mlsanityData to 7 Oct 2026

Are EEG-to-Text Models Working?

arXiv 2405.06459 ↗First posted 10 May 2024cs.CL cs.AIcited by 32

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Abstract (arXiv)

This work critically analyzes existing models for open-vocabulary EEG-to-Text translation. We identify a crucial limitation: previous studies often employed implicit teacher-forcing during evaluation, artificially inflating performance metrics. Additionally, they lacked a critical benchmark - comparing model performance on pure noise inputs. We propose a methodology to differentiate between models that truly learn from EEG signals and those that simply memorize training data. Our analysis reveals that model performance on noise data can be comparable to that on EEG data. These findings highlight the need for stricter evaluation practices in EEG-to-Text research, emphasizing transparent reporting and rigorous benchmarking with noise inputs. This approach will lead to more reliable assessments of model capabilities and pave the way for robust EEG-to-Text communication systems. Code is available at https://github.com/NeuSpeech/EEG-To-Text

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 8 of its 14 references that are in the map. First posted on arXiv 10 May 2024.

Builds on

8 of 14 references.

References · 8

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  3. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  4. 2021Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classificationcited by 67
  5. 2019ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotationcited by 65
  6. 2023UniCoRN: Unified Cognitive Signal ReconstructioN bridging cognitive signals and human languagecited by 22
  7. 2024NeuSpeech: Decode Neural signal as Speechcited by 10
  8. 2023BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervisioncited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 12; 2026: 13.

32 papers.

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  1. 2024The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learningcited by 21
  2. 2025LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scalecited by 20
  3. 2024MAD: Multi-Alignment MEG-to-Text Decodingcited by 14
  4. 2025Unlocking Non-Invasive Brain-to-Textcited by 14
  5. 2024Du-IN: Discrete units-guided mask modeling for decoding speech from Intracranial Neural signalscited by 12
  6. 2024NeuGPT: Unified multi-modal Neural GPTcited by 6
  7. 2026MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Trainingcited by 6
  8. 2024Towards Linguistic Neural Representation Learning and Sentence Retrieval from Electroencephalogram Recordingscited by 4
  9. 2024BrainECHO: Semantic Brain Signal Decoding through Vector-Quantized Spectrogram Reconstruction for Whisper-Enhanced Text Generationcited by 4
  10. 2025Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortexcited by 4
  11. 2025Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generationcited by 4
  12. 2025Foundation Models for Cross-Domain EEG Analysis Application: A Surveycited by 4
20 more
  1. 2025A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyondcited by 3
  2. 2025Pretraining Large Brain Language Model for Active BCI: Silent Speechcited by 3
  3. 2025Elementary, My Dear Watson: Non-Invasive Neural Keyword Spotting in the LibriBrain Datasetcited by 3
  4. 2024Decoding Linguistic Representations of Human Braincited by 1
  5. 2025Bridging Brain with Foundation Models through Self-Supervised Learningcited by 1
  6. 2026Meta-learning In-Context Enables Training-Free Cross Subject Brain Decodingcited by 1
  7. 2026Margin-Regularized Structured Semantic Alignment for Brain-Language Correspondencecited by 1
  8. 2026The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100cited by 1
  9. 2026The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decodingcited by 1
  10. 2025EEG-to-Text Translation: A Model for Deciphering Human Brain Activitycited by 0
  11. 2025Neurocognitive Modeling for Text Generation: Deep Learning Architecture for EEG Datacited by 0
  12. 2025DELTA: Language Diffusion-based EEG-to-Text Architecturecited by 0
  13. 2026Cross-Sensory Brain Passage Retrieval: Scaling Beyond Visual to Audiocited by 0
  14. 2026MEGnifying Emotion: Sentiment Analysis from Annotated Brain Datacited by 0
  15. 2026Escaping the BLEU Trap: A Signal-Grounded Framework with Decoupled Semantic Guidance for EEG-to-Text Decodingcited by 0
  16. 2026Measuring Robustness of Speech Recognition from MEG Signals Under Distribution Shiftcited by 0
  17. 2026Brain-CLIPLM: Semantic Compression for EEG-to-Text Decodingcited by 0
  18. 2026MindVoice: Reconstructing Intelligible Speech from Non-invasive Neural Signals with Pretrained Priorscited by 0
  19. 2026Is EEG-to-Text Feasible in Real-World Scenarios? An In-Depth Analysis Using a Neuropsychology-Inspired Benchmarkcited by 0
  20. 2026Decoding Imagined Speech: A Strictly Subject-Independent Approach Using EEGcited by 0