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image credit: L. Cicero
February 2018


Krishna Shenoy and team have been researching the use of brain machine interfaces (BMI) to assist people with paralysis. Recently, one of the researchers changed the task, requiring physical movement from a change in thought. He realized that the BMI would allow study of the mental rehearsal that occurs before the physical expression.

Although there are some important caveats, the results could point the way toward a deeper understanding of what mental rehearsal is and, the researchers believe, to a future where brain-machine interfaces, usually thought of as prosthetics for people with paralysis, are also tools for understanding the brain.

"Mental rehearsal is tantalizing, but difficult to study," said Saurabh Vyas, a graduate student in bioengineering and the paper's lead author. That's because there's no easy way to peer into a person's brain as he imagines himself racing to a win or practicing a performance. "This is where we thought brain-machine interfaces could be that lens, because they give you the ability to see what the brain is doing even when they're not actually moving," he said.

"We can't prove the connection beyond a shadow of a doubt," Krishna said, but "this is a major step in understanding what mental rehearsal may well be in all of us." The next steps, he and Vyas said, are to figure out how mental rehearsal relates to practice with a brain-machine interface – and how mental preparation, the key ingredient in transferring that practice to physical movements, relates to movement.

Meanwhile, Krishna said, the results demonstrate the potential of an entirely new tool for studying the mind. "It's like building a new tool and using it for something," he said. "We used a brain-machine interface to probe and advance basic science, and that's just super exciting."

Additional Stanford authors are Nir Even-Chen, a graduate student in electrical engineering, Sergey Stavisky, a postdoctoral fellow in neurosurgery, Stephen Ryu, an adjunct professor of electrical engineering, and Paul Nuyujukian, an assistant professor of bioengineering and of neurosurgery and a member of Stanford Bio-X and the Stanford Neurosciences Institute.

Funding for the study came from the National Institutes of Health, the National Science Foundation, a Ric Weiland Stanford Graduate Fellowship, a Bio-X Bowes Fellowship, the ALS Association, the Defense Advanced Research Projects Agency, the Simons Foundation and the Howard Hughes Medical Institute.

Excerpted from Stanford News, "Mental rehearsal prepares our minds for real-world action, Stanford researchers find," February 16, 2018.

 

Related News:

Research by PhD candidate and team detects errors from Neural Activity, November 2017.

Krishna Shenoy's translation device; turning thought into movement, March 2017.

Brain-Sensing Tech Developed by Krishna Shenoy and Team, September 2016.

Krishna Shenoy receives Inaugural Professorship, February 2017.

 

February 2018

Angad Rekhi (PhD candidate) and Amin Arbabian have developed a wake-up receiver that turns on a device in response to incoming ultrasonic signals – signals outside the range that humans can hear. By working at a significantly smaller wavelength and switching from radio waves to ultrasound, this receiver is much smaller than similar wake-up receivers that respond to radio signals, while operating at extremely low power and with extended range.

This wake-up receiver has many potential applications, particularly in designing the next generation of networked devices, including so-called "smart" devices that can communicate directly with one another without human intervention.

"As technology advances, people use it for applications that you could never have thought of. The internet and the cellphone are two great examples of that," said Rekhi. "I'm excited to see how people will use wake-up receivers to enable the next generation of the Internet of Things."

Excerpted from Stanford News, "Stanford researchers develop new method for waking up small electronic devices", February 12, 2018

 

Related news:

Amin's Research Team Powers Tiny Implantable Devices, December 2017.

Stanford Team led by Amin Arbabian receives DOE ARPA-E Award, January 2017.

Amin Arbabian receives Tau Beta Pi Undergrad Teaching Award, June 2016.

December 2017

The Stanford chapter of Tau Beta Pi, an engineering honor society, is proud to announce the inaugural "Teaching Honor Roll," which recognizes the extraordinary teaching of 12 educators in the School of Engineering, three are from Electrical Engineering.

Selection criteria include great teaching, extraordinary inspiration to study a topic, outstanding mentoring and particularly creative lecturing, but are by no means limited to these characteristics. Any undergraduate in the School of Engineering can nominate an instructor.

