ADAPTATION OF NEUROCOMPUTING INTERFACE FOR CONTROLLING THE STUDENT'S KNOWLEDGE

Advances in development of biosensor technology in the new century allowed to start using neurocomputing interfaces and electroencephalogram (EEG) of the users to analyze human activities. Existing studies show that neurocomputing interfaces enable an objective assessment of the state of the user. In this paper we attempt to adapt neurocomputing interfaces for analysis of students' knowledge as users of information systemДостижения в области развития технологий биосенсоров в новом веке позволили приступить к использованию нейрокомпьютерных интерфейсов и электроэнцефалограмм (ЭЭГ) пользователей в анализе деятельности человека. Существующие исследования показывают, что нейрокомпьютерные интерфейсы позволяют дать объективную оценку состояния пользователя. В данной работе нейрокомпьютерные интерфейсы адаптируются для анализа знаний студентов, которые взаимодействуют с информационной системо

Practical neurophysiological analysis of readability as a usability dimension

This paper discusses opportunities and feasibility of integrating neurophysiologic analysis methods, based on electroencephalography (EEG), in the current landscape of usability evaluation methods. The rapid evolution and growing availability of low-cost, easier to use devices and the accumulated knowledge in feature extraction and processing algorithms allow us to foresee the practicality of this integration. The work presented in this paper is focused on reading and readability, identified as a key element of usability heuristics, and observable in the neurophysiologic signals' space. The experiments are primarily designed to address the discrimination of the reading activity (silent, attentive and continuous) and the verification of decreasing readability, associated with the user's mental workload analysis. The results obtained in the series of experiments demonstrate the validity of the approach for each individual user, and raise the problem of inter-subject variability and the need for designing appropriate calibration procedures for different users

NEW TECHNOLOGIES OF THE PHYSICAL LABORATORY PRACTICE

Problems and functions of a laboratory practice on physics in the modern higher school are considered. The role of information technologies in the raising of physical education efficiency is shown. Advantages and lacks of the physical laboratory practice computerization are notedРассмотрены задачи и функции лабораторного практикума по физике в современной высшей школе. Показана роль информационных технологий в повышении эффективности физического образования. Отмечены преимущества и недостатки компьютеризации физического лабораторного практикум

Movement Pattern Recognition in Physical Rehabilitation - Cognitive Motivation-based IT Method and Algorithms

In this paper, a solution is presented to support both existing and future movement rehabilitation applications. The presented method combines the advantages of human-computer interaction-based movement therapy, with the cognitive property of intelligent decision-making systems. With this solution, therapy could be fully adapted to the needs of the patients and conditions while maintaining a sense of success in them, thereby motivating them. In our modern digital age, the development of HCI interfaces walks together with the growth of users’ needs. The available technologies have limitations, which can reduce the effectiveness of modern input devices, such as the Kinect sensor or any other similar sensors. In this article, multiple newly developed and modified methods are introduced with the aim to overcome these limitations. These methods can fully adapt the movement pattern recognition to the users' skills. The main goals are to apply this method in movement rehabilitation, where the supervisor, a therapist can personalize the rehabilitation exercises due to the Distance Vector-based Gesture Recognition (DVGR), Reference Distance-based Synchronous/Asynchronous Movement Recognition (RDSMR/RDAMR) and the Real-Time Adaptive Movement Pattern Classification (RAMPC) methods

Electroencephalogram Signal Processing For Hybrid Brain Computer Interface Systems

