505 research outputs found

    Extended attention span training system

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    Attention Deficit Disorder (ADD) is a behavioral disorder characterized by the inability to sustain attention long enough to perform activities such as schoolwork or organized play. Treatments for this disorder include medication and brainwave biofeedback training. Brainwave biofeedback training systems feed back information to the trainee showing him how well he is producing the brainwave pattern that indicates attention. The Extended Attention Span Training (EAST) system takes the concept a step further by making a video game more difficult as the player's brainwaves indicate that attention is waning. The trainee can succeed at the game only by maintaining an adequate level of attention. The EAST system is a modification of a biocybernetic system that is currently being used to assess the extent to which automated flight management systems maintain pilot engagement. This biocybernetic system is a product of a program aimed at developing methods to evaluate automated flight deck designs for compatibility with human capabilities. The EAST technology can make a contribution in the fields of medical neuropsychology and neurology, where the emphasis is on cautious, conservative treatment of youngsters with attention disorders

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

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    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

    Brain Waves and Connectivity of Autism Spectrum Disorders

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    AbstractThis research reports the brain waave pattern of individuals with ASD and to pinpoint the anomalies of ASD and its difference with the normal group. The findings revealed a general disruption in the overall connectivity of the different lobes known as hyper or hypo connectivity with excessive presence of slow wave (delta) at the frontal lobe and deficiency of beta in most of the brain regions. Other anomalies includes low alpha at the sensory motor regions, excess alpha in the left hemisphere and excess theta in the right frontal region. These anomalies explain the associated problem in attention, anxiety and social behaviors of ASD

    Neuromuse: training your brain through musical interaction

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    Presented at the 15th International Conference on Auditory Display (ICAD2009), Copenhagen, Denmark, May 18-22, 2009Human aural system is arguably one of the most refined sensor we posess. It is sensitive to such highly complex stimuli as conversa- tions or musical pieces. Be it a speaking voice or a band playing live, we are able to easily perceive relaxed or agitated states in an auditory stream. In turn, our own state of agitation can now be detected via electroencephalography technologies. In this pa- per we propose to explore both ideas in the form of a framework for conscious learning of relaxation through sonic feedback. Af- ter presenting the general paradigm of neurofeedback, we describe a set of tools to analyze electroencephalogram (EEG) data in real- time and we introduce a carefully designed, perceptually-grounded interactive music feedback system that helps the listener keeping track of and modulate her agitation state as measured by EEG

    Recent Applications in Graph Theory

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    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

    Intelligence quotient and perceptual ability: an inter-relationship based on brainwave power ratio features

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    Cognitive ability refers to the characteristic approach by the brain in processing information. These can be observed through various aspects of cognition such as intelligence and perceptual ability. Studies have shown that both mental constituents originate from the same neurological substrate in the prefrontal cortex. Hence, the paper discusses the relationship between intelligence and perceptual ability using electroencephalogram (EEG) features. The study is based on resting brainwave of fifty samples and focused on the left and right prefrontal cortex. The intelligence quotient (IQ) scores obtained are then used to establish the control groups. Subsequently, the pattern of alpha and theta power ratio for each IQ level is observed and successfully correlated with perceptual ability through the Neural Efficiency Hypothesis of intelligence.Keywords: EEG; intelligence; IQ; perceptual ability; power ratio

    Brain-Computer Interface

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    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

    Can Portable EEG Headsets be Used to Determine if Students are Learning?

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    This study examined EEGs recorded from a single-channel, portable EEG headset (NeuroSky MindWave) during the study period of a paired-associate word paradigm which used Swahili words and their English meanings. It was hypothesized that there would be a significant difference in gamma, theta, and beta band powers for when students recalled words correctly vs. when they did not recall correctly on a subsequent test. There were 35 participants who consisted of students that volunteered at the University of Memphis (20 females and 15 males, 31 of which were right-handed and 4 which were left-handed). A paired-samples t-test suggested that there was a higher mean z-score for brainwave activity during the study period in the high gamma range (41-49.75Hz) for when participants did not recall words correctly on a test, which was opposite of what previous research has found regarding encoding. Based on the results of this study, the MindWave seems to capture muscle activity and/or saccadic behavior that is suggested by higher gamma maximums on average in the study period for word-pairs which resulted in failed recall. Exploratory results may lend insight to future work using portable EEG devices. This study\u27s main objective was to determine if portable EEG devices could be used to determine when students learn new information. Further testing, especially using other portable EEG devices is necessary to answer this question

    Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals

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    The major aim of this paper is to explain the data poisoning attacks using label-flipping during the training stage of the electroencephalogram (EEG) signal-based human emotion evaluation systems deploying Machine Learning models from the attackers' perspective. Human emotion evaluation using EEG signals has consistently attracted a lot of research attention. The identification of human emotional states based on EEG signals is effective to detect potential internal threats caused by insider individuals. Nevertheless, EEG signal-based human emotion evaluation systems have shown several vulnerabilities to data poison attacks. The findings of the experiments demonstrate that the suggested data poison assaults are model-independently successful, although various models exhibit varying levels of resilience to the attacks. In addition, the data poison attacks on the EEG signal-based human emotion evaluation systems are explained with several Explainable Artificial Intelligence (XAI) methods, including Shapley Additive Explanation (SHAP) values, Local Interpretable Model-agnostic Explanations (LIME), and Generated Decision Trees. And the codes of this paper are publicly available on GitHub

    Brainwave Detection Model for Panic Attacks Based on Event-related Potential

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    Panic attacks could adversely affect a patient’s daily life and can pose risks to others. The symptoms of panic attacks can be timely observed by detecting the brainwave. This research presents a model that can evaluate the level of panic attack symptoms using the brainwaves detection during (or before) the symptom occurs. It helps monitor the patient’s brainwave based on Event-related potential (ERP). The model is derived from the simulation with horror pictures and frightening sound on the experimental group of 30 people. The survey related to symptoms has been used regarding to the criteria of the Beck Anxiety Inventory (BAI). The results showed that there is a consistent change of Electroencephalography (EEG) in each change of brainwaves where its quantitative analysis found that the changes of Beta, Gamma, and Alpha directly affect the model of Brainwaves Panic Attacks Measurement (BPAM) which is associated with panic attacks. 1 out of 30 cases scored higher than the average of the BPAM at 220 The Model BPAM can detect the risk to be Panic Attack compared to the use of tests Beck Anxiety Inventory (BAI) were found to be consistent. The test value BAI Score 19-63 was BPAM Score 401-1000. In addition, the results found that at P300 the brainwave pattern of EEG in meditation had decreased significantly whereas the brainwave related to attention had increased considerably for which human brain can potentially respond to stimulated external events
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