The 2017 honorees in the Tau Beta Pi Teaching Honor Roll include Electrical Engineering's Stephen Boyd, Reza Mahalati, and Rahul Prabala (BS '16, MS '17).

"I'm so glad to be able to make an impact with EE108," said Rahul Prabala (BS '16, MS '17) on hearing the news of his inclusion. "And I'm honored to be part of the first TBP Teaching Honor Roll."

The honor roll will be displayed in the Jen-Hsun Huang Engineering Center, with plaques bearing the names and short quotes from this year's 12 recipients. The Teaching Honor Roll wall can be found on the ground floor of Huang, near NVIDIA Auditorium. In subsequent years, a list of previous winners will be maintained on the Tau Beta Pi Honor Roll website.

Tau Beta Pi is the nation's second oldest honor society. Founded in 1885, it has chapters in at least 242 U.S. colleges and universities and a membership of well over 550,000. Tau Beta Pi promotes academic excellence, civic leadership and community service for students. In their duties, members organize panel discussions, host industry dinners and conduct math and science programs at local K-12 schools, among many other activities.

 

Congratulations Stephen, Reza, and Rahul!

Excerpted from Stanford Engineering's, "Tau Beta Pi engineering honor society debuts its "Teaching Honor Roll"" Dec. 6, 2017.

December 2017

We are very proud of the research being done by our graduate and undergraduate students.

Throughout the academic year, we encourage students to present at conferences and related interdisciplinary events. The practice of sharing and speaking about research to a variety of audiences is a quality we encourage. We are pleased to again acknowledge electrical engineering students who have been recognized for their presentation, poster, and/or paper awards.


Jerry Chang (EE PhD candidate)
Ting Chia (Jerry) Chang (PhD candidate '20) is the lead author of "Scaling of Ultrasound-Powered Receivers for Sub-Millimeter Wireless Implants." He and his co-authors received the Best Paper Award at the 2017 IEEE BioCAS Conference.
 

Ruishan Liu (EE PhD candidate)
Ruishan Liu (PhD candidate) received the Best Poster Award at the Bay Area Machine Learning Symposium. Ruishan belongs to the Stanford Laboratory for Machine Learning group, advised by Professor James Zou. Ruishan develops algorithms and theories in machine learning and reinforcement learning, and is interested in applications in genomics and healthcare.

Her poster title is, "The Effects of Memory Replay in Reinforcement Learning."

PhD candidates Connor McClellan and Fiona Ching-Hua Wang
PhD candidates Connor McClellan and Fiona Ching-Hua Wang each received the Best in Session Award at the TechCon 2017.

  • Connor's paper, "Effective n-type Doping of Monolayer MoS2 by AlO(x)" was presented in the 2-D and TMD Materials and Devices: I session. Professor Eric Pop is Connor's advisor 
  • Fiona's paper, "N-type Black Phosphorus Transistor with Low Work Function Contacts," was presented in the 2-D and TMD Materials and Devices: III session. Professor H.-S. Philip Wong is Fiona's advisor. 
Read More

David Hallac EE PhD candidate
David Hallac, EE PhD candidate, is the lead author of "Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data," which has been selected to receive the KDD 2017 Conference Best Paper Runner-Up Award and the Best Student Paper runner-up Award.
 

Iliana Erteza Bray EE PhD candidate
Iliana Erteza Bray, EE PhD candidate, received The Firestone Medal for Excellence in Undergraduate Research. Her paper is titled, “Frequency Shifts and Depth Dependence of Beta Band Activity in Rhesus Premotor Cortex Perceptual Decision-Making.” She ia advised by Krishna Shenoy (Electrical Engineering).
 

 JULY 2017 PhD candidates Alex Gabourie and Saurabh Suryavanshi
PhD candidates Alex Gabourie and Saurabh Suryavanshi received Best Paper Award at the 17th IEEE International Conference on Nanotechnology (IEEE NANO 2017). Their paper is titled, "Thermal Boundary Conductance of the MoS2-SiO2 Interface."

