# Scaling Law in Neural Data: Non-Invasive Speech Decoding with 175 Hours of EEG Data

arXiv: https://arxiv.org/abs/2407.07595 · HTML: https://mlsanity.com/paper/2407.07595/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2024.

## Built on by (most cited first, 13 of 13)

- [Whisper-LM: Improving ASR Models with Language Models for Low-Resource Languages](https://mlsanity.com/paper/2503.23542/) (2025)
- [Elementary, My Dear Watson: Non-Invasive Neural Keyword Spotting in the LibriBrain Dataset](https://mlsanity.com/paper/2510.21038/) (2025)
- [Decoding Phone Pairs from MEG Signals Across Speech Modalities](https://mlsanity.com/paper/2505.15355/) (2025)
- [An Approach to Simultaneous Acquisition of Real-Time MRI Video, EEG, and Surface EMG for Articulatory, Brain, and Muscle Activity During Speech Production](https://mlsanity.com/paper/2603.04840/) (2026)
- [A Silent Speech Decoding System from EEG and EMG with Heterogenous Electrode Configurations](https://mlsanity.com/paper/2506.13835/) (2025)
- [Detecting Reading-Induced Confusion Using EEG and Eye Tracking](https://mlsanity.com/paper/2508.14442/) (2025)
- [A Dataset Generation Scheme Based on Video2EEG-SPGN-Diffusion for SEED-VD](https://mlsanity.com/paper/2509.05321/) (2025)
- [Simple Models, Rich Representations: Visual Decoding from Primate Intracortical Neural Signals](https://mlsanity.com/paper/2601.11108/) (2026)
- [MEG-to-MEG Transfer Learning and Cross-Task Speech/Silence Detection with Limited Data](https://mlsanity.com/paper/2602.18253/) (2026)
- [MindVoice: Reconstructing Intelligible Speech from Non-invasive Neural Signals with Pretrained Priors](https://mlsanity.com/paper/2605.31173/) (2026)
- [Joint Text-Audio Alignment for EEG-to-Text Decoding in Chinese Speech Production and Perception](https://mlsanity.com/paper/2607.25626/) (2026)
- [SoniSpeech: A Large-Scale Open-Vocabulary Tri-Modal Dataset for Wearable Silent Speech Interfaces](https://mlsanity.com/paper/2608.00803/) (2026)
- [Decoding silent reading from non-invasive EEG](https://mlsanity.com/paper/2608.20186/) (2026)

## Abstract (arXiv)

Brain-computer interfaces (BCIs) hold great potential for aiding individuals with speech impairments. Utilizing electroencephalography (EEG) to decode speech is particularly promising due to its non-invasive nature. However, recordings are typically short, and the high variability in EEG data has led researchers to focus on classification tasks with a few dozen classes. To assess its practical applicability for speech neuroprostheses, we investigate the relationship between the size of EEG data and decoding accuracy in the open vocabulary setting. We collected extensive EEG data from a single participant (175 hours) and conducted zero-shot speech segment classification using self-supervised representation learning. The model trained on the entire dataset achieved a top-1 accuracy of 48% and a top-10 accuracy of 76%, while mitigating the effects of myopotential artifacts. Conversely, when the data was limited to the typical amount used in practice (~10 hours), the top-1 accuracy dropped to 2.5%, revealing a significant scaling effect. Additionally, as the amount of training data increased, the EEG latent representation progressively exhibited clearer temporal structures of spoken phrases. This indicates that the decoder can recognize speech segments in a data-driven manner without explicit measurements of word recognition. This research marks a significant step towards the practical realization of EEG-based speech BCIs.