Positive Technology Journal

A strategy for computer-assisted mental practice in stroke rehabilitation.

Neurorehabil Neural Repair. 2006 Dec;20(4):503-7

Authors: Gaggioli A, Meneghini A, Morganti F, Alcaniz M, Riva G

OBJECTIVE: To investigate the technical and clinical viability of using computer-facilitated mental practice in the rehabilitation of upper-limb hemiparesis following stroke. DESIGN: A single-case study. SETTING: Academic-affiliated rehabilitation center.Participant. A 46-year-old man with stable motor deficit of the upper right limb following subcortical ischemic stroke.Intervention. Three computer-enhanced mental practice sessions per week at the rehabilitation center, in addition to usual physical therapy. A custom-made virtual reality system equipped with arm-tracking sensors was used to guide mental practice. The system was designed to superimpose over the (unseen) paretic arm a virtual reconstruction of the movement registered from the nonparetic arm. The laboratory intervention was followed by a 1-month home-rehabilitation program, making use of a portable display device. MAIN OUTCOME MEASURES: Pretreatment and posttreatment clinical assessment measures were the upper-extremity scale of the Fugl-Meyer Assessment of Sensorimotor Impairment and the Action Research Arm Test. Performance of the affected arm was evaluated using the healthy arm as the control condition. RESULTS: The patient's paretic limb improved after the first phase of intervention, with modest increases after home rehabilitation, as indicated by functional assessment scores and sensors data. CONCLUSION: Results suggest that technology-supported mental training is a feasible and potentially effective approach for improving motor skills after stroke.

Re-blogged from IFTF's future now 

A very interesting way to see the changes in rhetoric over time (you can move the slider around to see trends in word usage)

The above tag cloud shows the popularity, frequency, and trends in the usages of words within speeches, official documents, declarations, and letters written by the Presidents of the US between 1776 - 2006 AD.

The dataset consists of over 360 documents downloaded from Encyclopedia Britannica and ThisNation.com.

From Emerging Technology Trends 

Californian researchers are using light to control biological nanomolecules and proteins. They think it's possible to put some of their nano-photoswitches in the cells of the retina, restoring light sensitivity in people with degenerative blindness such as macular degeneration...

Read the full article

From New Scientist tech

The social interactions that glue the World Wide Web together are now so complex it has outgrown the relatively narrow field of computer science..

read the full story here

An extended EM algorithm for joint feature extraction and classification in brain-computer interfaces.

Neural Comput. 2006 Nov;18(11):2730-61

Authors: Li Y, Guan C

For many electroencephalogram (EEG)-based brain-computer interfaces (BCIs), a tedious and time-consuming training process is needed to set parameters. In BCI Competition 2005, reducing the training process was explicitly proposed as a task. Furthermore, an effective BCI system needs to be adaptive to dynamic variations of brain signals; that is, its parameters need to be adjusted online. In this article, we introduce an extended expectation maximization (EM) algorithm, where the extraction and classification of common spatial pattern (CSP) features are performed jointly and iteratively. In each iteration, the training data set is updated using all or part of the test data and the labels predicted in the previous iteration. Based on the updated training data set, the CSP features are reextracted and classified using a standard EM algorithm. Since the training data set is updated frequently, the initial training data set can be small (semi-supervised case) or null (unsupervised case). During the above iterations, the parameters of the Bayes classifier and the CSP transformation matrix are also updated concurrently. In online situations, we can still run the training process to adjust the system parameters using unlabeled data while a subject is using the BCI system. The effectiveness of the algorithm depends on the robustness of CSP feature to noise and iteration convergence, which are discussed in this article. Our proposed approach has been applied to data set IVa of BCI Competition 2005. The data analysis results show that we can obtain satisfying prediction accuracy using our algorithm in the semisupervised and unsupervised cases. The convergence of the algorithm and robustness of CSP feature are also demonstrated in our data analysis.

Neural internet: web surfing with brain potentials for the completely paralyzed.

Neurorehabil Neural Repair. 2006 Dec;20(4):508-15

Authors: Karim AA, Hinterberger T, Richter J, Mellinger J, Neumann N, Flor H, Kübler A, Birbaumer N

Neural Internet is a new technological advancement in brain-computer interface research, which enables locked-in patients to operate a Web browser directly with their brain potentials. Neural Internet was successfully tested with a locked-in patient diagnosed with amyotrophic lateral sclerosis rendering him the first paralyzed person to surf the Internet solely by regulating his electrical brain activity. The functioning of Neural Internet and its clinical implications for motor-impaired patients are highlighted.

