Deep learning is leading to major advances in the realm of brain decoding
from functional Magnetic Resonance Imaging (fMRI). However, the large
inter-subject variability in brain characteristics has limited most studies to
train models on one subject at a time. Consequently, this approach hampers the
training of deep learning models, which typically requires very large datasets.
Here, we propose to boost brain decoding by aligning brain responses to videos
and static images across subjects. Compared to the anatomically-aligned
baseline, our method improves out-of-subject decoding performance by up to 75%.
Moreover, it also outperforms classical single-subject approaches when fewer
than 100 minutes of data is available for the tested subject. Furthermore, we
propose a new multi-subject alignment method, which obtains comparable results
to that of classical single-subject approaches while improving out-of-subject
generalization. Finally, we show that this method aligns neural representations
in accordance with brain anatomy. Overall, this study lays the foundations for
leveraging extensive neuroimaging datasets and enhancing the decoding of
individuals with a limited amount of brain recordings.

采用深度学习和脑解码技术，本研究通过跨参与者对齐脑响应视频和静态图像，提高了脑解码性能，并将神经表征与脑解剖结构相一致，为扩展神经成像数据集和增强有限脑记录的个体解码奠定了基础。

脑功能对新受试者的视觉语义解码起促进作用

Aligning brain functions boosts the decoding of visual semantics in  novel subjects

Inter-subject variability between individuals poses a challenge in
inter-subject brain signal analysis problems. A new algorithm for
subject-selection based on clustering covariance matrices on a Riemannian
manifold is proposed. After unsupervised selection of the subsets of relevant
subjects, data in a cluster is mapped to a tangent space at the mean point of
covariance matrices in that cluster and an SVM classifier on labeled data from
relevant subjects is trained. Experiment on an EEG seizure database shows that
the proposed method increases the accuracy over state-of-the-art from 86.83% to
89.84% and specificity from 87.38% to 89.64% while reducing the false positive
rate/hour from 0.8/hour to 0.77/hour.

利用 Riemannian 流形聚类协方差矩阵的新算法，对选择哪些实验对象进行无监督筛选，实验结果表明该方法在脑信号分析领域表现出色，准确率和特异度达到了 89.84% 和 89.64%，并将错误报警率从原来的每小时 0.8 降低至 0.77。