Test Data Sets for Evaluating Data Visualization Techniques

In this paper we take a step toward addressing a pressing general problem in the development of data visualization systems — how to measure their effectiveness. The step we take is to define a model for specifying the generation of test data that can be em-ployed for standardized and quantitative testing of a system’s per-formance. These test data sets, in conjunction with appropriate testing procedures, can provide a basis for certifying the effective-ness of a visualization system and for conducting comparative studies to steer system development

A common spatial pattern approach for classification of mental counting and motor execution EEG

© Springer Nature Switzerland AG 2018. A Brain Computer Interface (BCI) as a medium of communication is convenient for people with severe motor disabilities. Although there are a number of different BCIs, the focus of this paper is on Electroencephalography (EEG) as a means of human computer interaction. Motor imagery and mental arithmetic are the most popular techniques used to modulate brain waves that can be used to control devices. We show that it is possible to define different mental states using real fist rotation and imagined reverse counting. While people have already investigated left fist rotation and right fist rotation for dual state BCI, we intend to define a new state using mental reverse counting. We use Common Spatial Pattern (CSP) approach for feature extraction to distinguish between these states. CSP has been prominently used in the context of motor imagery task, we define its applicability for the distinction between motor execution and mental counting. CSP features are evaluated using classifiers like GMM, SVM, and GMM-UBM. GMM-UBM using data filtered through the beta band (13–30 Hz) gives the best performance

Electroencephalogram based brain-computer interface: An introduction

Electroencephalogram (EEG) signals are useful for diagnosing various mental conditions such as epilepsy, memory impairments and sleep disorders. Brain-Computer Interface (BCI) is a revolutionary new area using EEG that is most use-ful for the severely disabled individuals for hands-off device control and commu-nication as they create a direct interface from the brain to the external environ-ment, therefore circumventing the use of peripheral muscles and limbs. However, being non-invasive, BCI designs are not necessarily limited to this user group and other applications for gaming, music, biometrics etc have been developed more recently. This chapter will give an introduction to EEG based BCI and existing methodologies; specifically those based on transient and steady state evoked po-tentials, mental tasks and motor imagery will be described. Two real-life scenarios of EEG based BCI applications in biometrics and device control will also be brief-ly explored. Finally, current challenges and future trends of this technology will be summarized