多主题EEG规范化的深度多向典型相关分析
Deep Multiway Canonical Correlation Analysis for Multi-Subject EEG Normalization
摘要：
多个被试对自然刺激的脑记录的标准化是听觉神经科学的关键挑战之一。这种标准化的目的是以这样的方式转换大脑数据，以消除受试者间的冗余，并增强与刺激相关的成分。在本文中，我们提出了一个深度学习框架，以提高从参与音频听力任务的多个受试者记录的脑电图（EEG）数据的相关性。该模型扩展了线性多路典型相关分析（CCA）的音频EEG分析使用一个自动编码器网络与共享的编码器层。该模型被训练以优化涉及相关和重建的组合损失。实验是在听自然语音和音乐的受试者的脑电数据上进行的。在这些实验中，我们表明，所提出的基于深度多路CCA（DMCCA）的模型显着提高了线性多路CCA方法的相关性，语音和音乐任务的皮尔逊相关值分别绝对提高了0.08和0.29。
英文摘要：
The normalization of brain recordings from multiple subjects responding to the natural stimuli is one of the key challenges in auditory neuroscience. The objective of this normalization is to transform the brain data in such a way as to remove the inter-subject redundancies and to boost the component related to the stimuli. In this paper, we propose a deep learning framework to improve the correlation of electroencephalography (EEG) data recorded from multiple subjects engaged in an audio listening task. The proposed model extends the linear multi-way canonical correlation analysis (CCA) for audio-EEG analysis using an auto-encoder network with a shared encoder layer. The model is trained to optimize a combined loss involving correlation and reconstruction. The experiments are performed on EEG data collected from subjects listening to natural speech and music. In these experiments, we show that the proposed deep multi-way CCA (DMCCA) based model significantly improves the correlations over the linear multi-way CCA approach with absolute improvements of 0.08 and 0.29 in terms of the Pearson correlation values for speech and music tasks respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
