Brain Computer Interfaces Benefit from Cloud Advancements

By Kate Ericson

March 23, 2011

What do you get when you mix compute clouds and electroencephalograms (EEG) together? Ask Kathleen Ericson a PhD candidate in the Department of Computer Science at Colorado State University, who in a paper coauthored with Professors Shrideep Pallickara and Charles Anderson has explored some of these possibilities [1]. This paper was awarded the Best Student Paper award at the IEEE Conference on Cloud Computing Technology & Science in December 2010.

Brain Computer Interfaces (BCIs) have been gaining traction in recent years. These applications range from allowing people who have lost voluntary motor control to type at a keyboard [2] and also to allow navigating a wheelchair through a crowded room [3]. These applications rely on EEG data gathered from electrodes held close to the scalp. Machine Learning techniques, such as artificial neural networks, can then be used to interpret the user’s intent from these signals. 

EEG analysis is usually performed in physical proximity to the user that, in turn, can lead to limitations in the processing power available for analyzing the EEG signals. For example, the wheelchair application relies on a laptop carried by the user for all EEG analysis. Professor Anderson has been researching EEG classification problems for several years.

In current BCI applications, there is a one-to-one relationship between the user and the machine.  This usually means that there is a single, very well trained neural network that has been fine-tuned to interacting with that individual. Training a neural network to the point where it can provide meaningful classifications can be time consuming. EEG classification has the additional difficulty that the signals may change over time due to user fatigue. Because of user fatigue, a fine-tuned neural network may need to undergo a period of retraining while in use. 

The CSU team considered an alternative to the approach of a single well-trained neural network: the group of experts approach. This approach involves training many smaller neural networks.  Each network is smaller and less well trained than a single neural network would need to be – meaning that the training process is much shorter. While none of these networks can individually learn enough to accurately classify all data, each learns something slightly different, and an accurate classification can be built upon their predictions as a group. But such an approach also means the need for more compute capabilities.

The decision to moving the EEG analysis to the cloud allowed the team to move away from the one-to-one relationship that is common between users and machines in BCI applications. This is also where the Granules [4, 5] cloud runtime (created by Prof. Pallickara, the author’s PhD advisor) comes in. Granules is uniquely suited to processing such EEG streams. Granules provisions a radically different computation model. Unlike traditional computations that have a run-once semantics, computations in Granules have a lifetime associated with it and can execute multiple times and retain state across multiple executions. This feature comes in particularly handy when you are processing EEG streams in real time.

Using Granules, instead of having a single neural network devoted to classifying EEG signals, one could use a whole cluster. The group of experts’ approach is a particularly good fit for the Map-Reduce paradigm that is supported in Granules. Each mapper is responsible for training and maintaining a neural network.  When a mapper has classified data, it sends its classification on to the reducer. The reducer waits for all mappers to weigh in, and then produces an expert opinion based on the predictions of all the mappers. While their current implementation simply returns the most predicted classification, it is possible to train another neural network on the reducer that can learn which mappers have the best predictions and add appropriate weights to incoming predictions.

While other cloud runtimes, such as Hadoop [6], demand run-once semantics, Granules allows computations to be activated as more data is available (such as new EEG streams being generated), and enter a dormant state between rounds of execution.  Granules is then able to store state between successive rounds of execution.  This allowed the CSU team to train neural networks on a set of resources within Granules, and then stream EEG signals to the cloud for classification.  In Hadoop, this would have required one to write the neural network to file between rounds of execution, and load it back into memory before classifying any data.  This would have precluded the possibility of classifying EEG signals in real time.

An additional benefit of using Granules is the ability to concurrently interleave several long-running computations simultaneously on a given machine.  This means that a single cluster of neural networks can support thousands of users simultaneously.  In their experiments, the team at CSU has supported EEG streams generated by 150 users on a cloud of 10 computers.  In these experiments the streams were generated from a pre-recorded dataset, and were sent in bursts every 250ms for every user.  This resulted in their system classifying EEG streams at the rate of 12MB/s, 1GB/83s, and 1TB in 23 hours. 

This cloud returned classification results in under 250ms (before the next set of data is sent) in 99.9% of the test cases.  With 150 users, 0.04% of the messages were over the 250ms threshold: one of every 2,500 messages (once every 10 minutes) the response to a 250ms packet was too slow. Using compression algorithms on the streams should allow handling even more concurrent users within such a cluster. The design does not preclude allowing the existence of multiple such clusters which would allow the system to scale-out even more.

