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
- 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) - Spatio-Temporal Analysis of Transformer based Architecture for Attention
Estimation from EEG [2.7076510056452654]
We present a novel framework allowing us to retrieve the attention state, i.e degree of attention given to a specific task, from EEG signals.
While previous methods often consider the spatial relationship in EEG through electrodes, we propose here to also exploit the spatial and temporal information with a transformer-based network.
The proposed network has been trained and validated on two public datasets and achieves higher results compared to state-of-the-art models.
arXiv Detail & Related papers (2022-04-04T08:05:33Z) - 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) - A Survey of Controllable Text Generation using Transformer-based
Pre-trained Language Models [21.124096884958337]
Controllable Text Generation (CTG) is emerging area in the field of natural language generation (NLG)
We present a systematic critical review on the common tasks, main approaches, and evaluation methods in this area.
We discuss the challenges that the field is facing, and put forward various promising future directions.
arXiv Detail & Related papers (2022-01-14T08:32:20Z) - 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.