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

Saydul Akbar Murad, Nick Rahimi

TL;DR

This survey addresses the problem of translating EEG brain activity into text by outlining the challenges across data acquisition, preprocessing, feature extraction, and modeling, and by proposing a comprehensive EEG-to-generative model pipeline. It surveys data sources, public datasets such as ZuCo 1.0/2.0, devices, and artifact-removal and normalization techniques, while highlighting representative architectures like DeWave, MDADenseNet-AM, EEG-To-Text, and J-CRNN-BCI. The authors identify future directions including emotion-aware decoding, data diversification, multi-model frameworks, and cross-domain applications to broaden accessibility and impact of noninvasive BCIs. Overall, the work frames a path toward more accurate, robust, and inclusive EEG-to-text systems with real-world utility in communication for individuals with disabilities and beyond.

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.

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

TL;DR

This survey addresses the problem of translating EEG brain activity into text by outlining the challenges across data acquisition, preprocessing, feature extraction, and modeling, and by proposing a comprehensive EEG-to-generative model pipeline. It surveys data sources, public datasets such as ZuCo 1.0/2.0, devices, and artifact-removal and normalization techniques, while highlighting representative architectures like DeWave, MDADenseNet-AM, EEG-To-Text, and J-CRNN-BCI. The authors identify future directions including emotion-aware decoding, data diversification, multi-model frameworks, and cross-domain applications to broaden accessibility and impact of noninvasive BCIs. Overall, the work frames a path toward more accurate, robust, and inclusive EEG-to-text systems with real-world utility in communication for individuals with disabilities and beyond.

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.
Paper Structure (38 sections, 3 equations, 4 figures, 5 tables)

This paper contains 38 sections, 3 equations, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Challenges in EEG Decoding
  3. Data Acquisition
  4. Data Preprocessing and Feature Selection
  5. Model Building and Decoding
  6. System Limitations
  7. User Related Challenges
  8. Ethical Considerations
  9. EEG to Generative Model Pipeline
  10. Data Acquisition
  11. Datasets
  12. Generate Text
  13. Generate Image
  14. Devices
  15. Data Pre-processing
  16. ...and 23 more sections

Figures (4)

  • Figure 1: Challenges in EEG Signal Decoding for Text Generation.
  • Figure 2: Taxonomy of EEG Signal Processing for Text and Image Generation.
  • Figure 3: DeWave: A Transformer-Based EEG-to-Text Conversion Model.
  • Figure 4: Integrative Framework for EEG Signal Decoding and Sentiment Analysis: Leveraging Pretrained Language Models for Text Generation and Emotion Classification.