Difference between revisions of "Human Intranet"
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Human Intranet is an open, scalable platform that seamlessly integrates an ever-increasing number of sensor, actuation, computation, storage, communication and energy nodes located on, in, or around the human body acting in symbiosis with the functions provided by the body itself. Human Intranet presents a system vision in which, for example, disease would be treated by chronically measuring biosignals deep in the body, or by providing targeted, therapeutic interventions that respond on demand and in situ. To gain a holistic view of a person’s health, these sensors and actuators must communicate and collaborate with each other. Most of such systems prototyped or envisioned today serve to address deficiencies in the human sensory or motor control systems due to birth defects, illnesses, or accidents (e.g., invasive brain-machine interfaces, cochlear implants, artificial retinas, etc.). While all these systems target defects, one can easily imagine that this could lead to many types of enhancement and/or enable direct interaction with the environment: to make us humans smarter! | Human Intranet is an open, scalable platform that seamlessly integrates an ever-increasing number of sensor, actuation, computation, storage, communication and energy nodes located on, in, or around the human body acting in symbiosis with the functions provided by the body itself. Human Intranet presents a system vision in which, for example, disease would be treated by chronically measuring biosignals deep in the body, or by providing targeted, therapeutic interventions that respond on demand and in situ. To gain a holistic view of a person’s health, these sensors and actuators must communicate and collaborate with each other. Most of such systems prototyped or envisioned today serve to address deficiencies in the human sensory or motor control systems due to birth defects, illnesses, or accidents (e.g., invasive brain-machine interfaces, cochlear implants, artificial retinas, etc.). While all these systems target defects, one can easily imagine that this could lead to many types of enhancement and/or enable direct interaction with the environment: to make us humans smarter! | ||
− | Here, in our projects, we mainly focus on '''sensor, computation, communication, and emerging storage''' aspects to develop very efficient closed-loop sense-interpret-actuate systems, enabling distributed autonomous behavior. For example, to design the ''brain'' of our physical computing (i.e., the compute/interpret component), we rely on computing with ultra-wide words (e.g., 10,000 bits) that eases interfacing with various sensor modalities and actuators. This novel computing paradigm is called hyperdimensional (HD) computing that is inspired from the very size of the biological brain’s circuits: assuming 1 bit per synapse, the brain is made up of more than 24 billion of such ultra-wide words. You can watch some of our demos: | + | Here, in our projects, we mainly focus on '''sensor, computation, communication, and emerging storage''' aspects to develop very efficient closed-loop sense-interpret-actuate systems, enabling distributed autonomous behavior. |
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+ | <!--For example, to design the ''brain'' of our physical computing (i.e., the compute/interpret component), we rely on computing with ultra-wide words (e.g., 10,000 bits) that eases interfacing with various sensor modalities and actuators. This novel computing paradigm is called hyperdimensional (HD) computing that is inspired from the very size of the biological brain’s circuits: assuming 1 bit per synapse, the brain is made up of more than 24 billion of such ultra-wide words. You can watch some of our demos: | ||
* [https://bwrc.eecs.berkeley.edu/sites/default/files/files/u2630/flexemg_v2_lq.mp4#t=2 Video1] | * [https://bwrc.eecs.berkeley.edu/sites/default/files/files/u2630/flexemg_v2_lq.mp4#t=2 Video1] | ||
* [https://www.youtube.com/watch?time_continue=9&v=vTQGMQ6QaJE Video2] | * [https://www.youtube.com/watch?time_continue=9&v=vTQGMQ6QaJE Video2] | ||
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You can also find a collection of complemented projects with source codes/datasets here: | You can also find a collection of complemented projects with source codes/datasets here: | ||
* [https://github.com/HyperdimensionalComputing/collection Github link] | * [https://github.com/HyperdimensionalComputing/collection Github link] | ||
− | + | --> | |
==Prerequisites and Focus== | ==Prerequisites and Focus== | ||
If you are an B.S. or M.S. student at the ETHZ, typically there is no prerequisite. You can come and talk to us and we adapt the projects based on your skills. The scope and focus of projects are wide. You can choose to work on: | If you are an B.S. or M.S. student at the ETHZ, typically there is no prerequisite. You can come and talk to us and we adapt the projects based on your skills. The scope and focus of projects are wide. You can choose to work on: | ||