Kirby Smithe EE B.S. candidate
Kirby Smithe (PhD candidate) received first place for his presentation, "High-field transport and velocity saturation in CVD monolayer MoS2" at the EDISON 20 Conference.
 
Kirby's research involves growth and material characterization of 2D semiconductors and engineering 2D electronic devices for circuit-level applications. He is the recipient of the Stanford Graduate Fellowship as well as the NSF Graduate Fellowship. Kirby is advised by Professor Eric Pop.
 

Yuanfang Li (M.S. candidate) and Dr. Ardavan Pedram
Co-authors Yuanfang Li (MS candidate) and Dr. Ardavan Pedram received the Best Paper Award at the 28th annual IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP).

Yuanfang Li is an M.S. candidate and Dr. Ardavan Pedram is a senior research associate who manages the PRISM Project. The PRISM project enables the design of reconfigurable architectures to accelerate the building blocks of machine learning, high performance computing, and data science routines.

EE Admit Weekend poster winners
EE Admit Weekend hosts a competitive poster session. The presenting students are judged by faculty, peers and staff and scored on their presentation, poster, and professionalism. The awards went to:
  • Leighton Barnes winner in Information Systems and Science for poster titled "Geometry and the Relay Channel,” 
  • Adrian Alabi in Hardware/Software Systems for poster titled "915 MHz FSK Detection for Wireless Ultrasonic Imaging Data Reception,” 
  • Max Wang in Physical Technology and Science for poster titled "Minimally Invasive Ultrasonically Powered Implants for Next-Generation Therapies and Neuromodulation” 
Read More

December 2017

From desktop to laptop to mobile devices and wearables, personal computing platforms continue to evolve. Virtual reality (VR) and augmented reality (AR) are among the fastest evolving such platforms. VR is an immersive experience that replaces the user's real world with a simulated one. With VR, the user typically wears a headset and/or other wearables that provide simulated interaction through sound, haptics, and graphics. Augmented reality (AR) is not immersive, although it does add elements to the user's reality. An example of AR is the real time translation of traffic signs and restaurant menus while traveling in another country. Applications of VR and AR systems have been gaining in popularity and span entertainment, education, communication, training, behavioral therapy, and basic vision research.

VR and AR provide a host of opportunities for engineers to design new sensors, displays, algorithms, and embedded systems, as well as develop new applications. Stanford students interested in learning about VR and AR systems have been flocking to a new course developed by professor Gordon Wetzstein. The course, EE 267: Virtual Reality, emphasizes aspects of VR systems such as rendering, tracking, haptics, inertial measurement units, depth perception, and presence (or immersion).

EE 267, now in its third year, continues to appeal to undergrad and graduate students both within and outside of the electrical engineering department. The primary course objective is to build a head mounted display (HMD) from scratch and to create a final project of the student's own virtual environment. Past student projects have included innovative combination of 2d and 3d inputs; collection of user data via VR interaction; and developing VR immersive viewing options for medical scans.

"Many final projects are extraordinarily creative and provide novel solutions to current problems," states professor Wetzstein. "The students are enthusiastic to share their work and usually a few interested Silicon Valley companies attend our final presentations."

 

From a past student – "It became clear within my first week [of my internship] that everything in the EE 267 syllabus is relevant to what I'm doing here at Google and I would have been completely lost if I had not taken your class before starting this internship. There could not have been a better primer for working in VR/AR than your class and I hope that you will continue teaching it for many years!

When I tell my coworkers that I got to take a VR class at Stanford where we built our own HMDs, they are all very jealous and wish they could have had an opportunity like that when they were in grad school."

December 2017

Ting Chia (Jerry) Chang (PhD candidate '20) is the lead author of "Scaling of Ultrasound-Powered Receivers for Sub-Millimeter Wireless Implants." He and his co-authors received the Best Paper Award at the 2017 IEEE BioCAS Conference.

The 13th IEEE Biomedical Circuits and Systems Conference (BioCAS), was held October 19-21 in Turin, Italy. The annual conference is a premier international forum for researchers and engineers to present their state-of-the-art multidisciplinary research and development activities at the frontiers of medicine, life sciences, and engineering.

