Abstract:

Brain-Computer Interface (BCI) technology is a powerful tool used for communication between systems and users. Its main advantage is that it does not require any additional external devices or muscle intervention from the user in order to issue commands and carry out the interaction.

A BCI system is generally composed of the following components: signal acquisition, preprocessing, feature extraction, classification (detection), postprocessing and application interface. This type of interface was initially developed by the research community, primarily with biomedical applications in mind, which lead to several generations of assistive devices. Most devices were focused on restoring the movement ability for physically challenged users and replacing lost or reduced motor functionality. The positive examples and bright future prediction for BCI have prompted many researchers to study the possibility of extending the use of BCI to non-paralyzed humans through medical applications. Furthermore, the scope of research has been even further widened to include non-medical applications, especially educational systems. Those systems utilize brain electrical signals to determine the degree of clarity of studied information. In recent years, researchers have identified neural signatures of explicit and implicit learning. Explicit learning is a learning type achieved through conscious awareness, and it occurs when a person is thinking about the matter he/she is learning. Implicit learning is the opposite of explicit, and it corresponds to learning that is often called motor skill learning or muscle memory. Individuals are becoming more and more capable of performing a particular action after repeating it several times, but they are not able to precisely articulate what exactly they are learning, for example riding a bike.

One of the common approaches in implementing BCI systems is using electroencephalography (EEG). EEG is the recording of electrical activity along the scalp. It is performed by measuring the voltage fluctuations accompanying neurotransmission activity within the brain. EEG devices usually have a cap-like design with one or more attached electrodes used for measuring the brain activity. The main advantage of EEG devices is their usability compared to other types of devices for brain signal recording. It is easy to use, portable and inexpensive, which makes it ideal for widespread application in education. The goal of this paper is to inquire the possibility of using one of such devices in education. Within the context of this research, we have designed and conducted a preliminary experimental study that will enable further evaluation of the proposed approach and establishing of the main focus points for future research.

Keywords:

Brain-computer interface, education, learning systems.