A next-gen EEG could help bring back lost brain function

SLAC and Stanford researchers are developing a device that combines electrical brain stimulation with EEG recording, opening potential new paths for treating neurological disorders.

By Manuel Gnida, SLAC National Accelerator Laboratory June 27, 2018

A device under development at the Department of Energy’s SLAC National Accelerator Laboratory and Stanford University could help bring back lost brain function by measuring how the brain responds to therapies that stimulate it with electric current.

The approach could open new avenues for treating brain disorders and selectively switching brain activities on and off, says Anthony Norcia, a professor of psychology at Stanford who initiated the project.

Neurostimulation via electrodes placed on the scalp shows a lot of promise, but its immediate effects are hard to study because the brain’s neural response gets easily swamped by the million times stronger pulses that researchers send into the brain. To detect the much fainter brain response, scientists had to monitor brain waves and behavioral response in separate sessions before and after stimulation. The new device measures brain waves at practically the same time the stimulus is applied, potentially establishing a much better link between the two.

“The device works similar to radar, which sends out electromagnetic waves and passively listens for the weaker reflected waves,” says SLAC senior scientist Christopher Kenney. “Here, we send electrical pulses into the head via the electrodes of an EEG monitoring system, and in the time between those strong pulses we use the same electrodes to pick up the much weaker electrical signals from inside the head.”

Stimulating the Electrical Brain

Our brain is an intricate network of hundreds of billions of neurons, and anything that interrupts this network, such as abnormal brain development or a stroke, can cause severe disorders, including epilepsy, depression, anxiety, visual impairment, chronic pain and paralysis.

Stimulating brain tissue alters the way neurons fire and helps the brain form neural connections. Norcia’s research focuses on applying the method to cases of visual impairment, such as amblyopia (lazy eye) and strabismus (crossed eyes), and on better understanding phenomena like binocular rivalry, which describes the fact that when presented with two different images at the same time, we can only be aware of one at a time.

Norcia’s group develops models that describe how electrical activity from the brain’s visual centers radiates to the scalp, where it can be picked up and measured by an EEG. They also develop models for delivering electrical pulses to specific locations in the brain, where they alter brain function associated with vision.

“Our models give us a pretty good idea for how to design an array of electrodes to reach specific volumes inside the head,” Norcia says. “But we also want to be able to ‘listen’ to the brain’s response at the same time to figure out whether an applied stimulus had the desired effect.”

Doing so simultaneously isn’t possible with today’s clinical EEG systems, but that may soon change thanks to the collaboration with SLAC.

A New Type of EEG

Searching for a solution to the technical challenge, Norcia began talking to Kenney, who specializes in detector development for SLAC experiments that study nature’s most fundamental physics processes, and Martin Breidenbach, a professor of particle physics and astrophysics at SLAC and Stanford.

“At SLAC, we’re trying to answer some of the really big questions about our universe, and figuring out how the human mind works seems to be right up there,” Breidenbach says. “We certainly have the engineering skills and resources to help with some of the technical issues in neuroscience. With our background in high-energy physics, we’re also used to multidisciplinary collaborations and know how to make them work.”

About a year after the project received funding through Stanford Bio-X, the team has successfully tested a prototype for an EEG system that can deliver electrical brain stimulation and measure the brain’s ongoing activity at the same time.

To do so, they paired the electronics board of a conventional EEG monitor with another one they built that delivers electric stimuli generated with 9-volt batteries. Then they successfully tested the device on themselves.

Toward Medical Therapy

More work needs to be done before studies on a larger group of people can begin. For example, future versions of the device will have more electrodes and will provide more control over the way the pulses are delivered.

“Right now, we can basically switch stimuli on and off and set their intensities and durations,” says SLAC’s Jeff Olsen, an electrical engineer on the project. “In the next generation, we’ll be able to program the device, which will let us choose different types of signal shapes and synchronize electrical signals with other external triggers, such as visual stimulation.”

But the team’s plans don’t stop there.

“In the long run, we would like to develop a device on a chip,” Kenney says. That would make neurostimulation available to patients wherever they go.

Other collaborators involved in this project include Stephen Boyd, chair of Stanford’s Department of Electrical Engineering, and Nolan Williams, clinical assistant professor of psychiatry and behavioral sciences at Stanford.

