Posts tagged EEG
Articles and news from the latest research reports.
Simple Innovation to Electrodes Makes a Big Difference
The electroencephalogram (EEG) for human uses has been around since 1924. Small metal discs placed along the scalp measure electrical activity in the human brain, important in diagnosing or evaluating epilepsy, sleep disorders and other conditions.
But these electrodes have changed little since their introduction, and are far from perfect. Among other things, they pick up extraneous noise and movement in addition to brain wave activity, often making the readings difficult to interpret.
Walt Besio thinks he has a better way.
The National Science Foundation-funded scientist, who is associate professor of biomedical engineering at the University of Rhode Island, has invented a new and improved electrode, one that produces a performance difference that he says is akin to “taking the rabbit ears you used to have for your television set, and converting to high definition.”
His innovation is relatively simple, but apparently makes a big difference. Besio added two new metal rings around the basic disc, a change that eliminates outside noises and improves spatial resolution.
"EEG has two main problems: It’s very noisy and contaminated with artifacts, and it’s spatial resolution is bad," he explains. "We have improved the signal-to-noise ratio. It’s four times better than it was before. Because it is now a very local signal, it means we can put electrodes closer together, which improves spatial resolution, meaning you can better determine where the signal is coming from."
The additional rings work almost like an inner tube tossed on top of a rippling body of water. “The water is flat in the center of the inner tube and choppy on the outside,” he says. “The outer rings on the electrodes behave like that inner tube.”
For researchers and clinicians, having improved electrodes could open up potential new uses, as well as improve current ones-more accurate epilepsy diagnosis, for example, as well as the promise of “reading” someone’s thoughts in the future, with the goal, for example, of activating an otherwise inert body part, such as an arm or leg, and ultimately helping people with spinal cord injuries.
The aim is to have the highly sensitive electrodes first translate a person’s thoughts into electrical impulses that can be read by a computer, then, eventually move to robots, and later, limbs. Other scientists are conducting similar research, but Besio wants to show “that it works better with these types of electrodes.”
Mu-rhythm in the brain: The neural mechanism of speech as an audio-vocal perception-action system
Speech production is one of the most important components in human communication. However, the cortical mechanisms governing speech are not well understood because it is extremely challenging to measure the activity of the brain in action, that is, during speech production.
Now, Takeshi Tamura and Michiteru Kitazaki at Toyohashi University of Technology, Atsuko Gunji and her colleagues at National Institute of Mental Health, Hiroshige Takeichi at RIKEN, and Hiroaki Shigemasu at Kochi University of Technology have found modulation of mu-rhythms in the cortex related to speech production.
The researchers measured EEG (electroencephalogram) with pre-amplified electrodes during simulated vocalization, simulated vocalization with delayed auditory feedback, simulated vocalization under loud noise, and silent reading. The authors define ‘mu-rhythm’ as a decrease of power in 8-16Hz EEG during the task period.
The mu-rhythm at the sensory-motor cortical area was not only observed under all simulated vocalization conditions, but was also found to be boosted by the delayed feedback and attenuated by loud noises. Since these auditory interferences influence speech production, it supports the premise that audio-vocal monitoring systems play an important role in speech production. The motor-related mu-rhythm is a critical index to clarify neural mechanisms of speech production as an audio-vocal perception-action system.
In the future, a neurofeedback method based on monitoring mu-rhythm at the sensory-motor cortex may facilitate rehabilitation of speech-related deficits.
Ever wondered what your brain sounds like? Now you can hear the sweet melody of the mind by listening to sound composed from an analysis of brain activity.
Mickey Hart, Grateful Dead percussionist, and neurologist Adam Gazzaley, M.D., Ph.D., professor at the University of California San Francisco made history by becoming the first to sonify and visualize brain activity in real time in front of a live audience. The two did so at the closing session of Life @50+, the AARP National Event & Expo in New Orleans on September 22nd.
Brain and brain waves in epilepsy
Caption: 3D magnetic resonance imaging (MRI) scan of a brain (seen from the front), overlaid with an electroencephalogram (EEG) of a 17-year-old’s brain during an epileptic episode (chaotic brain activity). This EEG shows generalized epilepsy, where the whole brain cortex is affected: all the EEG traces show chaotic brain waves. Epilepsy can have many causes, but when the cause is unknown, as here, it is called essential epilepsy. An EEG measures the electrical activity of the brain using electrodes attached to the scalp. The electrode locations are labelled at far left, on diagrams of the head seen from above, with the front of the head at left.
