The University of Queensland, Queensland Brain Institute, St Lucia, 4072, Australia.
The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia, Australia.
Sci Data. 2022 Jun 13;9(1):296. doi: 10.1038/s41597-022-01398-z.
Brain-computer interfaces (BCIs) are a rapidly expanding field of study and require accurate and reliable real-time decoding of patterns of neural activity. These protocols often exploit selective attention, a neural mechanism that prioritises the sensory processing of task-relevant stimulus features (feature-based attention) or task-relevant spatial locations (spatial attention). Within the visual modality, attentional modulation of neural responses to different inputs is well indexed by steady-state visual evoked potentials (SSVEPs). These signals are reliably present in single-trial electroencephalography (EEG) data, are largely resilient to common EEG artifacts, and allow separation of neural responses to numerous concurrently presented visual stimuli. To date, efforts to use single-trial SSVEPs to classify visual attention for BCI control have largely focused on spatial attention rather than feature-based attention. Here, we present a dataset that allows for the development and benchmarking of algorithms to classify feature-based attention using single-trial EEG data. The dataset includes EEG and behavioural responses from 30 healthy human participants who performed a feature-based motion discrimination task on frequency tagged visual stimuli.
脑机接口（BCI）是一个快速发展的研究领域，需要对神经活动模式进行准确可靠的实时解码。这些协议通常利用选择性注意，这是一种将感觉处理优先于任务相关刺激特征（基于特征的注意）或任务相关空间位置（空间注意）的神经机制。在视觉模态中，对不同输入的神经反应的注意调制很好地由稳态视觉诱发电位（SSVEP）来索引。这些信号在单次脑电图（EEG）数据中可靠存在，对常见的 EEG 伪影有很大的抗干扰能力，并且可以分离对许多同时呈现的视觉刺激的神经反应。迄今为止，使用单次 SSVEP 对 BCI 控制进行视觉注意力分类的努力主要集中在空间注意力上，而不是基于特征的注意力上。在这里，我们提供了一个数据集，允许开发和基准测试使用单次 EEG 数据对基于特征的注意力进行分类的算法。该数据集包括 30 名健康人类参与者的 EEG 和行为反应，他们在频率标记的视觉刺激上执行基于特征的运动辨别任务。
