=Paper=
{{Paper
|id=Vol-3309/short3
|storemode=property
|title=Cognitive design of educational brain-computer interfaces
|pdfUrl=https://ceur-ws.org/Vol-3309/short3.pdf
|volume=Vol-3309
|authors=Vitalii Mykhalchuk
|dblpUrl=https://dblp.org/rec/conf/ittap/Mykhalchuk22
}}
==Cognitive design of educational brain-computer interfaces==
https://ceur-ws.org/Vol-3309/short3.pdf
Cognitive design of educational brain-computer interfaces
Vitalii Mykhalchuk
a
Taras Shevchenko National University of Kyiv, Faculty of Informational Technology, Kyiv, Ukraine
Abstract
This paper represents a conceptual description of the cognitive approach for the design of the brain-
computer interface, which can be implemented in educational environments. Study based on
performance metrics analysis and interpretation mental dynamics in daily routine inspired model. The
central object of interest is the development of brain-computer literacy for neuroplasticity training
implementation for efficient working and study activities and improving personal skills.
Keywords 1
Brain-computer interfacing, cognitive network, neuroplasticity, neuro-management, decision-making.
1. Introduction
At the present stage of development, the use of most brain-computer interfaces aims to train skills and
develop cognitive abilities, as well as collect user data to overcome illiteracy and improve technology. It is
also worth noting the success of the introduction of brain-computer interfaces to track well-being, and self-
care, as well as combating the complex of hyperactivity or lack of attention of children. The brain-computer
interface is also used for navigation and space mastery experiments, consciousness loading, and
neuroaesthetics research.
The most important task, that the educational environments enforced with the brain-computer interface can
solve is human-computer interaction skills development and training. Also, such networks can provide
platforms for needed and implemented workforce abilities of new trends such as neuromanagement. The
central subject of those research is the decision-making process and one of the last popular directions of this
approach is an implementation of skills and increasing of efficiency of working activity by self-development
and training.
The feature of human mental action is neuroplasticity, which allows to increase own activity in work and
daily life by consciousness augmentation, which influence is so expected nearest time. The brain-computer
literacy becomes really needed to create abilities and to develop neuroplasticity moderation and using it for
improving the efficiency of own activities and one of the obvious ways its implementation is educational
environments, where students can combine training of special and new skills.
2. Related works
There are a lot of real implemented samples of brain-computer interfaces today. A few projects should be
named are: a system for car-drive and race (Emotiv and SIMUSAFE), aviation (g.tec and BrainSigns),
neuromarketing (Emotiv, BrainSigns, JLL), mental health and self-care (Emotiv, g.tec, BitBrain, NeuroSky,
BrainSigns) and advertisement (Emotiv, NeuroSky, g.tec). All these companies have large experience in
assistive technologies providing, but they are the ones who inaugurated BCI users’ society [1].
The basis of BCI network development and use is well illustrated in Brain-Neuro-Computer Interface
Society report (2015). This paper is one of the most successful descriptions of today’s BCI essence and possible
steps for its improvement in human community [2].
The last neuromanagement achievements are improved by BrainSigns and JLL. The first companies
established workload idea with EEG for state recognition technology.
The basis of neuromanagement can be found in Neuromanagement and leadership (Venturella I. et all,
2018). This work describes EEG analysis of the mental activity in organization study according to the newest
tendencies of neuroscience analysis in cognitive sciences as well-being and workload through typical human
activity [3].
ITTAP’2022: 2nd International Workshop on Information Technologies: Theoretical and Applied Problems, November 22–24, 2022, Ternopil, Ukraine
EMAIL: mvv948@gmail.com (A1).
ORCID: 0000-0002-7559-999X (A. 1).
©️ 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
For experiment making, there are a few ways to build a study. Upon them is data science from used BCI.
Most of the modern systems allow to fix study results in comfortable representation, for example, as raw EEG
data appropriate for any next processing or typically classified by study settings. For example, in PsychoPy
you can use a simple projecting interface with a comfortable visual development platform for processing data
from different devices.
Experiments and tests usually are based on common popular methods with different levels depending on
the target tasks field. This research demonstrates a result of training with Stroop test control, but there are a lot
of other strategies which can be combined according to the purpose and the capacity of the system.
3. Proposed methodology
A typical organization of a modern intelligent environment equipped with a brain-computer interface passes
through several stages and has its own structure and features. Depending on the scope of application, different
design approaches are used, but the cognitive principles are still stay the basics for intellectual environments
organization [4].
