Games and Brain-Computer Interfaces: The State of the Art

BCI gaming is a very young field; most games are proof-of-concepts. Work that compares BCIs in a game environments with traditional BCIs indicates no negative effects, or even a positive effect of the rich visual environments on the performance. The low transfer-rate of current games poses a problem for control of a game. This is often solved by changing the goal of the game. Multi-modal input with BCI forms an promising solution, as does assigning more meaningful functionality to BCI control

BrainBasher: a BCI Game

Brain-computer interaction (BCI) is starting to focus on healthy subjects. This research adresses the effects of using this novel input modality to control a simple game, and also looks into the beneficial effects of bringing game elements into BCI experiments. A simple BCI game has been developed and evaluated with fifteen subjects using the Game Experience Questionnaire (GEQ) developed at the Eindhoven Game Experience Lab. Three variations of the game were evaluated for comparison: the original game with BCI input, one with keyboard input, and one with a more clinical look leaving out all extraneous information. The keyboard-controlled game was considered easy and boring, whereas using BCI for input resulted in a more challenging, immersive and richer experience. The design and additional information presented by the game also resulted in higher immersion compared to the clinical design

Covid-19 and marketing strategies

COVID-19 is the disease caused by a new coronavirus, SARS-CoV-2. It was named a coronavirus because the virus represents crown-like spikes on the outer surface of the virus (WHO, 2020). According to the World Health Organization (WHO, 2020), the first case reported was on December 31, 2019. The first case reported was a 'viral pneumonia' cluster at a Seafood Wholesale Market in Wuhan, China. On March 11, 2020, WHO declared the COVID-19 outbreak as a global pandemic. This disease was characterized as a pandemic because of its rapid spread, affecting many people and occurring worldwide. Common symptoms of this disease are high fever, dry cough, and fatigue. Other symptoms can be loss of taste and smell, headache, muscle pain, etc

Statistical Analysis of Balanced Brain and IQ Applications

EEG signal research had been studied massively in such balanced brain and IQ applications. This paper focuses on correlation between balanced brain and Intelligence Quotient (IQ) applications. At first, the raw EEG signals from both applications need to pre-process to remove artefact and unwanted frequency. Then, the EEG signals will go through statistical processes which are Scatterplot and Correlation test. As a result, there is correlation between the balanced brain and IQ application with strong and significant Pearson correlation

Identification of EEG signal patterns between adults with dyslexia and normal controls

Electroencephalography (EEG) is one of the most useful techniques used to represent behaviours of the brain and helps explore valuable insights through the measurement of brain electrical activity. Hence, it plays a vital role in detecting neurological disorders such as epilepsy. Dyslexia is a hidden learning disability with a neurological origin affecting a significant amount of the world population. Studies show unique brain structures and behaviours in individuals with dyslexia and these variations have become more evident with the use of techniques such as EEG, Functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) and Positron Emission Tomography (PET). In this thesis, we are particularly interested in discussing the use of EEG to explore unique brain activities of adults with dyslexia. We attempt to discover unique EEG signal patterns between adults with dyslexia compared to normal controls while performing tasks that are more challenging for individuals with dyslexia. These tasks include real--‐word reading, nonsense--‐ word reading, passage reading, Rapid Automatized Naming (RAN), writing, typing, browsing the web, table interpretation and typing of random numbers. Each participant was instructed to perform these specific tasks while staying seated in front of a computer screen with the EEG headset setup on his or her head. The EEG signals captured during these tasks were examined using a machine learning classification framework, which includes signal preprocessing, frequency sub--‐band decomposition, feature extraction, classification and verification. Cubic Support Vector Machine (CSVM) classifiers were developed for separate brain regions of each specified task in order to determine the optimal brain regions and EEG sensors that produce the most unique EEG signal patterns between the two groups. The research revealed that adults with dyslexia generated unique EEG signal patterns compared to normal controls while performing the specific tasks. One of the vital discoveries of this research was that the nonsense--‐words classifiers produced higher Validation Accuracies (VA) compared to real--‐ words classifiers, confirming difficulties in phonological decoding skills seen in individuals with dyslexia are reflected in the EEG signal patterns, which was detected in the left parieto--‐occipital. It was also uncovered that all three reading tasks showed the same optimal brain region, and RAN which is known to have a relationship to reading also showed optimal performance in an overlapping region, demonstrating the likelihood that the association between reading and RAN reflects in the EEG signal patterns. Finally, we were able to discover brain regions that produced exclusive EEG signal patterns between the two groups that have not been reported before for writing, typing, web browsing, table interpretation and typing of random numbers

Natural User Interface Usability Research in Context of Curved Displays Systems

Continuous development of information technologies makes us review ex-isting rules and recommendations designed to improve the efficiency of IT use, to ensure optimal working conditions for the users, to increase produc-tivity, security and to protect human health. Relevant researrch in the field of computer engineering is performed in the dissertation. The thesis analyzes natural user interfaces and their usabil-ity (efficiency, productivity and satisfaction with witch a particular user can reach specific goals in a specific environment) for performing of various functions. This dissertation examines factors, which determine efficiency of usability, and how efficiency is influenced by a curved display. The problem is relevant and the raised goal and objectives are new from the point of view of science. First of all, the thesis examines how to improve working conditions by developing graphical user interface of the infor-mation systems. Secondly, the influence of information submission to human, while one is performing task and specific domain tasks using graph-ical user interface, is examined. As there is no common opinion on how to create natural user interfaces and there is no definite set of parameters which determine the efficiency of usability, performed experimental research is an important contribution to the solution of these problems

Enhancement and optimization of a multi-command-based brain-computer interface

Brain-computer interfaces (BCI) assist disabled person to control many appliances without any physically interaction (e.g., pressing a button). SSVEP is brain activities elicited by evoked signals that are observed by visual stimuli paradigm. In this dissertation were addressed the problems which are oblige more usability of BCI-system by optimizing and enhancing the performance using particular design. Main contribution of this work is improving brain reaction response depending on focal approaches

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems