论文标题
诱发的潜在潜在的深度学习大脑表示视觉分类
An Evoked Potential-Guided Deep Learning Brain Representation For Visual Classification
论文作者
论文摘要
视觉分类的新观点旨在解码人类大脑活动的视觉对象的特征表示。从脑皮层记录脑电图（EEG）已被视为一种普遍理解图像分类任务的认知过程的方法。在这项研究中，我们提出了一个以视觉诱发电位为指导的深度学习框架，称为事件相关电位（ERP）长期记忆（LSTM）框架，由EEG信号提取以进行视觉分类。在特定的情况下，我们首先将ERP序列从多个脑电图通道提取到响应图像刺激相关信息。然后，我们训练了一个LSTM网络，以学习视觉对象的特征表示空间进行分类。在实验中，来自具有6个类别的图像数据集的50,000多次脑电图试验记录了10名受试者，其中包括总共72个示例。我们的结果表明，我们提出的ERP-LSTM框架可以分别达到类别（6类）和示例（72类）的交叉主题的分类精度。我们的结果表现优于使用现有的视觉分类框架，通过提高分类精度在12.62％-53.99％的范围内。我们的发现表明，从EEG信号解码视觉诱发电位是学习视觉分类的歧视性脑表示的有效策略。
The new perspective in visual classification aims to decode the feature representation of visual objects from human brain activities. Recording electroencephalogram (EEG) from the brain cortex has been seen as a prevalent approach to understand the cognition process of an image classification task. In this study, we proposed a deep learning framework guided by the visual evoked potentials, called the Event-Related Potential (ERP)-Long short-term memory (LSTM) framework, extracted by EEG signals for visual classification. In specific, we first extracted the ERP sequences from multiple EEG channels to response image stimuli-related information. Then, we trained an LSTM network to learn the feature representation space of visual objects for classification. In the experiment, 10 subjects were recorded by over 50,000 EEG trials from an image dataset with 6 categories, including a total of 72 exemplars. Our results showed that our proposed ERP-LSTM framework could achieve classification accuracies of cross-subject of 66.81% and 27.08% for categories (6 classes) and exemplars (72 classes), respectively. Our results outperformed that of using the existing visual classification frameworks, by improving classification accuracies in the range of 12.62% - 53.99%. Our findings suggested that decoding visual evoked potentials from EEG signals is an effective strategy to learn discriminative brain representations for visual classification.
