A Visual EEG Paradigm and Dataset for Recognizing the Size Transformation of Images

Visual stimulus-based BCI system has received attention recently in the field of BCI. However, the existing visual EEG decoding methods are limited, so it is necessary to propose a new visual stimulus paradigm and dataset to study the new visual EEG decoding methods. In this paper, we contribute a real-world dataset containing new visual stimulus paradigm and propose two baseline algorithms for visual EEG decoding. Our dataset contains EEG data acquired from 9 subjects (age:22-27, 3 female) without dysopsia by using 64 channels wet electrode head-mounted BCI equipment. We get total of 2160 groups of data from all subjects. The raw data records EEG signals in response to two types of visual stimuli: One is a circle that varies from small to large, and the other varies from large to small. To prove the validity of the dataset, we use two kinds of machine learning algorithm for classification. By using SVM, the accuracy of a single subject is 65.32%~97.75% with an average of 76.72%. Through LSTM, the average accuracy achieves to 81.85%. In addition, we classify each channel separately and find the average accuracy of channels in the visual region (10 channels, 73.84%) is higher than that in the non-visual region (49 channels, 65.28%). Both methods demonstrate the validity of the dataset.