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
this https URL

当前的基于脑电图 (EEG) 的深度学习模型通常针对特定的数据集和脑 - 计算机交互 (BCI) 应用而设计，限制了模型的规模，从而降低了其感知能力和泛化能力。最近，大型语言模型 (LLMs) 在文本处理方面取得了前所未有的成功，激发了我们探索大型脑电图模型 (LEMs) 的能力。我们希望通过无监督预训练，LEMs 可以突破 EEG 数据集不同任务类型的限制，并获得对 EEG 信号的通用感知能力，然后将模型进行微调以适应不同的下游任务。然而，与文本数据相比，EEG 数据集的容量通常很小且格式变化多样。为了克服这些挑战，我们提出了一种统一的 EEG 基础模型，称为 Large Brain Model (LaBraM)。LaBraM 通过将 EEG 信号分割为 EEG 通道块来实现跨数据集学习。我们使用量化向量神经谱预测来训练一个语义丰富的神经分词器，将连续的原始 EEG 通道块编码为紧凑的神经代码。然后，我们通过预测遮蔽的 EEG 通道块的原始神经代码来预训练神经 Transformer。我们的 LaBraM 模型在大约 20 个数据集中的各种类型的 EEG 信号上进行了约 2,500 小时的预训练，并在多种不同类型的下游任务上进行了验证。对异常检测、事件类型分类、情绪识别和步态预测的实验表明，我们的 LaBraM 在各自领域的性能优于其他 SOTA 方法。