Via Usability News 

Deadine: 7 November 2006
CENTRE FOR HCI DESIGN, CITY UNIVERSITY LONDON

Researcher in Human-Computer Interaction
Fixed term for three years

23.5K - 27K pounds pa inc

Closing date for applications: 7th November 2006.

We are seeking a researcher with a background in computing and human-computer interaction to join a 3 year EPSRC-funded research project that will investigate handover in healthcare settings. This is an
exciting opportunity to get involved in a major study of clinical handover and to contribute to the design of innovative technological solutions to enhance the efficacy of handover. The researcher will be primarily
responsible for implementing novel interactive systems to support handover using technologies such as PDAs, telemedicine and interactive whiteboards.


Clinical handover is the handing over of responsibility and care for patients from one individual or team to another. It has been shown to make a vital contribution to the safety and effectiveness of clinical work, yet current practice is highly variable. Handovers are often
impromptu, informal and supported by ad hoc artefacts such as paper-based notes. While there have been small-scale studies of clinical handover in specific settings, there is a lack of basic research. We will address this by conducting extensive field studies of handover in a range of healthcare settings, by developing a generic model of handover and by designing and prototyping novel software and hardware solutions to support handover.

Candidates should have a good BSc in a computing discipline with a significant HCI component. An MSc in a relevant discipline and research experience in HCI are desirable. Experience in designing and implementing interactive systems on a range of software and hardware platforms is essential. Candidates should also have a keen interest in healthcare issues and an awareness of current IT developments for healthcare.


This post is based in the Centre for HCI Design (HCID) at City University London, an independent research centre in the School of Informatics. HCID has an international reputation in human-computer interaction and software engineering research, including studies of work and human-system interaction, usability evaluation, accessibility, requirements engineering, and system modelling.

In return, we offer a comprehensive package of in-house staff training and development, and benefits that include a final salary pension scheme.

Actively working to promote equal opportunity and diversity.

For more information and an application pack, visit www.city.ac.uk/jobs or write to Recruitment Team, HR Department, City University, Northampton Square, London EC1V 0HB, quoting job reference number BD/10514.

Graded motor imagery for pathologic pain. A randomized controlled trial.

Neurology. 2006 Nov 2;

Authors: Moseley GL

Phantom limb and complex regional pain syndrome type 1 (CRPS1) are characterized by changes in cortical processing and organization, perceptual disturbances, and poor response to conventional treatments. Graded motor imagery is effective for a small subset of patients with CRPS1. OBJECTIVE: To investigate whether graded motor imagery would reduce pain and disability for a more general CRPS1 population and for people with phantom limb pain. METHODS: Fifty-one patients with phantom limb pain or CRPS1 were randomly allocated to motor imagery, consisting of 2 weeks each of limb laterality recognition, imagined movements, and mirror movements, or to physical therapy and ongoing medical care. RESULTS: There was a main statistical effect of treatment group, but not diagnostic group, on pain and function. The mean (95% CI) decrease in pain between pre- and post-treatment (100 mm visual analogue scale) was 23.4 mm (16.2 to 30.4 mm) for the motor imagery group and 10.5 mm (1.9 to 19.2 mm) for the control group. Improvement in function was similar and gains were maintained at 6-month follow-up. CONCLUSION: Motor imagery reduced pain and disability in these patients with complex regional pain syndrome type I or phantom limb pain, but the mechanism, or mechanisms, of the effect are not clear.

Tongue Piercing by a Yogi: QEEG Observations.

Appl Psychophysiol Biofeedback. 2006 Nov 3;

Authors: Peper E, Wilson VE, Gunkelman J, Kawakami M, Sata M, Barton W, Johnston J

This study reports on the QEEG observations recorded from a yogi during tongue piercing in which he demonstrated voluntary pain control. The QEEG was recorded with a Lexicor 1620 from 19 sites with appropriate controls for impedence and artifacts. A neurologist read the data for abnormalities and the QEEG was analyzed by mapping, single and multiple hertz bins, coherence, and statistical comparisons with a normative database. The session included a meditation baseline and tongue piercing. During the meditative baseline period the yogi's QEEG maps suggesting that he was able to lower his brain activity to a resting state. This state showed a predominance of slow wave potentials (delta) during piercing and suggested that the yogi induced a state that may be similar to those found when individuals are under analgesia. Further research should be conducted with a group of individuals who demonstrate exceptional self-regulation to determine the underlying mechanisms, and whether the skills can be used to teach others how to manage pain.