Deep learning techniques can be used to first training unsupervised models on
large amounts of unlabelled data, before fine-tuning the models on specific
tasks. This approach has seen massive success for various kinds of data, e.g.
images, language, audio, and holds the promise of improving performance in
various downstream tasks (e.g. encoding or decoding brain data). However, there
has been limited progress taking this approach for modelling brain signals,
such as Magneto-/electroencephalography (M/EEG). Here we propose two classes of
deep learning foundational models that can be trained using forecasting of
unlabelled MEG. First, we consider a modified Wavenet; and second, we consider
a modified Transformer-based (GPT2) model. The modified GPT2 includes a novel
application of tokenisation and embedding methods, allowing a model developed
initially for the discrete domain of language to be applied to continuous
multichannel time series data. We also extend the forecasting framework to
include condition labels as inputs, enabling better modelling (encoding) of
task data. We compare the performance of these deep learning models with
standard linear autoregressive (AR) modelling on MEG data. This shows that
GPT2-based models provide better modelling capabilities than Wavenet and linear
AR models, by better reproducing the temporal, spatial and spectral
characteristics of real data and evoked activity in task data. We show how the
GPT2 model scales well to multiple subjects, while adapting its model to each
subject through subject embedding. Finally, we show how such a model can be
useful in downstream decoding tasks through data simulation. All code is
available on GitHub (this https URL).

使用深度学习技术，以大量未标记数据预训练模型，并在特定任务上进行微调，对大脑信号进行建模比常规线性自回归模型具有更好的建模能力，其中 GPT2 模型在时间、空间和频谱特征方面更好地重现了实际数据和任务数据的诱发活动。

MEG 基础 GPT 模型

Foundational GPT Model for MEG

Decoding language from brain dynamics is an important open direction in the
realm of brain-computer interface (BCI), especially considering the rapid
growth of large language models. Compared to invasive-based signals which
require electrode implantation surgery, non-invasive neural signals (e.g. EEG,
MEG) have attracted increasing attention considering their safety and
generality. However, the exploration is not adequate in three aspects: 1)
previous methods mainly focus on EEG but none of the previous works address
this problem on MEG with better signal quality; 2) prior works have
predominantly used ``teacher-forcing" during generative decoding, which is
impractical; 3) prior works are mostly ``BART-based" not fully auto-regressive,
which performs better in other sequence tasks. In this paper, we explore the
brain-to-text translation of MEG signals in a speech-decoding formation. Here
we are the first to investigate a cross-attention-based ``whisper" model for
generating text directly from MEG signals without teacher forcing. Our model
achieves impressive BLEU-1 scores of 60.30 and 52.89 without pretraining \&
teacher-forcing on two major datasets (\textit{GWilliams} and
\textit{Schoffelen}). This paper conducts a comprehensive review to understand
how speech decoding formation performs on the neural decoding tasks, including
pretraining initialization, training \& evaluation set splitting, augmentation,
and scaling law.

在该论文中，我们首次探索了基于跨注意力的 “whisper” 模型，通过 MEG 信号直接生成文本，而无需教师强制，实现了令人印象深刻的 60.30 和 52.89 的 BLEU-1 得分，该论文还对有关神经解码任务的语音解码形成进行了全面的回顾。

解码神经信号为语音

Decode Neural signal as Speech

We present a novel approach to tackle explainability of deep graph networks
in the context of molecule property prediction tasks, named MEG (Molecular
Explanation Generator). We generate informative counterfactual explanations for
a specific prediction under the form of (valid) compounds with high structural
similarity and different predicted properties. We discuss preliminary results
showing how the model can convey non-ML experts with key insights into the
learning model focus in the neighborhood of a molecule.

本论文提出了一种新的方法来处理深度图网络在分子属性预测任务中的可解释性，称为 MEG（分子解释生成器），我们生成了高结构相似且具有不同预测属性的（有效）化合物的信息性对照解释，初步结果显示该模型可以向非机器学习专家传达关于分子邻域中学习模型集中的关键见解。