Positive Technology Journal

The Significance of Sigma Neurofeedback Training on Sleep Spindles and Aspects of Declarative Memory.

Appl Psychophysiol Biofeedback. 2006 Jul 15;

Authors: Berner I, Schabus M, Wienerroither T, Klimesch W

The functional significance of sleep spindles for overnight memory consolidation and general learning aptitude as well as the effect of four 10-minute sessions of spindle frequency (11.6-16 Hz, sigma) neurofeedback-training on subsequent sleep spindle activity and overnight performance change was investigated. Before sleep, subjects were trained on a paired-associate word list task after having received either neurofeedback training (NFT) or pseudofeedback training (PFT).Although NFT had no significant impact on subsequent spindle activity and behavioral outcomes, there was a trend for enhanced sigma band-power during NREM (stage 2 to 4) sleep after NFT as compared to PFT. Furthermore, a significant positive correlation between spindle activity during slow wave sleep (in the first night half) and overall memory performance was revealed. The results support the view that the considerable inter-individual variance in sleep spindle activity can at least be partly explained by differences in the ability to acquire new declarative information.We conclude that the short NFT before sleep was not sufficient to efficiently enhance phasic spindle activity and/or to influence memory processing. NFT was, however, successful in increasing sigma power, presumably because sigma NFT effects become more easily evident in actually trained frequency bands than in associated phasic spindle activity.

From Usability News 

The topic of emotion in Human-Computer Interaction is of increasing interest to the HCI community. Since Rosalind Picard's fundamental publications on affective computing, research in this field has gained significant momentum.

Emotion research is largely grounded in psychology yet spans across numerous other disciplines. The challenge of such an interdisciplinary research area is developing a common vocabulary and research framework that a mature discipline requires. What is increasingly needed for advanced and serious work in this field is to place it on a rigorous footing, including developing theoretical fundamentals of HCI-related emotion research, understanding emotions' function in HCI, ethical and legal issues, and the practical implications and consequences for the HCI community.

The first workshop on emotion in HCI held in Edinburgh last year brought an interdisciplinary group of practitioners and researchers together for a lively exchange of ideas, discussion of common problems, and identification of domains to explore.

This year's workshop will build on the success of last year. Focus will be on discussion and joint work on selected topics. Participants will engage in developing further the themes from the first workshop in as wide an application spectrum as possible, such as internet applications, ambient intelligence, office work, control rooms, mobile computing, virtual reality, presence, and home applications.

You are cordially invited to become part of this interdisciplinary forum. This will be a very practical workshop with the participants working together to find new insights, views, ideas and solutions. We therefore invite contributions which will enrich the discussions by their innovative content, fundamental nature, or new perspective. We also encourage demos of products or prototypes related to the topic.
Topics addressed by the workshop are:
- How do applications currently make use of emotions and how could it be improved?
- What makes applications that support affective interactions successful?
- How do we know if affective interactions are successful, and how can we measure this success?
- What value might affective applications, affective systems, and affective interaction have?
- What requirements on sensing technologies are there in HCI?
- What technology is currently available for sensing affective states?
- How reliable is sensing technology?
- Are there reliable and replicable processes to include emotion in HCI design projects?
- What opportunities and risks are there in designing affective applications?
- What are the relationships between emotion, affect, personality, and engagement, and what do they mean for interactive systems design?

To become part of this discussion please submit an extended abstract of your ideas or demo description. Case studies describing current applications or prototypes are strongly encouraged, as well as presentations of products or prototypes that you have developed.

The abstract should be limited to about 800 words. Accepted contributions will be published on the workshop's homepage with the possibility to extend them to short papers of 4 pages. It is also planned to produce a special issue of a journal on the results of the workshop.

Please note that registration to the HCI conference is required in order to take part in the workshop (at least for the day of the workshop). Early bird registration deadline is 21st July.

Dates:

27 June - position paper deadline
11 July - notification of acceptance
21 July - early registration deadline
12 September - workshop

A high-performance brain-computer interface.

Nature. 2006 Jul 13;442(7099):195-8

Authors: Santhanam G, Ryu SI, Yu BM, Afshar A, Shenoy KV

Recent studies have demonstrated that monkeys and humans can use signals from the brain to guide computer cursors. Brain-computer interfaces (BCIs) may one day assist patients suffering from neurological injury or disease, but relatively low system performance remains a major obstacle. In fact, the speed and accuracy with which keys can be selected using BCIs is still far lower than for systems relying on eye movements. This is true whether BCIs use recordings from populations of individual neurons using invasive electrode techniques or electroencephalogram recordings using less- or non-invasive techniques. Here we present the design and demonstration, using electrode arrays implanted in monkey dorsal premotor cortex, of a manyfold higher performance BCI than previously reported. These results indicate that a fast and accurate key selection system, capable of operating with a range of keyboard sizes, is possible (up to 6.5 bits per second, or approximately 15 words per minute, with 96 electrodes). The highest information throughput is achieved with unprecedentedly brief neural recordings, even as recording quality degrades over time. These performance results and their implications for system design should substantially increase the clinical viability of BCIs in humans.

Re-blogged from Omnibrain 

A joint venture of the Australian National University and the University of Sydney investigated whether repetitive transcranial magnetic stimulation, TMS, can improve a healthy person's ability to guess accurately the number of elements in a scene, the London Telegraph reported.

The website Pharmabiz provides a good definition of Neuroinformatics:

BrainMap is an online database of published functional neuroimaging experiments with coordinate-based (Talairach) activation locations. The goal of BrainMap is to provide a vehicle to share methods and results of brain functional imaging studies. It is a tool to rapidly retrieve and understand studies in specific research domains, such as language, memory, attention, reasoning, emotion, and perception, and to perform meta-analyses of like studies. 

BrainMap was created and developed by Peter T. Fox and Jack L. Lancaster of the Research Imaging Center of the University of Texas Health Science Center San Antonio.

Re-blogged from KurzweilAI.net

Via KurzweilAI.net 

Researchers at Columbia University are combining the processing power of the human brain with computer vision to develop a novel device that will allow people to search through images ten times faster than they can on their own. 

The "cortically coupled computer vision system," known as C3 Vision, is the brainchild of professor Paul Sajda, director of the Laboratory for Intelligent Imaging and Neural Computing at Columbia University. He received a one-year, $758,000 grant from Darpa for the project in late 2005.

The brain emits a signal as soon as it sees something interesting, and that "aha" signal can be detected by an electroencephalogram, or EEG cap. While users sift through streaming images or video footage, the technology tags the images that elicit a signal, and ranks them in order of the strength of the neural signatures. Afterwards, the user can examine only the information that their brains identified as important, instead of wading through thousands of images.

Read the full story on Wired