Jerry's research focuses on circuits and system design for miniaturized wireless medical implantable devices with ultrasonic links. He is advised by professor Amin Arbabian, who oversees the ArbabianLab Ultrasonically Powered Implantable Devices research team.

 

Congratulations to Jerry Chang and co-authors: M. J. Weber, J. Charthad, S. Baltsavias, and A. Arbabian for receiving the best paper award!

Paper Abstract:
We investigate scaling of ultrasound-powered wireless receivers for efficient, miniaturized implantable medical devices. Single crystalline piezoelectric material, PMN-PT, is chosen in this study as it has low resonance frequency with scaled dimensions. For accurate modeling of sub-mm-sized receivers, we perform simulations using the finite element method, followed by validation with measurements. Results are presented for scaling of the resonance frequency, resistance at resonance, and aperture efficiency of PMN-PT receivers with thickness of 0.5 mm and widths ranging from 0.3 mm to 1.0 mm. Since optimizing the overall harvesting efficiency of an implant requires not only an efficient receiver but also an efficient interface to the power electronics, we analyze impedance matching efficiency between the receivers and the power electronics using optogenetic stimulation as an example application. Finally, we show the measurement of prototype implants with scaled receivers and discuss the trade-off between size and power harvesting efficiency of sub-mm wireless implants.

 

 

October 2017

A dozen teams of EE students came together Friday afternoon to compete in EE's Annual Pumpkin Carving Contest.

This year's event was hosted in the Packard Atrium, with plenty of candy, refreshments, and music. View photo album.

Judges included student services staff Rachel Pham, graduate student Jerry Shi, and Professor Juan Rivas-Davila. Judging criteria included completeness, technical skill, creativity, and costumes.

In addition to judge's scores, a number of voting ballots were also available for attendees to vote for their favorite pumpkin. They were added to judges totals and counted toward the final result.

Third Place went to the "Eevee Evolution" of David Zeng, Tracey Hong, and Neal Jean.
Second place went to "EE42" Team, whose members are Katherine Kowalski, Amit Kohli, Justin Babauta, and Alec Preciado.
The First place team was "Pumpkin Carving Dream Team" of Nicole Grimwood, Nicolo Maganzini, Tori Fujinami, and Tong Mu.

 

Thanks to all of our staff, faculty, and students for your enthusiastic participation!

PhD candidate Nir Even-Chen
November 2017

PhD candidate Nir Even-Chen and his advisor, professor Krishna Shenoy, et al., share recent strides in brain-machine interface (BMI) innovation. BMIs are devices that record neural activity from the user's brain and translate it into movement of prosthetic devices. BMIs enable people with motor impairment, e.g. a spinal cord injury, to control and move prosthetic devices with their minds. They can control robotic arms for improving their independence or a computer cursor for typing and browsing the web. Even-Chen, et al's, recently published paper, "Augmenting intracortical brain-machine interface with neurally driven error detectors," describes a new system that reads users minds, detects when the user perceives a mistake, and intervenes with a corrective action. The new system allows users to control BMIs more easily, smoothly, and efficiently.

While most BMI studies focus on designing better techniques to infer the user's movement intention, Even-Chen, et al, improved the BMI performance by taking a very different approach, detecting and undoing mistakes. Their work presents both novel fundamental science and implementation of their idea. They showed for the first time that it is possible to detect key-selection errors from the motor cortex — a brain area mainly involved in movement control. Then, they used the data in real-time to undo—or even prevent—mistakes.

The need for real-time error correction

In our daily life, we all make mistakes, from typos during texting, clicking the wrong link on a web page, or knocking our cup of coffee over while reaching for the cake. Correcting these mistakes might be time-consuming, and annoying—especially when they occur frequently during challenging tasks. Imagine a system that could detect – or predict – your mistakes (e.g., typos) and automatically undo, or even prevent them from happening. This can save the time of manually correcting the mistake, especially when the errors are frequent and the actions to correct them slow you down. Error detection is not always trivial, in some cases only the person who made the mistake knows what she intended. Thus, such an error detection system needs to infer one's intention, i.e., read her mind. An automatic error detection system is most effective when the task is challenging or when our skill is limited, and errors are common. A good candidate for testing such an error detection approach is a BMI system. First, BMIs enable a readout of the user's mind. And second, it can be highly beneficial for BMI users, since BMI control is challenging and prone to errors.