A wireless low-power, high-quality EEG headset
Imec, Holst Centre and Panasonic have developed a new prototype of a wireless EEG (electroencephalogram, or brain waves) headset designed to be a reliable, high-quality and wearable EEG monitoring system.
The system combines ease-of-use with ultra-low power electronics. Continuous impedance monitoring and the use of active electrodes increases the quality of EEG signal recording compared to former versions of the system.
The EEG data is transmitted to a receiver located up to 10 meters away. The headset integrates active electrodes (reduce the susceptibility of the system to power-line interference and cable motion artifacts to improve signal quality), EEG amplifier, microcontroller, and low-power wireless transmitter.
The receiver can continuously record 8-channel EEG signals while concurrently recording electrode-tissue contact impedance (ETI), a measure of contact quality.
The system has a high (>92 dB) common-mode rejection ratio (to reduce interference from power lines and other sources) and low noise (<6 µVpp, 0.5-100Hz), with configurable cut-off frequency (to filter out high or low frequencies).
The heart of the system is the low-power (750µW) 8-channel EEG monitoring chipset. Each EEG channel consists of two active electrodes and a low-power analog signal processor. The EEG channels are designed to extract high-quality EEG signals under a large amount of common-mode interference. The active electrode chips have buffer functionality with high input impedance (1.4GΩ at 10Hz), enabling recordings from dry electrodes, and low output impedance reducing the power-line interference without using shielded wires
The system is integrated into imec’s EEG headset with dry electrodes, which enables EEG recordings with minimal set-up time. The small size of the electronics system, measuring only 35mm x 30mm x 5mm (excluding battery), allows easy integration in any other product.
The BCMI-MIdAS (Brain-Computer Music Interface for Monitoring and Inducing Affective States) project
The central purpose of the project is to develop technology for building innovative intelligent systems that can monitor our affective state, and induce specific affective states through music, automatically and adaptively. This is a highly interdisciplinary project, which will address several technical challenges at the interface between science, technology and performing arts/music (incorporating computer-generated music and machine learning).
Research questions
- How can music change affective states and what are the specific musical traits (i.e., the parameters of a piece of music) that elicit such states?
- How can we control such traits in a piece of music in order to induce specific affective states in a participant?
- How can we effectively detect information about affective states induced by music in the EEG signal, going beyond EEG asymmetry and characterising information contained in synchronisation patterns?
- How can we use the EEG to monitor the affective state induced by music on-line (i.e., in “real-time”)?
- How can we produce a generative music system capable of generating music embodying musical traits aimed at inducing specific affective states, observable in the EEG of the participant?
- How can we build an intelligent adaptive system for monitoring and inducing affective states through music on-line?
(Source: cmr.soc.plymouth.ac.uk)
At this year’s Tokyo Games Show, Japanese purveyor of electronically-augmented fashion Neurowear unveiled the successor to its Necomimi brain-activated cat ears. It’s called Shippo, and it’s a brain-controlled motorized tail that responds to the user’s current emotional state with corresponding wagging.
Shippo requires a NeuroSky electroencephalograph (EEG) headset, alongside a clip-on heart monitor, in order to observe brain activity and pick up on the user’s emotional state. This information is then translated to wagging, which will be soft and slow or hard and fast, depending on whether one is relaxing or excited/anxious. The EEG headset communicates with the fluffy appendage via a Bluetooth connection.
Research at Sandia National Laboratories has shown that it’s possible to predict how well people will remember information by monitoring their brain activity while they study.
A team under Laura Matzen of Sandia’s cognitive systems group was the first to demonstrate predictions based on the results of monitoring test volunteers with electroencephalography (EEG) sensors.
For example, “if you had someone learning new material and you were recording the EEG, you might be able to tell them, ‘You’re going to forget this, you should study this again,’ or tell them, ‘OK, you got it and go on to the next thing,’” Matzen said.
The study, funded under Sandia’s Laboratory Directed Research and Development program (LDRD), had two parts: predicting how well someone will remember what’s studied and predicting who will benefit most from memory training.