3.1. The cognitive approach in BCI enforced educational environments design
In educational intelligent systems, the brain-computer interface can be implemented as a complex
amplifying component that ensures the optimization of activities in several areas. The first is the brain-
computer interface module itself, which adds a new degree of quality to the management of the environment
and the operation of information. The second is a mandatory module of training own skills, which helps to
realize the skills of mental management in a smart environment and expands cognitive competencies (soft
skills). The third is specially focused on a specific application. It is also possible to expand efficiency of the
system with the type of hyperscanning, brain-to-brain interaction through the cognitive network, discussion,
and competition or game as an organizational approach.
One of the problems with the implementation of brain-computer interfaces is the long-term development
of user mental management skills and maintaining cognitive capacity. An indicator of readiness is a low level
of brain-computer illiteracy. Good data for activity is a reaction, good health, especially clear neurofeedback
of sufficient power [5].
Then brain-assisted intellectual environment must be equipped with means as technical as software for the
personal development of BCI-abilities and fit own skills constantly. To achieve these conditions the
organization of the BCI-environment should be embodied in efficient projection. The most successful possible
realization of such an idea is well argument decision with enough efficiency. A working group of users can be
represented as a small functional psychological group. Then for creating the special BCI the patterns for
common tasks and features of interaction must be studied, from which the suitable must be selected and
brought into the samples base, if machine learning is used for system development. This compliance is
regulated with basic standards and recommendations from the distributor of equipment and software [6].
3.2. Multitarget BCI networking
The value of individual mental activity features factor, especially in international groups, should be
considered. To pretend such causes, the program for common training must be successfully embodied into a
real system and necessarily provided with its use.
For the efficient design with correct implementation of the cognitive approach for the educational
environment, a few necessary principles must be obligatory observed. The first between them is the immersive
component of user’s interface. Such condition is necessary to include dual concept as system preset as user's
skills provided.
Personal-oriented BCI representation usually has a typical user profile descriptive mode, which can be
described as idiolect. A strong well-trained personal profile will be very efficient in the realization of such
functional patterns as mental command and identification (personal BCI recognition) [7].
Sure, all profiles can be improved in the testing process with a unique machine learning strategy for
achievement of desired aim and level, which must be described at the beginning of design with a clear number
of features (competencies) and its quality indicators (represented as Professional Brain view or Cognitive
Image).
For small groups (5 persons) we can represent it with "jargon" – proficiency-oriented lexis and own
semantics. Usually, such vocabulary is embodied as a command space built with ordered logic-shaped
semantics of its interactions. Each command is a cognemic formation into some space of operational abilities
globally understood as a space of mental activity with the universal grammar of action execution with trained
free will efforts and general translation method namely EEG with chosen technology of signal reception
(fNIRS, MEG, SSVEP, P300 etc.) for recognized data input into the environment for the next management
realization and interface implementation (Fig. 1) [8].
Figure 1: Conceptual Scheme of Modern BCI System for Efficient Network
This structure helps to achieve successful multitask interaction and is acceptable for real enterprises and
educational networks including developers and agile platforms providing improving quality and evolutional
stability at the same time.
Target tasks can be differentiated by some levels of activity and studies can include quality and priority
accordance monitor also. For example, basic working actions can be noticed by screen-timing control in
combination with pression and recall analysis. Mindfulness tasks can include individual research on personal
psychological activity features namely real neuromarketing on the neuroplasticity training process.
4. Results
For an illustration of the described method, we studied individual performance metrics dynamics during
neuroplasticity training in the improvised environment using EmotivLABs BCI. For the primary and additional
research, we have used EmotivBCI and PRO applications, so, our study includes data from different periods
and conditions of activity such as routine, tests, stress situations, training, and so on.
Emotiv methods allow to classify a current mental activity based on EEG analysis and represent its results
in a few modes. The constant is neuromanagement performance metrics, which illustrated the dynamical
representation of activity during the task solving with a used interval of changes in 5 sec. The resultant norms
of the efficient performance, collected by the research of 20 attempts with different exercises and training
combinations are 54% value for engagement, 21% - exciting, 35% - focus, 21% - interest, 48% - relaxation,
and 20% of stress (Figure 2).
Figure 2: Typical Performance Metrics studied by Emotiv BCI analysis through routine activity2.
