent text. We conclude with potential future research directions, emphasizing the need for enhanced accuracy, reduced system constraints, and the exploration of novel applications across varied sectors. By addressing these aspects, this review aims to contribute to developing more accessible and effective Brain-Computer Interface (BCI) technology for a broader user base.

Related papers

• Transformer-based EEG Decoding: A Survey [2.3288585185469146]
  Transformer is renowned for its strong handling capability of sequential data by the attention mechanism.<n>Deep learning approaches have gradually revolutionized the field by providing an end-to-end long-cascaded architecture.
  arXiv  Detail & Related papers  (2025-07-03T05:12:06Z)
• 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)
• Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook [85.43403500874889]
  Retrieval-augmented generation (RAG) has emerged as a pivotal technique in artificial intelligence (AI) Recent advancements in RAG for embodied AI, with a particular focus on applications in planning, task execution, multimodal perception, interaction, and specialized domains.
  arXiv  Detail & Related papers  (2025-03-23T10:33:28Z)
• A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond [4.720913027054481]
  Integration of Brain-Computer Interfaces (BCIs) and Generative Artificial Intelligence (GenAI) has opened new frontiers in brain signal decoding. Recent advances in deep learning, including Generative Adversarial Networks (GANs) and Transformer-based Large Language Models (LLMs), have significantly improved EEG-based generation of images, text, and speech.
  arXiv  Detail & Related papers  (2025-02-17T17:16:41Z)
• 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. The approach involves three stages: (1) training an EEG encoder for visual feature extraction, (2) fine-tuning LLMs on image and text data, enabling multimodal description generation, and (3) further fine-tuning on EEG embeddings to generate text directly from EEG during inference.
  arXiv  Detail & Related papers  (2024-10-10T00:47:59Z)
• A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications [20.54846023209402]
  This survey focuses on emerging methods and technologies that are poised to transform our comprehension and interpretation of brain activity. We delve into self-supervised learning methods that enable the robust representation of brain signals. We also explore emerging discriminative methods, including graph neural networks (GNN), foundation models, and large language models (LLMs)-based approaches. The survey provides an extensive overview of these cutting-edge techniques, their current applications, and the profound implications they hold for future research and clinical practice.
  arXiv  Detail & Related papers  (2024-09-26T08:09:15Z)
• SEE: Semantically Aligned EEG-to-Text Translation [5.460650382586978]
  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.
  arXiv  Detail & Related papers  (2024-09-14T05:37:15Z)
• 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)
• Towards Possibilities & Impossibilities of AI-generated Text Detection: A Survey [97.33926242130732]
  Large Language Models (LLMs) have revolutionized the domain of natural language processing (NLP) with remarkable capabilities of generating human-like text responses. Despite these advancements, several works in the existing literature have raised serious concerns about the potential misuse of LLMs. To address these concerns, a consensus among the research community is to develop algorithmic solutions to detect AI-generated text.
  arXiv  Detail & Related papers  (2023-10-23T18:11:32Z)
• EEG based Emotion Recognition: A Tutorial and Review [21.939910428589638]
  The scientific basis of EEG-based emotion recognition in the psychological and physiological levels is introduced. We categorize these reviewed works into different technical routes and illustrate the theoretical basis and the research motivation.
  arXiv  Detail & Related papers  (2022-03-16T08:28:28Z)
• 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)
• Computational Emotion Analysis From Images: Recent Advances and Future Directions [79.05003998727103]
  In this chapter, we aim to introduce image emotion analysis (IEA) from a computational perspective. We begin with commonly used emotion representation models from psychology. We then define the key computational problems that the researchers have been trying to solve.
  arXiv  Detail & Related papers  (2021-03-19T13:33:34Z)
• Graph signal processing for machine learning: A review and new perspectives [57.285378618394624]
  We review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. We discuss exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. We provide new perspectives on future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side, and machine learning and network science on the other.
  arXiv  Detail & Related papers  (2020-07-31T13:21:33Z)
• EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications [65.32004302942218]
  Brain-Computer Interface (BCI) is a powerful communication tool between users and systems. Recent technological advances have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications.
  arXiv  Detail & Related papers  (2020-01-28T10:36:26Z)

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.