Visual Color Decoding Using Brain-Computer Interfaces

Abstract

With the rapid development of Brain-Computer Interfaces (BCI), human visual decoding, as one of the important research directions of BCI, has aroused great attention. But most visual decoding researches focused on graphics decoding. In this paper, we investigate the possibility to build a new kind of BCI visual decoding based on visual color observation for the first time. We selected 10 subjects without color blindness disease to participate in our tests. They were asked to observe red, green, blue screens in turn with an interval of 1 second. 5 subjects took the test without a task, while another 5 subjects took the test with a task of simply counting one of the appearances of the color. The result shows that the visual color classification for group A without task can reach 83.57% on average, whereas the visual color classification for group B with the task is 78.57% on average. It shows that these subjects may distract themselves while taking the task, however, the classification accuracy is relatively higher than 66.11% for selected channels for both cases with or without taking a task as interference to BCI.