Improving the performance of GIS/spatial analysts though novel applications of the Emotiv EPOC EEG headset

Geospatial information systems are used to analyze spatial data to provide decision makers with relevant, up-to-date, information. The processing time required for this information is a critical component to response time. Despite advances in algorithms and processing power, we still have many “human-in-the-loop” factors. Given the limited number of geospatial professionals, analysts using their time effectively is very important. The automation and faster humancomputer interactions of common tasks that will not disrupt their workflow or attention is something that is very desirable. The following research describes a novel approach to increase productivity with a wireless, wearable, electroencephalograph (EEG) headset within the geospatial workflow

Brain computer interface based neurorehabilitation technique using a commercially available EEG headset

Neurorehabilitation has recently been augmented with the use of virtual reality and rehabilitation robotics. In many systems, some known volitional control must exist in order to synchronize the user intended movement with the therapeutic virtual or robotic movement. Brain Computer Interface (BCI) aims to open up a new rehabilitation option for clinical population having no residual movement due to disease or injury to the central or peripheral nervous system. Brain activity contains a wide variety of electrical signals which can be acquired using many invasive and non-invasive acquisition techniques and holds the potential to be used as an input to BCI. Electroencephalogram (EEG) is a non-invasive method of acquiring brain activity which then, with further processing and classification, can be used to predict various brain states such as an intended motor movement. EEG provides the temporal resolution required to obtain significant result which may not be provided by many other non-invasive techniques. Here, EEG is recorded using a commercially available EEG headset provided by Emotiv Inc. Data is collected and processed using BCI2000 software, and the difference in the Mu-rhythm due to Event Related Synchronization (ERS) and Desynchronization (ERD) is used to distinguish an intended motor movement and resting brain state, without the need for physical movement. The idea is to combine this user intent/free will with an assistive robot to achieve the user initiated, repetitive motor movements required to bring therapeutic changes in the targeted subject group, as per Hebbian type learning

Functional changes in prefrontal cortex following frequency-specific training

Altres ajuts: European ANR (COEN4007-18, COEN-0003-01); PHRC grants from the French Ministry of Health and research funding; France Parkinson and ARSLA charity.Numerous studies indicate a significant role of pre-frontal circuits (PFC) connectivity involving attentional and reward neural networks within attention deficit hyperactivity disorder (ADHD) pathophysiology. To date, the neural mechanisms underlying the utility of non-invasive frequency-specific training systems in ADHD remediation remain underexplored. To address this issue, we created a portable electroencephalography (EEG)-based wireless system consisting of a novel headset, electrodes, and neuro program, named frequency specific cognitive training (FSCT). In a double-blind, randomized, controlled study we investigated the training effects in N = 46 school-age children ages 6-18 years with ADHD. 23 children in experimental group who underwent FCST training showed an increase in scholastic performance and meliorated their performance on neuropsychological tests associated with executive functions and memory. Their results were compared to 23 age-matched participants who underwent training with placebo (pFSCT). Electroencephalogram (EEG) data collected from participants trained with FSCT showed a significant increase in 14-18 Hz EEG frequencies in PFC brain regions, activities that indicated brain activation in frontal brain regions, the caudate nucleus, and putamen. These results demonstrate that FSCT targets specific prefrontal and striatal areas in children with ADHD, suggesting a beneficial modality for non-invasive modulation of brain areas implicated in attention and executive functions

INNOVATIVE HUMAN-COMPUTER INTERACTIONS TO SUPPORT COGNITIVE COLLABORATIVE GEOSPATIAL ENVIRONMENTS

In the current world geospatial information is being demanded in almost real time, which requires the speed at which this data is processed and made available to the user to be at an all-time high. In order to keep up with this ever increasing speed, analysts must find ways to increase their productivity. At the same time the demand for new analysts is high, and current methods of training are long and can be costly. Through the use of human computer interactions and basic networking systems, this paper explores new ways to increase efficiency in data processing and analyst training