− | * '''Efficient hardware architectures in emerging technologies''' (e.g., [https://www.zurich.ibm.com/sto/memory/ the IBM computational memory]) | + | <!-- * '''Efficient hardware architectures in emerging technologies''' (e.g., [https://www.zurich.ibm.com/sto/memory/ the IBM computational memory])--> |
− | * '''System-level design and testing''' | + | * '''Exploring new Human Intranet/IoT applications''' |
+ | * '''Algorithm design and optimizations''' (Python) | ||
+ | * '''System-level design and testing''' (Altium, C-programming) | ||
* '''Sensory interfaces''' (analog and digital) | * '''Sensory interfaces''' (analog and digital) | ||
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===Useful Reading=== | ===Useful Reading=== | ||
*[https://ieeexplore.ieee.org/document/7030200/ The Human Intranet--Where Swarms and Humans Meet] | *[https://ieeexplore.ieee.org/document/7030200/ The Human Intranet--Where Swarms and Humans Meet] | ||
− | + | *[https://ieeexplore.ieee.org/abstract/document/8490896 Efficient Biosignal Processing Using Hyperdimensional Computing: Network Templates for Combined Learning and Classification of ExG Signals] | |
− | *[https://ieeexplore.ieee.org/document/ | + | *[https://iopscience.iop.org/article/10.1088/1741-2552/aab2f2/meta A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update] |
− | *[https:// | ||
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=Available Projects= | =Available Projects= | ||
Here, we provide a short description of the related projects for you to see the scope of our work. The directions and details of the projects can be adapted based on your interests and skills. Please do not hesitate to come and talk to us for more details. | Here, we provide a short description of the related projects for you to see the scope of our work. The directions and details of the projects can be adapted based on your interests and skills. Please do not hesitate to come and talk to us for more details. | ||
− | = | + | =Brain-Machine Interfaces= |
<!-- [[File:BCI.png|thumb|center]] | <!-- [[File:BCI.png|thumb|center]] | ||
[[File:BCI-dryEEG.jpg|thumb|right]] --> | [[File:BCI-dryEEG.jpg|thumb|right]] --> | ||
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===Short Description=== | ===Short Description=== | ||
− | Noninvasive | + | Noninvasive brain–machine interfaces (BMIs) and neuroprostheses aim to provide a communication and control channel based on the recognition of the subject’s intentions from spatiotemporal neural activity typically recorded by EEG electrodes. What makes it particularly challenging, however, is its susceptibility to errors over time in the recognition of human intentions. |
− | In this project, | + | In this project, our goal is to develop efficient and fast learning algorithms that replace traditional signal processing and classification methods by directly operating with raw data from electrodes. Furthermore, we aim to efficiently deploy those algorithms on tightly resource-limited devices (e.g., Microcontroller units) for near sensor classification using artificial intelligence. |
===Links=== | ===Links=== | ||
+ | * [https://iis-people.ee.ethz.ch/~herschmi/EdgeDL20.pdf Q-EEGNet: an Energy-Efficient 8-bit Quantized Parallel EEGNet Implementation for Edge Motor-Imagery Brain–Machine Interfaces] | ||
+ | * [https://iis-people.ee.ethz.ch/~herschmi/MEMEA20.pdf An Accurate EEGNet-based Motor-Imagery Brain–Computer Interface for Low-Power Edge Computing] | ||
* [https://iis-people.ee.ethz.ch/~arahimi/papers/EUSIPCO18.pdf Fast and Accurate Multiclass Inference for Motor Imagery BCIs Using Large Multiscale Temporal and Spectral Features] | * [https://iis-people.ee.ethz.ch/~arahimi/papers/EUSIPCO18.pdf Fast and Accurate Multiclass Inference for Motor Imagery BCIs Using Large Multiscale Temporal and Spectral Features] | ||
* [https://iis-people.ee.ethz.ch/~arahimi/papers/MONET17.pdf Hyperdimensional Computing for Blind and One-Shot Classification of EEG Error-Related Potentials] | * [https://iis-people.ee.ethz.ch/~arahimi/papers/MONET17.pdf Hyperdimensional Computing for Blind and One-Shot Classification of EEG Error-Related Potentials] | ||
* [https://arxiv.org/abs/1812.05705 Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer Interfaces] | * [https://arxiv.org/abs/1812.05705 Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer Interfaces] | ||
− | + | ===Available Projects=== | |
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===Short Description=== | ===Short Description=== | ||
Seizure detection systems hold promise for improving the quality of life for patients with epilepsy that afflicts nearly 1% of the world's population. | Seizure detection systems hold promise for improving the quality of life for patients with epilepsy that afflicts nearly 1% of the world's population. | ||
