Mental load recording using EEG in human-machine interaction with visual processing, decision making and movement controlcessing, decision making and movement control

Abstract

Introduction: The main interest of the study of the interactions between a person with a team, system or machine, from a cognitive approach, is to know the level of stress or mental load that occurs. The electroencephalogram (EEG) is a way to observe these levels through the behavior of neurological rhythms and with brain-computer interfaces (BCI), record them to facilitate their analysis and future application in emerging technologies such as AI, IoT and science of data.

Method: A simple and short-duration task was performed in 30 repetitions by a volunteer, who was placed with electrodes using the 10-20 system to record the mental load in Pz, O1 and O2 of Alpha and Theta with the Aura interface of Mirai Innovation Research Institute and Jasp software to determine the Pearson correlation coefficient.

Results: 30 databases were registered, which were processed in Jasp to calculate the correlation between Alpha and Theta rhythms, at locations Pz, O1 and O2.

Conclusions: The processing of information obtained concluded that there is a positive correlation between both rhythms, information that coincides with that established in the available literature and validates the rest of the data to be used in the construction of the neurological basis that intervenes in visual processing tasks, taking decision making and motion control, for future emerging technology developments.

Keywords: mental load; EEG; human-machine interaction.