SLAC is a multi-program laboratory exploring frontier questions in photon science, astrophysics, particle physics and accelerator research. Located in Menlo Park, Calif., SLAC is operated by Stanford University for the U.S. Department of Energy’s Office of Science.

SLAC National Accelerator Laboratory is supported by the Office of Science of the U.S. Department of Energy. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit science.energy.gov.

Source SLAC National Accelerator Laboratory

Reading the motor intention from brain activity within 100 ms

Yasuharu Koike, Tokyo Institute of Technology August 2, 2018

A collaborative study by researchers at Tokyo Institute of Technology has developed a new technique to decode motor intention of humans from Electroencephalography (EEG).

This technique is motivated by the well documented ability of the brain to predict sensory outcomes of self-generated and imagined actions utilizing so called forward models. The method enabled for the first time, nearly 90% single trial decoding accuracy across tested subjects, within 96 ms of the stimulation, with zero user training, and with no additional cognitive load on the users.

The ultimate dream of brain computer interface (BCI) research is to develop an efficient connection between machines and the human brain, such that the machines may be used at will. For example, enabling an amputee to use a robot arm attached to him, just by thinking of it, as if it was his own arm.

A big challenge for such a task is the deciphering of a human user’s movement intention from his brain activity, while minimizing the user effort. While a plethora of methods have been suggested for this in the last two decades (1-2) they all require large effort in part of the human user — they either require extensive user training, work well, with only a section of the users, or need to use a conspicuous stimulus, inducing additional attentional and cognitive loads on the users.

In this study, Researchers from Tokyo Institute of Technology (Tokyo Tech), Le Centre national de la recherche scientifique (CNRS-France), AIST, and Osaka University propose a new movement intention decoding philosophy and technique that overcomes all these issues while providing equally much better decoding performance.

The fundamental difference between the previous methods and what they propose is in what is decoded. All the previous methods decode what movement a user intends/imagines, either directly (as in the so called active BCI systems) or indirectly, by decoding what he is attending to (like the reactive BCI systems). Here the researchers propose to use a subliminal sensory stimulator with the Electroencephalography (EEG), and decode, not what movement a user intends/imagines, but to decode whether the movement he intends matches (or not) the sensory feedback sent to the user using the stimulator.

Their proposal is motivated by the multitude of studies on so called Forward models in the brain; the neural circuitry implicated in predicting sensory outcomes of self-generated movements (3). The sensory prediction errors, between the forward model predictions and the actual sensory signals, are known to be fundamental for our sensory-motor abilities- for haptic perception (4), motor control (5), motor learning (6), and even inter-personal interactions (7-8) and the cognition of self (9). The researchers therefore hypothesized the predictions errors to have a large signature in EEG, and perturbing the prediction errors (using an external sensory stimulator) to be a promising way to decode movement intentions.

This proposal was tested in a binary simulated wheelchair task, in which users thought of turning their wheelchair either left or right. The researchers stimulated the user’s vestibular system (as this is the dominant sensory feedback during turning), towards either the left or right direction, subliminally using a galvanic vestibular stimulator. They then decode for the presence of prediction errors (ie. whether or stimulation direction matches the direction the user imagines, or not) and consequently, as the direction of stimulation is known, the direction the user imagines. This procedure provides excellent single trial decoding accuracy (87.2% median) in all tested subjects, and within 96 ms of stimulation. These results were obtained with zero user training and with no additional cognitive load on the users, as the stimulation was subliminal.

This proposal promises to radically change how movement intention is decoded, due to several reasons. Primarily, because the method promises better decoding accuracies with no user training and without inducing additional cognitive loads on the users. Furthermore, the fact that the decoding can be done in less than 100 ms of the stimulation highlights its use for real-time decoding. Finally, this method is distinct from other methods utilizing ERP, ERD and ERN, showing that it can be used in parallel to current methods to improve their accuracy.

Source Tokyo Institute of Technology

References

Utilizing sensory prediction errors for movement intention decoding: A new methodology, Ganesh G, Nakamura K, Saetia S, Tobar AM, Yoshida E, Ando H, Yoshimura N, Koike Y. Sci Adv. 2018 May 9;4(5):eaaq0183. doi: 10.1126/sciadv.aaq0183. Full text