School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju, 61005, South Korea.
Bio and Medical Health Division, Korea Testing Laboratory, 87, Digital-ro 26-gil, Guro-gu, Seoul, 08389, South Korea.
Sci Data. 2022 Jul 8;9(1):388. doi: 10.1038/s41597-022-01509-w.
As attention to deep learning techniques has grown, many researchers have attempted to develop ready-to-go brain-computer interfaces (BCIs) that include automatic processing pipelines. However, to do so, a large and clear dataset is essential to increase the model's reliability and performance. Accordingly, our electroencephalogram (EEG) dataset for rapid serial visual representation (RSVP) and P300 speller may contribute to increasing such BCI research. We validated our dataset with respect to features and accuracy. For the RSVP, the participants (N = 50) achieved about 92% mean target detection accuracy. At the feature level, we observed notable ERPs (at 315 ms in the RSVP; at 262 ms in the P300 speller) during target events compared to non-target events. Regarding P300 speller performance, the participants (N = 55) achieved about 92% mean accuracy. In addition, P300 speller performance over trial repetitions up to 15 was explored. The presented dataset could potentially improve P300 speller applications. Further, it may be used to evaluate feature extraction and classification algorithm effectively, such as for cross-subjects/cross-datasets, and even for the cross-paradigm BCI model.
随着对深度学习技术的关注度不断提高，许多研究人员试图开发包括自动处理管道的即用型脑机接口 (BCI)。然而，要做到这一点，一个大型且清晰的数据集对于提高模型的可靠性和性能至关重要。因此，我们的快速序列视觉呈现 (RSVP) 和 P300 拼写器的脑电图 (EEG) 数据集可能有助于增加此类 BCI 研究。我们针对特征和准确性对我们的数据集进行了验证。对于 RSVP，参与者（N=50）的目标检测平均准确率约为 92%。在特征水平上，与非目标事件相比，我们在目标事件中观察到了明显的 ERP（RSVP 中为 315ms；P300 拼写器中为 262ms）。关于 P300 拼写器的性能，参与者（N=55）的平均准确率约为 92%。此外，还探索了多达 15 次试验重复的 P300 拼写器性能。所呈现的数据集可能会改善 P300 拼写器的应用。此外，它可以用于有效地评估特征提取和分类算法，例如跨受试者/数据集，甚至是跨范式 BCI 模型。