The goal of this research was to evaluate and compare three types of brain computer interface (BCI) systems, P300, steady state visually evoked potentials (SSVEP) and Hybrid as virtual spelling paradigms. Hybrid BCI is an innovative approach to combine the P300 and SSVEP. However, it is challenging to process the resulting hybrid signals to extract both information simultaneously and effectively. The major step executed toward the advancement to modern BCI system was to move the BCI techniques from traditional LED system to electronic LCD monitor. Such a transition allows not only to develop the graphics of interest but also to generate objects flickering at different frequencies. There were pilot experiments performed for designing and tuning the parameters of the spelling paradigms including peak detection for different range of frequencies of SSVEP BCI, placement of objects on LCD monitor, design of the spelling keyboard, and window time for the SSVEP peak detection processing. All the experiments were devised to evaluate the performance in terms of the spelling accuracy, region error, and adjacency error among all of the paradigms: P300, SSVEP and Hybrid. Due to the different nature of P300 and SSVEP, designing a hybrid P300-SSVEP signal processing scheme demands significant amount of research work in this area. Eventually, two critical questions in hybrid BCl are: (1) which signal processing strategy can best measure the user\u27s intent and (2) what a suitable paradigm is to fuse these two techniques in a simple but effective way. In order to answer these questions, this project focused mainly on developing signal processing and classification technique for hybrid BCI. Hybrid BCI was implemented by extracting the specific information from brain signals, selecting optimum features which contain maximum discrimination information about the speller characters of our interest and by efficiently classifying the hybrid signals. The designed spellers were developed with the aim to improve quality of life of patients with disability by utilizing visually controlled BCI paradigms. The paradigms consist of electrodes to record electroencephalogram signal (EEG) during stimulation, a software to analyze the collected data, and a computing device where the subject’s EEG is the input to estimate the spelled character. Signal processing phase included preliminary tasks as preprocessing, feature extraction, and feature selection. Captured EEG data are usually a superposition of the signals of interest with other unwanted signals from muscles, and from non-biological artifacts. The accuracy of each trial and average accuracy for subjects were computed. Overall, the average accuracy of the P300 and SSVEP spelling paradigm was 84% and 68.5 %. P300 spelling paradigms have better accuracy than both the SSVEP and hybrid paradigm. Hybrid paradigm has the average accuracy of 79 %. However, hybrid system is faster in time and more soothing to look than other paradigms. This work is significant because it has great potential for improving the BCI research in design and application of clinically suitable speller paradigm

The effect of leads on cognitive load and learning in a conceptually rich hypertext environment

The purpose of this experiment was to determine whether leads affect cognitive load and learning from conceptually rich hypertext. Measures of cognitive load included self-report of mental effort, reading time, and event-related desynchronization percentage of alpha, beta, and theta brain wave rhythms. Conceptual and structural knowledge tests, as well as a recall measure were used to determine learning performance. Measures of learners\u27 reading ability, prior knowledge, and metacognitive awareness were employed to establish the effect of individual differences on cognitive load and learning from traditional and lead-augmented hypertext. Results demonstrated that while leads appeared to reduce brain wave activity associated with split attention, processing of redundant information contained in hypertext nodes may have increased extraneous cognitive load, and decreased germane load that is required for learning to take place. Whereas the benefits of leads relative to cognitive load and learning may have been mediated by the redundancy effect, learners with better developed metacognitive skills tended to use leads as a tool to review information in the linked nodes while revisiting content in the primary text passage. Limitations of the currently available cognitive load measures are discussed as applied to direct assessment of this theoretical construct

Comprehensive evaluation methods for translating BCI into practical applications: usability, user satisfaction and usage of online BCI systems

Although brain-computer interface (BCI) is considered a revolutionary advancement in human-computer interaction and has achieved significant progress, a considerable gap remains between the current technological capabilities and their practical applications. To promote the translation of BCI into practical applications, the gold standard for online evaluation for classification algorithms of BCI has been proposed in some studies. However, few studies have proposed a more comprehensive evaluation method for the entire online BCI system, and it has not yet received sufficient attention from the BCI research and development community. Therefore, the qualitative leap from analyzing and modeling for offline BCI data to the construction of online BCI systems and optimizing their performance is elaborated, and then user-centred is emphasized, and then the comprehensive evaluation methods for translating BCI into practical applications are detailed and reviewed in the article, including the evaluation of the usability (including effectiveness and efficiency of systems), the evaluation of the user satisfaction (including BCI-related aspects, etc.), and the evaluation of the usage (including the match between the system and user, etc.) of online BCI systems. Finally, the challenges faced in the evaluation of the usability and user satisfaction of online BCI systems, the efficacy of online BCI systems, and the integration of BCI and artificial intelligence (AI) and/or virtual reality (VR) and other technologies to enhance the intelligence and user experience of the system are discussed. It is expected that the evaluation methods for online BCI systems elaborated in this review will promote the translation of BCI into practical applications