Bacteria Hunt: Evaluating multi-paradigm BCI interaction

The multimodal, multi-paradigm brain-computer interfacing (BCI) game Bacteria Hunt was used to evaluate two aspects of BCI interaction in a gaming context. One goal was to examine the effect of feedback on the ability of the user to manipulate his mental state of relaxation. This was done by having one condition in which the subject played the game with real feedback, and another with sham feedback. The feedback did not seem to affect the game experience (such as sense of control and tension) or the objective indicators of relaxation, alpha activity and heart rate. The results are discussed with regard to clinical neurofeedback studies. The second goal was to look into possible interactions between the two BCI paradigms used in the game: steady-state visually-evoked potentials (SSVEP) as an indicator of concentration, and alpha activity as a measure of relaxation. SSVEP stimulation activates the cortex and can thus block the alpha rhythm. Despite this effect, subjects were able to keep their alpha power up, in compliance with the instructed relaxation task. In addition to the main goals, a new SSVEP detection algorithm was developed and evaluated

The Impact of Flow in an EEG-based Brain Computer Interface

Major issues in Brain Computer Interfaces (BCIs) include low usability and poor user performance. This paper tackles them by ensuring the users to be in a state of immersion, control and motivation, called state of flow. Indeed, in various disciplines, being in the state of flow was shown to improve performances and learning. Hence, we intended to draw BCI users in a flow state to improve both their subjective experience and their performances. In a Motor Imagery BCI game, we manipulated flow in two ways: 1) by adapting the task difficulty and 2) by using background music. Results showed that the difficulty adaptation induced a higher flow state, however music had no effect. There was a positive correlation between subjective flow scores and offline performance, although the flow factors had no effect (adaptation) or negative effect (music) on online performance. Overall, favouring the flow state seems a promising approach for enhancing users' satisfaction, although its complexity requires more thorough investigations

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology

EEG-based Brain-computer interfaces (BCI) are facing grant challenges in their real-world applications. The technical difficulties in developing truly wearable multi-modal BCI systems that are capable of making reliable real-time prediction of users’ cognitive states under dynamic real-life situations may appear at times almost insurmountable. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report our attempt to develop a pervasive on-line BCI system by employing state-of-art technologies such as multi-tier fog and cloud computing, semantic Linked Data search and adaptive prediction/classification models. To verify our approach, we implement a pilot system using wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end fog servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end cloud servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch and the UCSD Movement Disorder Center to use our system in real-life personal stress and in-home Parkinson’s disease patient monitoring experiments. We shall proceed to develop a necessary BCI ontology and add automatic semantic annotation and progressive model refinement capability to our system

Neural correlates of flow, boredom, and anxiety in gaming: An electroencephalogram study

Games are engaging and captivating from a human-computer interaction (HCI) perspective as they can facilitate a highly immersive experience. This research examines the neural correlates of flow, boredom, and anxiety during video gaming. A within-subject experimental study (N = 44) was carried out with the use of electroencephalogram (EEG) to assess the brain activity associated with three states of user experience - flow, boredom, and anxiety - in a controlled gaming environment. A video game, Tetris, was used to induce flow, boredom, and anxiety. A 64 channel EEG headset was used to track changes in activation patterns in the frontal, temporal, parietal, and occipital lobes of the players\u27 brains during the experiment. EEG signals were pre-processed and Fast Fourier Transformation values were extracted and analyzed. The results suggest that the EEG potential in the left frontal lobe is lower in the flow state than in the resting and boredom states. The occipital alpha is lower in the flow state than in the resting state. Similarly, the EEG theta in the left parietal lobe is lower during the flow state than the resting state. However, the EEG theta in the frontal-temporal region of the brain is higher in the flow state than in the anxiety state. The flow state is associated with low cognitive load, presence of attention levels, and loss of self-consciousness when compared to resting and boredom states --Abstract, page iii

Brain-computer interface of focus and motor imagery using wavelet and recurrent neural networks

Brain-computer interface is a technology that allows operating a device without involving muscles and sound, but directly from the brain through the processed electrical signals. The technology works by capturing electrical or magnetic signals from the brain, which are then processed to obtain information contained therein. Usually, BCI uses information from electroencephalogram (EEG) signals based on various variables reviewed. This study proposed BCI to move external devices such as a drone simulator based on EEG signal information. From the EEG signal was extracted to get motor imagery (MI) and focus variable using wavelet. Then, they were classified by recurrent neural networks (RNN). In overcoming the problem of vanishing memory from RNN, was used long short-term memory (LSTM). The results showed that BCI used wavelet, and RNN can drive external devices of non-training data with an accuracy of 79.6%. The experiment gave AdaDelta model is better than the Adam model in terms of accuracy and value losses. Whereas in computational learning time, Adam's model is faster than AdaDelta's model

Using brain-computer interaction and multimodal virtual-reality for augmenting stroke neurorehabilitation