Intracortical BMIs, which records neural activity directly from the brain, showed a promising result in pilot clinical trials and are the highest-performing BMI systems to date. This makes them prime candidates for serving as an assistive technology for people with paralysis. Although the performance of intracortical BMI systems has markedly improved in the last two decades, errors — such as selecting the wrong key during typing — still occur and their performance is far from able-bodied performance. 

Previously it was unknown if errors can be detected from the same brain region traditionally used for decoding BMI user's movement intention—the motor cortex. In their work, Even-Chen and colleagues found that when errors occur a characteristic brain activity can be observed. That brain activity pattern enables them to detect mistakes with high accuracy shortly after and even before they occurred.

This finding encouraged them to develop and implement first-of-its-kind error "detect-and-act" system. This system reads the user's mind, detects when the user thinks an error occurred, and can automatically "undo" or "prevent" them. The detect-and-act system works independently and in parallel to a traditional movement BMI that estimate user's movement intention (see figure). In a challenging BMI task that resulted in substantial errors, this approach improved the performance of a BMI. Using the detect-and-act system, hard tasks will have fewer errors and become easier, the use of a BMI will become smoother, and be less frustrating.

A detect-and-act system can potentially be used to improve how fast people with paralysis can type or control a robotic arm using a BMI. For example, automatically correcting a mistake when they type, or stopping the movement of a robotic arm when they are about to knock over their coffee. While this work has been done in pre-clinical trial with monkeys, Even-Chen and colleagues also presented encouraging preliminary results of a clinical trial (BrainGate2) at a conference, and showed the potential translation to humans.

 

Read more: Journal of Neural Engineering, "Augmenting intracortical brain-machine interface with neurally driven error detectors."
Additional authors include Sergey Stavisky, Jonathan Kao, Stephen Ryu, and Krishna Shenoy. 

 

October 2017

Ruishan Liu (PhD candidate) has received the Best Poster Award at the Bay Area Machine Learning Symposium, October 19, 2017. Ruishan belongs to the Stanford Laboratory for Machine Learning group, advised by Professor James Zou. Ruishan develops algorithms and theories in machine learning and reinforcement learning. She is also interested in applications in genomics and healthcare.

 

Poster Title:
"The Effects of Memory Replay in Reinforcement Learning"

Poster Abstract:
Experience replay is a key technique behind many recent advances in deep reinforcement learning. Despite its wide-spread application, very little is understood about the properties of experience replay. How does the amount of memory kept affect learning dynamics? Does it help to prioritize certain experiences?

In our work, we address these questions by formulating a dynamical systems ODE model of Q-learning with experience replay. We derive analytic solutions of the ODE for a simple setting. We show that even in this very simple setting, the amount of memory kept can substantially affect the agent's performance. Too much or too little memory both slow down learning.

We also proposed a simple algorithm for adaptively changing the memory buffer size which achieves consistently good empirical performance.

 

Congratulations to Ruishan!

July 2017

Kirby Smithe (PhD candidate) received first place for his presentation, "High-field transport and velocity saturation in CVD monolayer MoS2" at the EDISON 20 Conference in July.

All student preesenters were ranked by a committee comprised of members of the International Advisory Committee. More than 25 presentations and posters were evaluated by this committee. Kirby's award is accompanied by $300 and a glass commemorative trophy.

 

Kirby's research involves growth and material characterization of 2D semiconductors and engineering 2D electronic devices for circuit-level applications. He is the recipient of the Stanford Graduate Fellowship as well as the NSF Graduate Fellowship. Kirby is part of the Pop Lab research group, advised by Professor Eric Pop.

 

Congratulations to Kirby!

 

 

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