This paper focuses on EEG-based visual recognition, aiming to predict the visual object class observed by a subject based on his/her EEG signals. One of the main challenges is the large variation between signals from different subjects. It limits recognition systems to work only for the subjects involved in model training, which is undesirable for real-world scenarios where new subjects are frequently added. This limitation can be alleviated by collecting a large amount of data for each new user, yet it is costly and sometimes infeasible. To make the task more practical, we introduce a novel problem setting, namely subject adaptive EEG-based visual recognition. In this setting, a bunch of pre-recorded data of existing users (source) is available, while only a little training data from a new user (target) are provided. At inference time, the model is evaluated solely on the signals from the target user. This setting is challenging, especially because training samples from source subjects may not be helpful when evaluating the model on the data from the target subject. To tackle the new problem, we design a simple yet effective baseline that minimizes the discrepancy between feature distributions from different subjects, which allows the model to extract subject-independent features. Consequently, our model can learn the common knowledge shared among subjects, thereby significantly improving the recognition performance for the target subject. In the experiments, we demonstrate the effectiveness of our method under various settings. Our code is available at https://github.com/DeepBCI/Deep-BCI/tree/master/1_Intelligent_BCI/Subject_Adaptive_EEG_based_Visual_Recognition.
翻译:本文侧重于基于 EEG 的视觉识别, 目的是预测一个以其 EEG 信号为基础的主题所观测的视觉对象类别。 主要的挑战之一是不同主题的信号之间的巨大差异。 它限制识别系统只对模型培训所涉的科目起作用, 这对现实世界的情景来说是不可取的, 而对于经常增加新主题的情景来说, 这是不可取的。 这一限制可以通过为每个新用户收集大量数据来缓解, 然而成本高昂, 有时不可行 。 为了让任务更加实用, 我们引入了一个新问题设置, 即适应性 EEEG 的视觉识别。 在这个设置中, 现有用户( 源) 的预录数据组( 源) 存在, 而只提供了来自新用户( 目标) 的少量培训数据。 在推论时间里, 模型只用目标用户的信号来评估。 特别是, 来源主题的培训样本在评估目标对象的数据模型时可能无帮助。 为了解决新的问题, 我们设计了一个简单而有效的基线, 最大限度地减少不同主题的地分布差异,, 并且只提供来自新用户( 源) 的预录的数据数据( 数据( 目标) 仅由新用户( 目标) (目标) 目标) 定义) 测试中, 的模型/ 测试中, 我们的实验中学习的模型中学习了我们学习了我们的常规/ 的实验中, 的实验中, 我们的实验中学习了共同的实验中学习了常规/ 。