Sustained attention is a cognitive ability to maintain task focus over extended periods of time (Mackworth, 1948; Chun, Golomb, & Turk-Browne, 2011). In this study, scalp electroencephalography (EEG) signals were processed in real time using a 32 dry-electrode system during a sustained visual attention task. An attention training paradigm was implemented, as designed in DeBettencourt, Cohen, Lee, Norman, and Turk-Browne (2015) in which the composition of a sequence of blended images is updated based on the participant's decoded attentional level to a primed image category. It was hypothesized that a single neurofeedback training session would improve sustained attention abilities. Twenty-two participants were trained on a single neurofeedback session with behavioral pretraining and posttraining sessions within three consecutive days. Half of the participants functioned as controls in a double-blinded design and received sham neurofeedback.

During the neurofeedback session, attentional states to primed categories were decoded in real time and used to provide a continuous feedback signal customized to each participant in a closed-loop approach. We report a mean classifier decoding error rate of 34.3% (chance $=$ 50%). Within the neurofeedback group, there was a greater level of task-relevant attentional information decoded in the participant's brain before making a correct behavioral response than before an incorrect response. This effect was not visible in the control group (interaction $p=723$e$-$4), which strongly indicates that we were able to achieve a meaningful measure of subjective attentional state in real time and control participants' behavior during the neurofeedback session. We do not provide conclusive evidence whether the single neurofeedback session per se provided lasting effects in sustained attention abilities.

We developed a portable EEG neurofeedback system capable of decoding attentional states and predicting behavioral choices in the attention task at hand. The neurofeedback code framework is Python based and open source, and it allows users to actively engage in the development of neurofeedback tools for scientific and translational use.

持续注意力是一种在较长时间内保持任务专注的认知能力（Mackworth，1948 年；Chun、Golomb 和 Turk-Browne，2011 年）。在这项研究中，头皮脑电图 (EEG) 信号在持续视觉注意任务期间使用 32 干电极系统实时处理。实施了一种注意力训练范式，如 DeBettencourt、Cohen、Lee、Norman 和 Turk-Browne (2015) 中设计的，其中混合图像序列的组成根据参与者对初始图像类别的解码注意力级别进行更新。假设单次神经反馈训练会提高持续注意力的能力。22 名参与者在连续三天内接受了单一的神经反馈训练，包括行为训练前和训练后训练。

在神经反馈会话期间，对启动类别的注意力状态被实时解码，并用于以闭环方法为每个参与者提供定制的连续反馈信号。我们报告平均分类器解码错误率为 34.3%（机会$=$50%）。在神经反馈组中，在做出正确行为反应之前，参与者大脑中解码的与任务相关的注意力信息水平高于错误反应之前。这种效果在对照组中不可见（交互作用$磷=723$电子$——$4），这强烈表明我们能够实时实现对主观注意力状态的有意义的测量，并在神经反馈会话期间控制参与者的行为。我们没有提供单次神经反馈会话本身是否对持续注意力能力产生持久影响的确凿证据。

我们开发了一种便携式 EEG 神经反馈系统，能够解码注意力状态并预测手头注意力任务中的行为选择。神经反馈代码框架是基于 Python 的开源代码框架，它允许用户积极参与用于科学和翻译用途的神经反馈工具的开发。