Improving the Effectiveness of Learning with the Help of Neurocomputer Interface

Abstract

The article considers modern technologies for reading signals from the human brain and nervous system and selects the optimal technology to improve the efficiency of adult learning with the help of a neurocomputer interface. Existing brain-computer interfaces (BCI) technologies can be divided into invasive and non-invasive. The first, invasive BCIs, are neuroimplants in certain parts of the brain that work on the basis of electrocorticography (ECOG) or intracranial EEG (iEEG) technology and do not require deep intervention in brain structures; or another invasive BCIs, based on intracortical recording technology using implants with electrodes placed in brain closer to the signal source, and required more complicate operation. The second, non-invasive BCI, reads signals from the brain and nervous system and is based on electroencephalogram (EEG). Compared to invasive BCIs with their more accurate signal, transcranial BCIs communicate with the brain through the skull bones, muscles, and all tissues. Their use does not require intervention in the human body. To increase the effectiveness of training, there was chosen a physiotherapeutic method of transcranial electrical stimulation (TES) in combination with a braincomputer interface based on electroencephalography (EEG), as the most accessible non-invasive method of exposure and feedback due to BCI without known side effects to mental functions and personality. The use of brain-computer interfaces, in particular transcranial electrical stimulation in combination with electroencephalography, increases cognitive abilities in learning, including multitasking. This method can also be used to increase the effectiveness of human assimilation of the necessary new digital environments and is used not only for training complex professions, but also for the masses. Side effects on higher mental functions and personality have not been sufficiently studied to recommend or avoid the use of neurocomputer interfaces for widespread use in education.