− | In this project, | + | In this project, our goal is to develop efficient techniques for EEG as well as non-EEG signals to detect an upcoming seizure in an ultra-low-power device. This covers a wide range of analog and digital techniques. The abilities of hyperdimensional computing for one-shot and online learning can come to rescue. |
===Links=== | ===Links=== | ||
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* [https://www.youtube.com/watch?time_continue=87&v=ouyPXkEud40 Controlling tinnitus with neurofeedback] | * [https://www.youtube.com/watch?time_continue=87&v=ouyPXkEud40 Controlling tinnitus with neurofeedback] | ||
− | === | + | ===Available Projects=== |
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category = Available | category = Available | ||
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+ | <!-- | ||
=Extremely Resilient Hyperdimensional Processor= | =Extremely Resilient Hyperdimensional Processor= | ||
[[File:BrainChip.jpg|thumb|left]] | [[File:BrainChip.jpg|thumb|left]] | ||
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=Flexible High-Density Sensors for Hand Gesture Recognition= | =Flexible High-Density Sensors for Hand Gesture Recognition= | ||
− | + | [[File:Hyperdimensional_EMG.png|thumb|center]] | |
[[File:FlexEMG.png|thumb|right|500px]] | [[File:FlexEMG.png|thumb|right|500px]] | ||
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===Short Description=== | ===Short Description=== | ||
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* [https://www.tu-chemnitz.de/etit/proaut/publications/IROS2016_neubert.pdf Learning Vector Symbolic Architectures for Reactive Robot Behaviours] | * [https://www.tu-chemnitz.de/etit/proaut/publications/IROS2016_neubert.pdf Learning Vector Symbolic Architectures for Reactive Robot Behaviours] | ||
* [https://www.aaai.org/ocs/index.php/WS/AAAIW13/paper/download/7075/6578 Learning Behavior Hierarchies via High-Dimensional Sensor Projection (paper)] | * [https://www.aaai.org/ocs/index.php/WS/AAAIW13/paper/download/7075/6578 Learning Behavior Hierarchies via High-Dimensional Sensor Projection (paper)] | ||
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category = Human Intranet | category = Human Intranet | ||
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** '''e-mail''': [mailto:xiaywang@iis.ee.ethz.ch xiaywang@iis.ee.ethz.ch] | ** '''e-mail''': [mailto:xiaywang@iis.ee.ethz.ch xiaywang@iis.ee.ethz.ch] | ||
** ETZ J68.2 | ** ETZ J68.2 | ||
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* [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Prof. Luca Benini] | * [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Prof. Luca Benini] | ||
** '''e-mail''': [mailto:lbenini@iis.ee.ethz.ch lbenini@iis.ee.ethz.ch] | ** '''e-mail''': [mailto:lbenini@iis.ee.ethz.ch lbenini@iis.ee.ethz.ch] | ||
** ETZ J84 | ** ETZ J84 |
Revision as of 13:01, 12 November 2020
What is Human Intranet?
The world around us is getting a lot smarter quickly: virtually every single component of our daily living environment is being equipped with sensors, actuators, processing, and connection into a network that will soon count billions of nodes and trillions of sensors. These devices only interact with the human through the traditional input and output channels. Hence, they only indirectly communicate with our brain—through our five sense modalities—forming two separate computing systems: biological versus physical. It could be made a lot more effective if a direct high bandwidth link existed between the two systems, allowing them to truly collaborate with each other and to offer opportunities for enhanced functionality that would otherwise be hard to accomplish. The emergence of miniaturized sense, compute and actuate devices as well as interfaces that are form-fitted to the human body opens the door for a symbiotic convergence between biological function and physical computing.
Human Intranet is an open, scalable platform that seamlessly integrates an ever-increasing number of sensor, actuation, computation, storage, communication and energy nodes located on, in, or around the human body acting in symbiosis with the functions provided by the body itself. Human Intranet presents a system vision in which, for example, disease would be treated by chronically measuring biosignals deep in the body, or by providing targeted, therapeutic interventions that respond on demand and in situ. To gain a holistic view of a person’s health, these sensors and actuators must communicate and collaborate with each other. Most of such systems prototyped or envisioned today serve to address deficiencies in the human sensory or motor control systems due to birth defects, illnesses, or accidents (e.g., invasive brain-machine interfaces, cochlear implants, artificial retinas, etc.). While all these systems target defects, one can easily imagine that this could lead to many types of enhancement and/or enable direct interaction with the environment: to make us humans smarter!
Here, in our projects, we mainly focus on sensor, computation, communication, and emerging storage aspects to develop very efficient closed-loop sense-interpret-actuate systems, enabling distributed autonomous behavior.
