The Smartphone Brain Scanner: A Portable Real-Time Neuroimaging System

Combining low cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. We present a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system - Smartphone Brain Scanner - combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully mobile system for real-time 3D EEG imaging. We discuss the benefits and challenges of a fully portable system, including technical limitations as well as real-time reconstruction of 3D images of brain activity. We present examples of the brain activity captured in a simple experiment involving imagined finger tapping, showing that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using a off-the-shelf consumer neuroheadset is lower compared to that obtained using high density standard EEG equipment, we propose that mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings

Implementation of Robotic arm control with Emotiv Epoc

Brain Computer Interface (BCI) has opened up a new hope for people suffering from severe motor disabilities, having no physical activities caused due to disease or injury to the central or peripheral nervous system. A BCI based robotic arm movement control is designed and implemented. The proposed system acquires data from the scalp of subjects a group of sensors. Emotiv EPOC a commercially available EEG headset is used, which analyzes the acquired EEG signals real time. The signals are processed and accordingly commands are issued for different movements which will be based on the characteristic patterns for various facial expressions, human emotions and cognitive actions. The idea is to combine the user intent with a robotic arm to achieve the user initiated motor movements

Anxiety reducing through a neurofeedback serious game with dynamic difficulty adjustment

Presently, society has to deal with a large number of mental issues. Anxiety disorder is a serious concern, affecting millions of people’s lives and, although methods to tackle the problem currently exist, these main treatments are being linked to some issues and improvements must be found. One of the alternatives is Neurofeedback, a biofeedback treatment, completely non-invasive and showing impressive results so far. It uses a neuroheadset equipment to read the neural activity of the brain, giving the user visual feedback about it. The purpose this, is to train the users’ brain in specific regions and frequencies, allowing the subjects to learn how to voluntarily control its neural activity, even outside of the session. Current applications using this method might be too simple, which can become tedious and disengaging. Serious games can help with these issues, since it can bring enjoyment and engagement while doing this type of treatment. The interest in games’ capabilities in education has been increasing over the past years, since it has been proved that games are an excellent tool for education and skill learning. Joining these concepts of game and neurofeedback, this project aims to create a serious game prototype, applying the current treatment knowledge. The development process of a new game with neuroheadset integration, capable of reading the neural activity of the user while playing and giving the appropriate feedback, will be described in the present document. Since studies proved that a good balance between challenge and skill increases the learning performance, a dynamic difficulty adjustment system is implemented within the game, allowing the game to adapt itself to each user’s skill individually, and keeping the user in a challenging, motivating zone. At the end of the document, the results of pilot test on a few subjects are shown.Na sociedade actual o número de problemas relacionados com perturbações mentais tem sido cada vez mais relevante, sendo esse o caso da ansiedade. O distúrbio de ansiedade é um problema que atinge milhões de pessoas e, embora existam métodos para combater este problema, estudos comprovam que estes têm algumas lacunas que podem trazer outros problemas associados, sendo portanto necessário procurar melhorias aos métodos actuais. Uma das alternativas tem apresentado excelentes resultados e denomina-se Neurofeedback. Este é um tratamento de biofeedback, nãoinvasivo e que utiliza um equipamento neuroheadset para capturar a actividade neuronal, apresentando indicações visuais sobre o comportamento do utilizador. Isto é feito com o objectivo de treinar o cérebro do utilizador, em regiões e frequências específicas, para que este seja capaz de controlar voluntariamente a sua actividade neuronal. As aplicações actualmente utilizadas com este intuito podem se tornar aborrecidas e monótonas devido à sua simplicidade. Um jogo sério pode ajudar com estes problemas, uma vez que é capaz de trazer divertimento e motivação para este tipo de tratamento. O crescente interesse nas capacidades educativas dos jogos sérios, tem identificado estes como excelentes ferramentas para a educação. Este projecto pretende portanto criar um protótipo de um jogo sério, aplicando os conceitos de neurofeedback. Neste documento, é apresentado o processo de desenvolvimento de um novo jogo com integração de um neuroheadset, capaz de identificar a actividade neuronal do jogador dando respostas adequadas. Uma vez que estudos comprovam que um bom balanço entre desafio apresentado e técnica do utilizador aumenta a capacidade de aprendizagem, foi implementado também um sistema de ajuste de dificuldade dinâmica, permitindo uma adaptação do jogo a cada indivíduo e mantendo este numa zona motivante de equilíbrio entre desafio e proficiência. No final serão apresentados os resultados de um teste piloto efectuado em alguns indivíduos

Exploiting the Data Sensitivity of Neurometric Fidelity for Optimizing EEG Sensing

