Positive Technology Journal

Virtual reality in acquired brain injury upper limb rehabilitation: evidence-based evaluation of clinical research.

Brain Inj. 2009 Mar;23(3):179-91

Authors: Mumford N, Wilson PH

PRIMARY OBJECTIVE: Acquired brain injury (ABI) is associated with significant cognitive, behavioural, psychological and physical impairment. Hence, it has been important to leverage assessment approaches in rehabilitation by using current and emerging technologies, including virtual reality (VR). A number of VR rehabilitation programmes have been designed in recent years, mainly to improve upper limb function. However, before this technology gains widespread use, evaluation of the scientific evidence supporting VR-assisted rehabilitation is needed. The present review aimed to assess the rationale, design and methodology of research investigating the clinical impact of VR on ABI upper-limb rehabilitation. RESEARCH DESIGN: A total of 22 studies were surveyed using a Cochrane-style review. RESEARCH METHODS: Studies were classified on a number of key criteria: theoretical bases and aims, sample populations and recruitment procedures, characteristics of the VR systems, evaluation design including control procedures and statistical analysis of results. Studies were rated using the Downs and Black (DB) scale. RESULTS: The review demonstrated that few studies used a conventional randomized controlled study design. Moderate support was shown for both teacher-animation and game-like systems. CONCLUSION: While VR-assisted rehabilitation shows early promise, clinicians are advised to be cautious about adopting these technologies before adequate data is available.

The development of personalised cognitive prosthetics.

Conf Proc IEEE Eng Med Biol Soc. 2008;1:787-90

Authors: Nugent CD, Davies RJ, Donnelly MP, Hallberg J, Hariz M, Craig D, Meiland F, Moelaert F, Bengtsson JE, Savenstedt S, Mulvenna M, Droes RM

Persons suffering from mild dementia can benefit from a form of cognitive prosthetic which can be used to assist them with their day to day activities. Within our current work we are aiming to develop a successful user-validated cognitive prosthetic for persons with mild dementia. We have devised a three phased waterfall methodology to support our developments. Based on the evaluation of the first of these phases which involved the processes of user requirements gathering, prototype development and evaluation of in situ deployment of the technology we have been able to guide the technical development within the second phase of our work. Within this paper we provide an overview of the first phase of our methodology and demonstrate how we have used the results from this to guide the second phase of our work, especially with regards to the notion of personalisation.

Improving the performance of brain-computer interface through meditation practicing.

Conf Proc IEEE Eng Med Biol Soc. 2008;1:662-5

Authors: Eskandari P, Erfanian A

Cognitive tasks using motor imagery have been used for generating and controlling EEG activity in most brain-computer interface (BCI). Nevertheless, during the performance of a particular mental task, different factors such as concentration, attention, level of consciousness and the difficulty of the task, may be affecting the changes in the EEG activity. Accordingly, training the subject to consistently and reliably produce and control the changes in the EEG signals is a critical issue in developing a BCI system. In this work, we used meditation practice to enhance the mind controllability during the performance of a mental task in a BCI system. The mental states to be discriminated are the imaginative hand movement and the idle state. The experiments were conducted on two groups of subject, meditation group and control group. The time-frequency analysis of EEG signals for meditation practitioners showed an event-related desynchronization (ERD) of beta rhythm before imagination during resting state. In addition, a strong event-related synchronization (ERS) of beta rhythm was induced in frequency around 25 Hz during hand motor imagery. The results demonstrated that the meditation practice can improve the classification accuracy of EEG patterns. The average classification accuracy was 88.73% in the meditation group, while it was 70.28% in the control group. An accuracy as high as 98.0% was achieved in the meditation group.