  The current electroencephalogram (EEG) based deep learning models are
typically designed for specific datasets and applications in brain-computer
interaction (BCI), limiting the scale of the models and thus diminishing their
perceptual capabilities and generalizability. Recently, Large Language Models
(LLMs) have achieved unprecedented success in text processing, prompting us to
explore the capabilities of Large EEG Models (LEMs). We hope that LEMs can
break through the limitations of different task types of EEG datasets, and
obtain universal perceptual capabilities of EEG signals through unsupervised
pre-training. Then the models can be fine-tuned for different downstream tasks.
However, compared to text data, the volume of EEG datasets is generally small
and the format varies widely. For example, there can be mismatched numbers of
electrodes, unequal length data samples, varied task designs, and low
signal-to-noise ratio. To overcome these challenges, we propose a unified
foundation model for EEG called Large Brain Model (LaBraM). LaBraM enables
cross-dataset learning by segmenting the EEG signals into EEG channel patches.
Vector-quantized neural spectrum prediction is used to train a semantically
rich neural tokenizer that encodes continuous raw EEG channel patches into
compact neural codes. We then pre-train neural Transformers by predicting the
original neural codes for the masked EEG channel patches. The LaBraMs were
pre-trained on about 2,500 hours of various types of EEG signals from around 20
datasets and validated on multiple different types of downstream tasks.
Experiments on abnormal detection, event type classification, emotion
recognition, and gait prediction show that our LaBraM outperforms all compared
SOTA methods in their respective fields. Our code is available at
https://github.com/935963004/LaBraM.
