Therapeutic potential of computer to cerebral cortex implantable devices

In this article, an overview of some of the latest developments in the field of cerebral cortex to computer interfacing (CCCI) is given. This is posed in the more general context of Brain-Computer Interfaces in order to assess advantages and disadvantages. The emphasis is clearly placed on practical studies that have been undertaken and reported on, as opposed to those speculated, simulated or proposed as future projects. Related areas are discussed briefly only in the context of their contribution to the studies being undertaken. The area of focus is notably the use of invasive implant technology, where a connection is made directly with the cerebral cortex and/or nervous system. Tests and experimentation which do not involve human subjects are invariably carried out a priori to indicate the eventual possibilities before human subjects are themselves involved. Some of the more pertinent animal studies from this area are discussed. The paper goes on to describe human experimentation, in which neural implants have linked the human nervous system bidirectionally with technology and the internet. A view is taken as to the prospects for the future for CCCI, in terms of its broad therapeutic role.

Towards natural human computer interaction in BCI

BCI systems require correct classification of signals interpreted from the brain for useful operation. To this end this paper investigates a method proposed in [1] to correctly classify a series of images presented to a group of subjects in [2]. We show that it is possible to use the proposed methods to correctly recognise the original stimuli presented to a subject from analysis of their EEG. Additionally we use a verification set to show that the trained classification method can be applied to a different set of data. We go on to investigate the issue of invariance in EEG signals. That is, the brain representation of similar stimuli is recognisable across different subjects. Finally we consider the usefulness of the methods investigated towards an improved BCI system and discuss how it could potentially lead to great improvements in the ease of use for the end user by offering an alternative, more intuitive control based mode of operation.

Use of auditory cues for wayfinding assistance in virtual environment: music aids route decision

This paper addresses the crucial problem of wayfinding assistance in the Virtual Environments (VEs). A number of navigation aids such as maps, agents, trails and acoustic landmarks are available to support the user for navigation in VEs, however it is evident that most of the aids are visually dominated. This work-in-progress describes a sound based approach that intends to assist the task of 'route decision' during navigation in a VE using music. Furthermore, with use of musical sounds it aims to reduce the cognitive load associated with other visually as well as physically dominated tasks. To achieve these goals, the approach exploits the benefits provided by music to ease and enhance the task of wayfinding, whilst making the user experience in the VE smooth and enjoyable.

Learning how older adults undertake computer tasks [poster]

This paper describes a study that was conducted to learn more about how older adults use the tools in a GUI to undertake tasks in Windows applications. The objective was to gain insight into what people did and what they found most difficult. File and folder manipulation, and some aspects of formatting presented difficulties, and these were thought to be related to a lack of understanding of the task model, the correct interpretation of the visual cues presented by the interface, and the recall and translation of the task model into a suitable sequence of actions.

Toward construction of an inexpensive brain computer interface for goal oriented applications

The paper describes the implementation of an offline, low-cost Brain Computer Interface (BCI) alternative to more expensive commercial models. Using inexpensive general purpose clinical EEG acquisition hardware (Truscan32, Deymed Diagnostic) as the base unit, a synchronisation module was constructed to allow the EEG hardware to be operated precisely in time to allow for recording of automatically time stamped EEG signals. The synchronising module allows the EEG recordings to be aligned in stimulus time locked fashion for further processing by the classifier to establish the class of the stimulus, sample by sample. This allows for the acquisition of signals from the subject’s brain for the goal oriented BCI application based on the oddball paradigm. An appropriate graphical user interface (GUI) was constructed and implemented as the method to elicit the required responses (in this case Event Related Potentials or ERPs) from the subject.

Single-trial event-related potential analysis for brain-computer interfaces

Abstract. Different types of mental activity are utilised as an input in Brain-Computer Interface (BCI) systems. One such activity type is based on Event-Related Potentials (ERPs). The characteristics of ERPs are not visible in single-trials, thus averaging over a number of trials is necessary before the signals become usable. An improvement in ERP-based BCI operation and system usability could be obtained if the use of single-trial ERP data was possible. The method of Independent Component Analysis (ICA) can be utilised to separate single-trial recordings of ERP data into components that correspond to ERP characteristics, background electroencephalogram (EEG) activity and other components with non- cerebral origin. Choice of specific components and their use to reconstruct “denoised” single-trial data could improve the signal quality, thus allowing the successful use of single-trial data without the need for averaging. This paper assesses single-trial ERP signals reconstructed using a selection of estimated components from the application of ICA on the raw ERP data. Signal improvement is measured using Contrast-To-Noise measures. It was found that such analysis improves the signal quality in all single-trials.

Collaborative grid infrastructure for molecular simulations: The eMinerals minigrid as a prototype integrated computer and data grid

This paper describes a prototype grid infrastructure, called the eMinerals minigrid, for molecular simulation scientists. which is based on an integration of shared compute and data resources. We describe the key components, namely the use of Condor pools, Linux/Unix clusters with PBS and IBM's LoadLeveller job handling tools, the use of Globus for security handling, the use of Condor-G tools for wrapping globus job submit commands, Condor's DAGman tool for handling workflow, the Storage Resource Broker for handling data, and the CCLRC dataportal and associated tools for both archiving data with metadata and making data available to other workers.