Every year millions of people suffer from stroke resulting to initial paralysis, slow motor recovery and chronic conditions that require continuous reha bilitation and therapy. The increasing socio-economical and psychological impact of stroke makes it necessary to find new approaches to minimize its sequels, as well as novel tools for effective, low cost and personalized reha bilitation. The integration of current ICT approaches and Virtual Reality (VR) training (based on exercise therapies) has shown significant improve ments. Moreover, recent studies have shown that through mental practice and neurofeedback the task performance is improved. To date, detailed in formation on which neurofeedback strategies lead to successful functional recovery is not available while very little is known about how to optimally utilize neurofeedback paradigms in stroke rehabilitation. Based on the cur rent limitations, the target of this project is to investigate and develop a novel upper-limb rehabilitation system with the use of novel ICT technolo gies including Brain-Computer Interfaces (BCI’s), and VR systems. Here, through a set of studies, we illustrate the design of the RehabNet frame work and its focus on integrative motor and cognitive therapy based on VR scenarios. Moreover, we broadened the inclusion criteria for low mobility pa tients, through the development of neurofeedback tools with the utilization of Brain-Computer Interfaces while investigating the effects of a brain-to-VR interaction.Todos os anos, milho˜es de pessoas sofrem de AVC, resultando em paral isia inicial, recupera¸ca˜o motora lenta e condic¸˜oes cr´onicas que requerem re abilita¸ca˜o e terapia cont´ınuas. O impacto socioecon´omico e psicol´ogico do AVC torna premente encontrar novas abordagens para minimizar as seque las decorrentes, bem como desenvolver ferramentas de reabilita¸ca˜o, efetivas, de baixo custo e personalizadas. A integra¸c˜ao das atuais abordagens das Tecnologias da Informa¸ca˜o e da Comunica¸ca˜o (TIC) e treino com Realidade Virtual (RV), com base em terapias por exerc´ıcios, tem mostrado melhorias significativas. Estudos recentes mostram, ainda, que a performance nas tare fas ´e melhorada atrav´es da pra´tica mental e do neurofeedback. At´e a` data, na˜o existem informac¸˜oes detalhadas sobre quais as estrat´egias de neurofeed back que levam a uma recupera¸ca˜o funcional bem-sucedida. De igual modo, pouco se sabe acerca de como utilizar, de forma otimizada, o paradigma de neurofeedback na recupera¸c˜ao de AVC. Face a tal, o objetivo deste projeto ´e investigar e desenvolver um novo sistema de reabilita¸ca˜o de membros supe riores, recorrendo ao uso de novas TIC, incluindo sistemas como a Interface C´erebro-Computador (ICC) e RV. Atrav´es de um conjunto de estudos, ilus tramos o design do framework RehabNet e o seu foco numa terapia motora e cognitiva, integrativa, baseada em cen´arios de RV. Adicionalmente, ampli amos os crit´erios de inclus˜ao para pacientes com baixa mobilidade, atrav´es do desenvolvimento de ferramentas de neurofeedback com a utilizac¸˜ao de ICC, ao mesmo que investigando os efeitos de uma interac¸˜ao c´erebro-para-RV

Wireless Sensors for Brain Activity—A Survey

Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex structure and long periods of application time, unwittingly causing discomfort and distress on the users. Given their size and price, aside from their lower sensitivity and narrower spectrum band(s), wearable EEG devices may be used regularly by individuals for continuous collection of user data from non-medical environments. This allows their usage for diverse, nontraditional, non-medical applications, including cognition, BCI, education, and gaming. Given the reduced need for standardization or accuracy, the area remains a rather incipient one, mostly driven by the emergence of new devices that represent the critical link of the innovation chain. In this context, the aim of this study is to provide a holistic assessment of the consumer-grade EEG devices for cognition, BCI, education, and gaming, based on the existing products, the success of their underlying technologies, as benchmarked by the undertaken studies, and their integration with current applications across the four areas. Beyond establishing a reference point, this review also provides the critical and necessary systematic guidance for non-medical EEG research and development efforts at the start of their investigation.</jats:p

Wireless Sensors for Brain Activity — A Survey

Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex structure and long periods of application time, unwittingly causing discomfort and distress on the users. Given their size and price, aside from their lower sensitivity and narrower spectrum band(s), wearable EEG devices may be used regularly by individuals for continuous collection of user data from non-medical environments. This allows their usage for diverse, nontraditional, non-medical applications, including cognition, BCI, education, and gaming. Given the reduced need for standardization or accuracy, the area remains a rather incipient one, mostly driven by the emergence of new devices that represent the critical link of the innovation chain. In this context, the aim of this study is to provide a holistic assessment of the consumer-grade EEG devices for cognition, BCI, education, and gaming, based on the existing products, the success of their underlying technologies, as benchmarked by the undertaken studies, and their integration with current applications across the four areas. Beyond establishing a reference point, this review also provides the critical and necessary systematic guidance for non-medical EEG research and development efforts at the start of their investigation

Measuring Brain Activation Patterns from Raw Single-Channel EEG during Exergaming: A Pilot Study

Physical and cognitive rehabilitation is deemed crucial to attenuate symptoms and to improve the quality of life in people with neurodegenerative disorders, such as Parkinson's Disease. Among rehabilitation strategies, a novel and popular approach relies on exergaming: the patient performs a motor or cognitive task within an interactive videogame in a virtual environment. These strategies may widely benefit from being tailored to the patient's needs and engagement patterns. In this pilot study, we investigated the ability of a low-cost BCI based on single-channel EEG to measure the user's engagement during an exergame. As a first step, healthy subjects were recruited to assess the system's capability to distinguish between (1) rest and gaming conditions and (2) gaming at different complexity levels, through Machine Learning supervised models. Both EEG and eye-blink features were employed. The results indicate the ability of the exergame to stimulate engagement and the capability of the supervised classification models to distinguish resting stage from game-play(accuracy > 95%). Finally, different clusters of subject responses throughout the game were identified, which could help define models of engagement trends. This result is a starting point in developing an effectively subject-tailored exergaming system