Ultimately, this research has tremendous promise. By moving EEG analysis to the cloud, one can avoid the limitations many mobile BCI applications have.  Instead of having a single machine dedicated to a single user, one could have a cluster of hundreds of machines serving tens of thousands of users.  This approach has multiple benefits: First, by aggregating so much user data, one can have a much larger base to train the neural networks.  Secondly, one can leverage a group of experts approach – multiple smaller neural networks spread across a cloud can work together to produce an expert opinion.  Lastly, this also opens up possibilities for even more complex analysis with the Map-Reduce paradigm.  EEG streams can be analyzed not only over an immediate interval, but longer intervals of data (such as seconds or minutes) can be analyzed for trends. The Colorado State University team is exploring these research issues.

[1] K. Ericson, et al., “Analyzing Electroencephalograms Using Cloud Computing Techniques,” in IEEE  Conference on Cloud Computing Technology and Science, Indianopolis, USA, 2010.

[2] C. W. Anderson and J. A. Bratman, “Translating Thoughts into Actions by Finding Patterns in Brainwaves,” in Fourteenth Yale Workshop on Adaptive and Learning Systems, New Haven, CT, 2008, pp. 1-6.

[3] F. Galan, et al., “A brain-actuated wheelchair: Asynchronous and non-invasive Brain-computer interfaces for continuous control of robots,” Clinical Neurophysiology, vol. 119, pp. 2159-2169, 2008.

[4] S. Pallickara, et al., “Granules: A Lightweight, Streaming Runtime for Cloud Computing With Support for Map-Reduce,” in IEEE International Conference on Cluster Computing, New Orleans, LA., 2009.

[5] S. Pallickara, et al., “An Overview of the Granules Runtime for Cloud Computing,” in IEEE International Conference on e-Science, Indianapolis, 2008.
[6] T. White, Hadoop: The Definitive Guide, 1 ed.: O’Reilly Media, 2009.


Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

GlobalFoundries Puts Wind in AMD’s Sails with 12nm FinFET

September 24, 2017

From its annual tech conference last week (Sept. 13), where GlobalFoundries welcomed more than 600 semiconductor professionals (reaching the Santa Clara venue’s max capacity and doubling 2016 attendee numbers), the one Read more…

By Tiffany Trader

Machine Learning at HPC User Forum: Drilling into Specific Use Cases

September 22, 2017

The 66th HPC User Forum held September 5-7, in Milwaukee, Wisconsin, at the elegant and historic Pfister Hotel, highlighting the 1893 Victorian décor and art of “The Grand Hotel Of The West,” contrasted nicely with Read more…

By Arno Kolster

Google Cloud Makes Good on Promise to Add Nvidia P100 GPUs

September 21, 2017

Google has taken down the notice on its cloud platform website that says Nvidia Tesla P100s are “coming soon.” That's because the search giant has announced the beta launch of the high-end P100 Nvidia Tesla GPUs on t Read more…

By George Leopold

HPE Extreme Performance Solutions

HPE Prepares Customers for Success with the HPC Software Portfolio

High performance computing (HPC) software is key to harnessing the full power of HPC environments. Development and management tools enable IT departments to streamline installation and maintenance of their systems as well as create, optimize, and run their HPC applications. Read more…

Cray Wins $48M Supercomputer Contract from KISTI

September 21, 2017

It was a good day for Cray which won a $48 million contract from the Korea Institute of Science and Technology Information (KISTI) for a 128-rack CS500 cluster supercomputer. The new system, equipped with Intel Xeon Scal Read more…

By John Russell

GlobalFoundries Puts Wind in AMD’s Sails with 12nm FinFET

September 24, 2017

From its annual tech conference last week (Sept. 13), where GlobalFoundries welcomed more than 600 semiconductor professionals (reaching the Santa Clara venue Read more…

By Tiffany Trader

Machine Learning at HPC User Forum: Drilling into Specific Use Cases

September 22, 2017

The 66th HPC User Forum held September 5-7, in Milwaukee, Wisconsin, at the elegant and historic Pfister Hotel, highlighting the 1893 Victorian décor and art o Read more…

By Arno Kolster

Stanford University and UberCloud Achieve Breakthrough in Living Heart Simulations

