Simultaneous EEG-fNIRS Data on Learning Capability via Implicit Learning Induced by Cognitive Tasks

The dataset procured by this research was used to systematically identify the relationship between implicit learning events and neurological signals’ characteristics by measuring the participant’s brain state as they performed cognitive tasks experiments. Implicit learning is the ability to learn complex information without explicit awareness, commonly seen in small children while learning to speak their native language for the first time without learning grammar. Research suggests that people skilled at implicit learning tend to learn faster. The implicit learning of the underlying rules, commonly used in learning approaches, is of interest. Simultaneous measurement of Encephalography (EEG) and Functional Near-Infrared Spectroscopy (fNIRS) signals at shared locations over the head was obtained to understand participants’ learning ability in a laboratory setting. Utilizing the data obtained from measuring both electrophysiological activity and hemodynamic responses at the same locations at the same time could bring about new insights, leading to new findings for neurovascular coupling in the brain and extending knowledge on how brains work. This dataset comprised EEG and fNIRS data from thirty healthy adults (age 21-29) while undergoing cognitive serial reaction times task experiments. The participants’ data from each data type are divided into two main groups: participants deemed to have achieved implicit learning during the experiment and those who did not. The differentiations were evaluated during the post-interviews of the experiment. This grouping of datasets could be used for classification applications. Brain data in this research can help identify prominent brain areas and features or patterns corresponding to implicit learning events. Thus, it can be used to identify and develop a learning detection model. With the detection model, a form of neurofeedback training regimen or therapy could be developed to produce a better and novel teaching approach.

A data article is being submitted for publication in a journal. If the manuscript is accepted, we will provide a link to the article for more information about the dataset.

本研究所获取的数据集被用于系统地识别隐式学习事件与神经信号特征之间的关系，通过测量参与者在执行认知任务实验期间的大脑状态来实现。隐式学习是指在没有明确意识的情况下学习复杂信息的能力，这在年幼儿童首次学习母语时尤为常见，他们在学习说话时无需学习语法。研究指出，擅长隐式学习的人往往学习速度更快。对常用学习方法中基础规则的隐式学习尤其引起关注。通过对头部共享位置同时进行脑电图（EEG）和功能性近红外光谱（fNIRS）信号的同步测量，以理解参与者在实验室环境下的学习能力。利用在同一位置同时测量电生理活动和血流动力学反应所获得的数据，可能带来新的洞见，进而为神经血管耦联在脑部的新发现提供支持，并扩展对大脑工作原理的知识。该数据集包含了三十名健康成年人（年龄21-29岁）在执行认知连续反应时间任务实验期间所获得的EEG和fNIRS数据。根据实验中的隐式学习情况，将参与者的数据分为两大组：被认为在实验中实现了隐式学习的参与者和未实现隐式学习的参与者。这些差异在实验后的访谈中被评估。这种数据集的分组可用于分类应用。本研究中的脑部数据有助于识别与隐式学习事件相对应的显著脑区、特征或模式。因此，它可以用于识别和发展学习检测模型。通过检测模型，可以开发一种形式的神经反馈训练方案或治疗方法，以产生更优、更创新的教学方法。一篇关于数据集的论文正在提交至期刊发表。如果稿件被接受，我们将提供文章链接以获取更多关于数据集的信息。