On Creating A Brain-To-Text Decoder
Not on the map. Why?
Abstract (arXiv)
Brain decoding has emerged as a rapidly advancing and extensively utilized technique within neuroscience. This paper centers on the application of raw electroencephalogram (EEG) signals for decoding human brain activity, offering a more expedited and efficient methodology for enhancing our understanding of the human brain. The investigation specifically scrutinizes the efficacy of brain-computer interfaces (BCI) in deciphering neural signals associated with speech production, with particular emphasis on the impact of vocabulary size, electrode density, and training data on the framework's performance. The study reveals the competitive word error rates (WERs) achievable on the Librispeech benchmark through pre-training on unlabelled data for speech processing. Furthermore, the study evaluates the efficacy of voice recognition under configurations with limited labeled data, surpassing previous state-of-the-art techniques while utilizing significantly fewer labels. Additionally, the research provides a comprehensive analysis of error patterns in voice recognition and the influence of model size and unlabelled training data. It underscores the significance of factors such as vocabulary size and electrode density in enhancing BCI performance, advocating for an increase in microelectrodes and refinement of language models.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 58 references that are in the map. First posted on arXiv 10 Jan 2025.
Builds on
22 of 58 references.
References · 22
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
- 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,838
- 2020Big Bird: Transformers for Longer Sequencescited by 1,673
- 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,303
- 2022data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Languagecited by 718
- 2013Speech Recognition with Deep Recurrent Neural Networkscited by 666
- 2020Recipes for building an open-domain chatbotcited by 593
- 2020ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-trainingcited by 221
10 more
- 2021BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG datacited by 194
- 2019Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)cited by 128
- 2021Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classificationcited by 67
- 2019ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotationcited by 65
- 2019Inducing brain-relevant bias in natural language processing modelscited by 38
- 2017Residual Convolutional CTC Networks for Automatic Speech Recognitioncited by 21
- 2020NeuroQuery: comprehensive meta-analysis of human brain mappingcited by 10
- 2024Contextualization of ASR with LLM using phonetic retrieval-based augmentationcited by 10
- 2021Reading Task Classification Using EEG and Eye-Tracking Datacited by 5
- 2018Text to brain: predicting the spatial distribution of neuroimaging observations from text reportscited by 1
Built on by
Citing papers per year: 2026: 2.
2 papers.