September 21, 2017

Cardiac arrhythmia can be an undesirable and potentially lethal side effect of drugs. During this condition, the electrical activity of the heart turns chaotic, Read more…

By Wolfgang Gentzsch, UberCloud, and Francisco Sahli, Stanford University

PNNL’s Center for Advanced Tech Evaluation Seeks Wider HPC Community Ties

September 21, 2017

Two years ago the Department of Energy established the Center for Advanced Technology Evaluation (CENATE) at Pacific Northwest National Laboratory (PNNL). CENAT Read more…

By John Russell

Exascale Computing Project Names Doug Kothe as Director

September 20, 2017

The Department of Energy’s Exascale Computing Project (ECP) has named Doug Kothe as its new director effective October 1. He replaces Paul Messina, who is stepping down after two years to return to Argonne National Laboratory. Kothe is a 32-year veteran of DOE’s National Laboratory System. Read more…

Takeaways from the Milwaukee HPC User Forum

September 19, 2017

Milwaukee’s elegant Pfister Hotel hosted approximately 100 attendees for the 66th HPC User Forum (September 5-7, 2017). In the original home city of Pabst Blu Read more…

By Merle Giles

Kathy Yelick Charts the Promise and Progress of Exascale Science

September 15, 2017

On Friday, Sept. 8, Kathy Yelick of Lawrence Berkeley National Laboratory and the University of California, Berkeley, delivered the keynote address on “Breakthrough Science at the Exascale” at the ACM Europe Conference in Barcelona. In conjunction with her presentation, Yelick agreed to a short Q&A discussion with HPCwire. Read more…

By Tiffany Trader

DARPA Pledges Another $300 Million for Post-Moore’s Readiness

September 14, 2017

The Defense Advanced Research Projects Agency (DARPA) launched a giant funding effort to ensure the United States can sustain the pace of electronic innovation vital to both a flourishing economy and a secure military. Under the banner of the Electronics Resurgence Initiative (ERI), some $500-$800 million will be invested in post-Moore’s Law technologies. Read more…

By Tiffany Trader

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

NERSC Scales Scientific Deep Learning to 15 Petaflops

August 28, 2017

A collaborative effort between Intel, NERSC and Stanford has delivered the first 15-petaflops deep learning software running on HPC platforms and is, according Read more…

By Rob Farber

Oracle Layoffs Reportedly Hit SPARC and Solaris Hard

September 7, 2017

Oracle’s latest layoffs have many wondering if this is the end of the line for the SPARC processor and Solaris OS development. As reported by multiple sources Read more…

By John Russell

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

IBM Clears Path to 5nm with Silicon Nanosheets

June 5, 2017

Two years since announcing the industry’s first 7nm node test chip, IBM and its research alliance partners GlobalFoundries and Samsung have developed a proces Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Leading Solution Providers

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Google Releases Deeplearn.js to Further Democratize Machine Learning

August 17, 2017

Spreading the use of machine learning tools is one of the goals of Google’s PAIR (People + AI Research) initiative, which was introduced in early July. Last w Read more…

By John Russell

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

EU Funds 20 Million Euro ARM+FPGA Exascale Project

September 7, 2017

At the Barcelona Supercomputer Centre on Wednesday (Sept. 6), 16 partners gathered to launch the EuroEXA project, which invests €20 million over three-and-a-half years into exascale-focused research and development. Led by the Horizon 2020 program, EuroEXA picks up the banner of a triad of partner projects — ExaNeSt, EcoScale and ExaNoDe — building on their work... Read more…

By Tiffany Trader

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Amazon Debuts New AMD-based GPU Instances for Graphics Acceleration

September 12, 2017

Last week Amazon Web Services (AWS) streaming service, AppStream 2.0, introduced a new GPU instance called Graphics Design intended to accelerate graphics. The Read more…

By John Russell

Cray Moves to Acquire the Seagate ClusterStor Line

July 28, 2017

This week Cray announced that it is picking up Seagate's ClusterStor HPC storage array business for an undisclosed sum. "In short we're effectively transitioning the bulk of the ClusterStor product line to Cray," said CEO Peter Ungaro. Read more…

By Tiffany Trader

GlobalFoundries: 7nm Chips Coming in 2018, EUV in 2019

June 13, 2017

GlobalFoundries has formally announced that its 7nm technology is ready for customer engagement with product tape outs expected for the first half of 2018. The Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This