Deep Learning methods have been rising in popularity in the past few years, and are now used as a fundamental component in various application domains such as computer vision, natural language processing, bioinformatics. Supervised learning with Convolutional Neural Networks has become the state of the art approach in many image related works. However, despite the great success of deep learning methods in other areas they remain relatively unexplored in the brain imaging field. In this paper we make an overview of recent achievements of Deep Learning to automatically extract features from brain signals that enable building Brain-Machine Interfaces (BMI). Major challenge in the BMI research is to find common subject-independent neural signatures due to the high brain data variability across multiple subjects. To address this problem we propose a Deep Neural Autoencoder with sparsity constraint as a promising approach to extract hidden features from Electroencephalogram data (in-dept feature learning) and build a subject-independent noninvasive BMI in the affective neuro computing framework. Future direction for research are also outlined.

中文翻译：

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脑机接口的深度学习模型

深度学习方法在过去几年越来越受欢迎，现在被用作各种应用领域的基本组件，如计算机视觉、自然语言处理、生物信息学。使用卷积神经网络进行监督学习已成为许多图像相关工作中最先进的方法。然而，尽管深度学习方法在其他领域取得了巨大成功，但它们在脑成像领域仍然相对未被探索。在本文中，我们概述了深度学习的最新成就，可自动从大脑信号中提取特征，从而构建脑机接口 (BMI)。由于多个受试者的大脑数据差异很大，因此 BMI 研究的主要挑战是找到与受试者无关的共同神经特征。为了解决这个问题，我们提出了一种具有稀疏约束的深度神经自动编码器，作为从脑电图数据中提取隐藏特征（内部特征学习）并在情感神经计算框架中构建与主题无关的非侵入性 BMI 的有前途的方法。还概述了未来的研究方向。