该文9月份发表在arXiv, 意大利unict的Concetto Spampinato教授等发表的,Concetto Spampinato教授研究领域在机器学习 脑机接口等,具有很强的时代意义。下面对于文章做个简要概述:

What if we could effectively read the mind and transfer human visual capabilities to computer vision methods? In this paper, we aim at addressing this question by developing the first visual object classifier driven by human brain signals. In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories. Afterward, we train a Convolutional Neural Network (CNN)–based regressor to project images onto the learned manifold, thus effectively allowing machines to employ human brain–based features for automated visual classification. We use a 32-channel EEG to record brain activity of seven subjects while looking at images of 40 ImageNet object classes. The proposed RNN-based approach for discriminating object classes using brain signals reaches an average accuracy of about 40%, which outperforms existing methods attempting to learn EEG visual object representations. As for automated object categorization, our human brain–driven approach obtains competitive performance, comparable to those achieved by powerful CNN models, both on ImageNet and CalTech 101, thus demonstrating its classification and generalization capabilities. This gives us a real hope that, indeed, human mind can be read and transferred to machines.

如果能有效将人类视觉转化成计算机视觉,这将是非常有效的读取大脑技术,这篇文章研发了基于人脑信号的第一个视觉分分类器,实际上,这篇文章通过记录观看不同类别的图片记录人脑信号,然后通过RNN处理,生成基于可分辨的脑电信号框架,然后通过一个CNN回归映射到新的模型上, 笔者采用了32个channel的脑电信号,40个不同种类图片,每个类别50张,这种模式准确率达到了40%,

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