Control tests included attention, decision, workload, and emotional metrics. In improvised situations tested
persons, who will more flexible, show the higher reaction in decision-making process then untrained. All these
results you can find in the Figure 3 graph, based on Stroop test control. As stimuli in this training randomly
colored words are used. Then the accuracy is dependent on correct detection of color confirmed by appropriate
key pressing or by voice input. Possible absolute BCI can include a biosensual device for throat impulses
sensing that allows to read and recognize appropriate lingual activity figures. Also, BCI can be combined with
eye-blinking recognition. These directions should have special research and discussion [9].
Figure 3: Typical dynamics of neuroplasticity in Stroop test training using Emotiv BCI analysis.
By control cuts: 1 – test introduction attempt, first metrics collection, individual image; 2, 3 – correction of
individual metrics, development for training; 4, 5 – fixation of skills, current test; 6 – combining with
meditation; 7 – stress situation, spontaneous test; 8-12 next 10 attempts training with the same exercises.
2
All values are demonstrating a rate of workload as a percentage relative to the absolute value of performance. Used indicators based on 5 seconds
classified study of EEG data from mental activity during the task solving (means decision-making in case of the test).
5. Discussion
The next levels of workload and cognitive organization of efficient network development obviously will
include a range of improvements for special tasks and conditions. Research and implementation of hybrid BCI
require a separate careful study of software connection for target interests achieving and right system design.
Besides, this direction gets closer to the technology of invisible computing and intelligent environments using.
Individuality has a large importance in the development and implementation of cognitive networks based
on BCI. The well-trained system must have frequent data collecting and serious processing to provide high-
quality data representation and clear settings with flexible functionality. The user profile must have a stable
training program with regular updates.
The next development of described approach should continue accordingly to the conditions and
requirements of chosen environment. To achieve the needed quality and efficiency of the system it is important
to remember that a special attention must be paid to the software choice and system design.
The nearest conceptual approach which obviously should be considered is mindfulness training. Such
ideology can improve the strategy of tests and increase the efficiency of the system, especially in long-term
projects such as life-long studies.
6. Acknowledgments
The cognitive approach can be implemented with obligate changes and decisions suitable to chosen network
features and its appointment. The optimal number of users for the system may be changed in a combined mode
of design when some features are simplified, and the basic system is playing the role of server and moderator
administrates the network.
7. References
[1] Timothy K. Lam, Oshin Vartanian, Justin G. Hollands. The brain under cognitive workload: Neural
networks underlying multitasking performance in the multi-attribute task battery, Neuropsychologia,
Volume 174 (2022). https://doi.org/10.1016/j.neuropsychologia.2022.108350.
[2] Brunner, C., et al. BNCI Horizon 2020: towards a roadmap for the BCI community. (2015) http://bnci-
horizon-2020.eu/roadmap
[3] Venturella, I., Fronda, G., Vanutelli, M. E., Balconi, M. Neuromanagement and leadership: an EEG
hyperscanning study, Poster, in Proceedings of the "SEPEX – SEPNECA – AIP experimental Joint
Conference", (Madrid, 03-06 July 2018), Fundación UNED, Madrid (2018): 64-64
http://hdl.handle.net/10807/131920.
[4] Mykhalchuk, V. The prospects for BCI in e-society. Management of Development of Complex Systems.
Issue, 50. (2022) P. 101-106.
[5] Wegemer, Ch. Brain-computer interfaces and education: the state of technology and imperatives for the
future. International Journal of Learning Technology. (2019)
https://www.inderscienceonline.com/doi/10.1504/IJLT.2019.101848
[6] Kadosh, R.C. (Ed.) The Stimulated Brain: Cognitive Enhancement Using Non-invasive Brain
Stimulation, Elsevier, London, UK (2014). https://www.elsevier.com/books/the-stimulated-brain/cohen-
kadosh/978-0-12-404704-4
[7] Toppi J, et al. Investigating Cooperative Behavior in Ecological Settings: An EEG Hyperscanning Study.
Plos1, (2016). https://doi.org/10.1371/journal.pone.0154236
[8] Jiang, L., Stocco, A., Losey, D.M. et al. BrainNet: A Multi-Person Brain-to-Brain Interface for Direct
Collaboration Between Brains. Sci Rep 9, 6115 (2019)
[9] Mykhalchuk, V. Description of cognitive BCI system. github.com (2022). https://github.com/vit-
mych/cognitive-network.