Prerequisites and Focus
If you are an B.S. or M.S. student at the ETHZ, typically there is no prerequisite. You can come and talk to us and we adapt the projects based on your skills. The scope and focus of projects are wide. You can choose to work on:
- Exploring new Human Intranet/IoT applications
- Algorithm design and optimizations (Python)
- System-level design and testing (Altium, C-programming)
- Sensory interfaces (analog and digital)
Useful Reading
- The Human Intranet--Where Swarms and Humans Meet
- Efficient Biosignal Processing Using Hyperdimensional Computing: Network Templates for Combined Learning and Classification of ExG Signals
- A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update
Available Projects
Here, we provide a short description of the related projects for you to see the scope of our work. The directions and details of the projects can be adapted based on your interests and skills. Please do not hesitate to come and talk to us for more details.
Brain-Machine Interfaces
Short Description
Noninvasive brain–machine interfaces (BMIs) and neuroprostheses aim to provide a communication and control channel based on the recognition of the subject’s intentions from spatiotemporal neural activity typically recorded by EEG electrodes. What makes it particularly challenging, however, is its susceptibility to errors over time in the recognition of human intentions.
In this project, our goal is to develop efficient and fast learning algorithms that replace traditional signal processing and classification methods by directly operating with raw data from electrodes. Furthermore, we aim to efficiently deploy those algorithms on tightly resource-limited devices (e.g., Microcontroller units) for near sensor classification using artificial intelligence.
Links
- Q-EEGNet: an Energy-Efficient 8-bit Quantized Parallel EEGNet Implementation for Edge Motor-Imagery Brain–Machine Interfaces
- An Accurate EEGNet-based Motor-Imagery Brain–Computer Interface for Low-Power Edge Computing
- Fast and Accurate Multiclass Inference for Motor Imagery BCIs Using Large Multiscale Temporal and Spectral Features
- Hyperdimensional Computing for Blind and One-Shot Classification of EEG Error-Related Potentials
- Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer Interfaces
Available Projects
- Exploratory Development of a Unified Foundational Model for Multi Biosignal Analysis
- Deep Learning Based Anomaly Detection in ECG Signals Using Foundation Models
- Pretraining Foundational Models for EEG Signal Analysis Using Open Source Large Scale Datasets
- EEG-based drowsiness detection
- In-ear EEG signal acquisition
- EEG earbud
- Advanced EEG glasses
- Predict eye movement through brain activity
- BCI-controlled Drone
Epilepsy Seizure Detection Device
Short Description
Seizure detection systems hold promise for improving the quality of life for patients with epilepsy that afflicts nearly 1% of the world's population. In this project, our goal is to develop efficient techniques for EEG as well as non-EEG signals to detect an upcoming seizure in an ultra-low-power device. This covers a wide range of analog and digital techniques. The abilities of hyperdimensional computing for one-shot and online learning can come to rescue.
Links
- The SWEC-ETHZ iEEG Database and Algorithms
- Epilepsy monitoring and seizure forecasts at Wyss Center
- Controlling tinnitus with neurofeedback
Available Projects
- Advanced EEG glasses
- Self Aware Epilepsy Monitoring
- EEG artifact detection with machine learning
- EEG artifact detection for epilepsy monitoring
Projects in Progress
No pages meet these criteria.
Completed Projects
These are projects that were recently completed:
- Ultrasound-EMG combined hand gesture recognition
- Smart e-glasses for concealed recording of EEG signals
- Wireless EEG Acquisition and Processing
- Ultrasound based hand gesture recognition
- Design of combined Ultrasound and Electromyography systems
- Ultra low power wearable ultrasound probe
- Hardware Constrained Neural Architechture Search
- Memory Augmented Neural Networks in Brain-Computer Interfaces
- Low Latency Brain-Machine Interfaces
- Deep Convolutional Autoencoder for iEEG Signals
- TCNs vs. LSTMs for Embedded Platforms
- An Energy Efficient Brain-Computer Interface using Mr.Wolf
- Exploring Algorithms for Early Seizure Detection
- Improving Resiliency of Hyperdimensional Computing
- Toward Superposition of Brain-Computer Interface Models
- FPGA Optimizations of Dense Binary Hyperdimensional Computing
- Fast and Accurate Multiclass Inference for Brain–Computer Interfaces
Where to find us
- Michael Hersche
- e-mail: herschmi@iis.ee.ethz.ch
- ETZ J76.2
- Xiaying Wang
- e-mail: xiaywang@iis.ee.ethz.ch
- ETZ J68.2
- Prof. Luca Benini
- e-mail: lbenini@iis.ee.ethz.ch
- ETZ J84