Integration of Assistive Technologies into 3D Simulations: Exploratory Studies

Virtual worlds and environments have many purposes, ranging from games to scientific research. However, universal accessibility features in such virtual environments are limited. As the impairment prevalence rate increases yearly, so does the research interests in the field of assistive technologies. This work introduces research in assistive technologies and presents three software developments that explore the integration of assistive technologies within virtual environments, with a strong focus on Brain-Computer Interfaces. An accessible gaming system, a hands-free navigation software system, and a Brain-Computer Interaction plugin have been developed to study the capabilities of accessibility features within virtual 3D environments. Details of the specification, design, and implementation of these software applications are presented in the thesis. Observations and preliminary results as well as directions of future work are also included

NON-INVASIVE BRAIN EEG ROBOT NAVIGATION FRAMEWORK BASED ON EMOTIV EPOC

A Brain computer interface (BCI) has introduced new dimensions and created a new era for creative applications for developers and researchers giving alternative communication channels for people suffering of motor disabilities. The motor system is currently the primary focus, where EEG signals are being obtained while the subject is imagining or performing a motor response. With the help of an EEG signals, navigating a robot or controlling a wheel chair has come from science fiction movies or stories to reality. The purpose of this study is to design a navigation framework for a robot or a wheel chair using the capabilities of the brain EEG using an online acquisition device in our case it’s Emotiv Epoc. The framework is based on SSVEP focusing on visual cortex EEG signal analysis, the methodology used to complete this application is agile software development which basically is based on iterative and incremental development providing a rapid and flexible product. The tools used to implement the framework are divided into two categories hardware and software as for the hardware EEG acquisition device is used to acquire EEG signals, a robot based on NXT logo is used to demonstrate the control capabilities of the framework, as for the software Emotiv SDK , Open vibe as a signal processing and visual studio to design the a GUI as a man in the middle for interfacing the EEG signal processing platform with the robot

MeditAid:a wearable adaptive neurofeedback-based system for training mindfulness state

A recent interest in interaction design is towards the development of novel technologies emphasizing the value of mindfulness, monitoring, awareness, and self-regulation for both health and wellbeing. Whereas existing systems have focused mostly on relaxation and awareness of feelings, there has been little exploration on tools supporting the self-regulation of attention during mindfulness sitting meditation. This paper describes the design and initial evaluation of MeditAid, a wearable system integrating electroencephalography (EEG) technology with an adaptive aural entrainment for real time training of mindfulness state. The system identifies different meditative states and provides feedback to support users in deepening their meditation. We report on a study with 16 meditators about the perceived strengths and limitations of the MeditAid system. We demonstrate the benefits of binaural feedback in deepening meditative states, particularly for novice meditators

Simplification of EEG Signal Extraction, Processing, and Classification Using a Consumer-Grade Headset to Facilitate Student Engagement in BCI Research

Brain-computer interfaces (BCIs) are an emerging technology that leverage neurophysiological signals as input to computing systems. By circumventing the reliance on traditional input methods (e.g., mouse and keyboard), BCIs show a promising alternative interaction modality for people with disabilities. Advances in BCI research have further inspired a range of novel applications, such as the use of neurophysiological signals as passive input (e.g., to detect and reduce operator workload when managing multiple machines). BCIs have also emerged as a tool for student engagement due to the intrinsic interdisciplinarity of the technology, which spans the fields of computer science, electrical engineering, neuroscience, psychology and their broad applicability. However, these benefits also stand as a challenge to students interested in BCI research, as the need for familiarity with multiple related disciplines creates a high barrier to entry. Towards overcoming this barrier, we developed a simplified EEG-based BCI wherein we integrated a low-cost, consumer-grade headset for signal extraction with a novel graphical user interface that affords seamless exploration of several signal processing and machine learning techniques for analysis. Here, electrical activity is measured in real-time via an extracortical electrode placed on the user’s forehead, superior to the prefrontal cortex. The headset can then be connected to any Bluetooth-compatible device via a Bluetooth connection for (1) processing and classification of the signal contents and (2) operation of a machine (e.g., the Cozmo robot) via the intentional brain activity of the user. An additional visualization model also allows the user to explore the signal processing techniques, including the information decomposition and classification