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

Description

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.

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