With newly developed wireless neuroheadsets, electroencephalography (EEG) neurometrics can be incorporated into in situ and ubiquitous physiological monitoring for human mental health. As a resource constraint system providing critical health services, the EEG headset design must consider both high application fidelity and energy efficiency. However, through empirical studies with an off-the-shelf Emotiv EPOC Neuroheadset, we uncover a mismatch between lossy EEG sensor communication and high neurometric application fidelity requirements. To tackle this problem, we study how to learn the sensitivity of neurometric application fidelity to EEG data. The learned sensitivity is used to develop two algorithms: 1) an energy minimization algorithm minimizing the energy usage in EEG sampling and networking while meeting applications\u27 fidelity requirements and 2) a fidelity maximization algorithm maximizing the sum of all applications\u27 fidelities through the incorporation and optimal utilization of a limited data buffer. The effectiveness of our proposed solutions is validated through trace-driven experiments

Steering a Tractor by Means of an EMG-Based Human-Machine Interface

An electromiographic (EMG)-based human-machine interface (HMI) is a communication pathway between a human and a machine that operates by means of the acquisition and processing of EMG signals. This article explores the use of EMG-based HMIs in the steering of farm tractors. An EPOC, a low-cost human-computer interface (HCI) from the Emotiv Company, was employed. This device, by means of 14 saline sensors, measures and processes EMG and electroencephalographic (EEG) signals from the scalp of the driver. In our tests, the HMI took into account only the detection of four trained muscular events on the driver’s scalp: eyes looking to the right and jaw opened, eyes looking to the right and jaw closed, eyes looking to the left and jaw opened, and eyes looking to the left and jaw closed. The EMG-based HMI guidance was compared with manual guidance and with autonomous GPS guidance. A driver tested these three guidance systems along three different trajectories: a straight line, a step, and a circumference. The accuracy of the EMG-based HMI guidance was lower than the accuracy obtained by manual guidance, which was lower in turn than the accuracy obtained by the autonomous GPS guidance; the computed standard deviations of error to the desired trajectory in the straight line were 16 cm, 9 cm, and 4 cm, respectively. Since the standard deviation between the manual guidance and the EMG-based HMI guidance differed only 7 cm, and this difference is not relevant in agricultural steering, it can be concluded that it is possible to steer a tractor by an EMG-based HMI with almost the same accuracy as with manual steering

Investigating the Effects of Custom Made Orthotics on Brain Forms: A Pilot Study

OBJECTIVES: To determine (1) the feasibility of this novel approach and technique of recording brain activity, wirelessly and continuously, during human gait, and (2) if custom made orthotics will alter the brain activity patterns recorded. METHODS: Gait trials were performed on 16 participants walking with and without orthotic devices in their shoes while simultaneously collecting EEG data through the Emotiv wireless neuroheadset. RESULTS: The Emotiv neuroheadset was capable of detecting changes in brain activity between the two gait trials. The differences in brain activity identified between conditions were not statistically significant. CONCLUSION: The findings suggest the Emotiv EEG device is sensitive enough to detect changes in brain activation patterns during human gait. Further research is required before definite conclusions can be made about this novel device, or about what effects, if any, orthotics have on brain activation patterns during gait

A video game design based on Emotiv Neuroheadset

This paper presents our work on the development of a video maze game in Android system, and a new method to play the game using gyroscope and electromyography (EMG) signals obtained by a wireless Emotiv Neuroheadset. The TeamViewer software is used to share the computer screen and to transfer the data to an Android device, and the Emotiv EPOC headset is used to detect the intension of the user who is playing the game. The cursor position is controlled using information from the gyroscope embeded in the headset. The clicks are generated through the users blinking action based on the expressive suite data acquired from Emotiv headset signal data. A program called Neuro Mousecontrol is used to act as a tool for controlling gyroscope movements and clicking actions together. Extensive tests have demonstrated the effectiveness of the developed system

A new method to detect event-related potentials based on Pearson\u2019s correlation

Event-related potentials (ERPs) are widely used in brain-computer interface applications and in neuroscience. Normal EEG activity is rich in background noise, and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise. The noise produced by EEG activity itself is not correlated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely proportional to the square root of N, where N is the number of averaged epochs. This is the easiest strategy currently used to detect ERPs, which is based on calculating the average of all ERP\u2019s waveform, these waveforms being time- and phase-locked. In this paper, a new method called GW6 is proposed, which calculates the ERP using a mathematical method based only on Pearson\u2019s correlation. The result is a graph with the same time resolution as the classical ERP and which shows only positive peaks representing the increase\u2014in consonance with the stimuli\u2014in EEG signal correlation over all channels. This new method is also useful for selectively identifying and highlighting some hidden components of the ERP response that are not phase-locked, and that are usually hidden in the standard and simple method based on the averaging of all the epochs. These hidden components seem to be caused by variations (between each successive stimulus) of the ERP\u2019s inherent phase latency period (jitter), although the same stimulus across all EEG channels produces a reasonably constant phase. For this reason, this new method could be very helpful to investigate these hidden components of the ERP response and to develop applications for scientific and medical purposes. Moreover, this new method is more resistant to EEG artifacts than the standard calculations of the average and could be very useful in research and neurology. The method we are proposing can be directly used in the form of a process written in the well-known Matlab programming language and can be easily and quickly written in any other software language