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=Introduction=
+
[[File:HI.png|thumb|right|450px]]
[[File:HI.png|thumb|center]]
 
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 the brain—through our five sense modalities—forming two separate computing systems, while it could be made a lot more effective if a direct high bandwidth link existed between the two, 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, and artificial retinas). 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!
+
=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.
  
Here, in our projects, we mainly focus on '''sensor, computation, and storage''' aspects to develop very efficient closed-loop sense-interpret-actuate systems, enabling distributed autonomous behavior. More specifically, to design our ''physical brain'' (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 brain’s circuits: assuming 1 bit per synapse, they constitute more than 24 billion of such ultra-wide words. Overall, our projects cover '''algorithmic, hardware/software, and system level''' design and developments.
+
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:
 +
* [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://iis-people.ee.ethz.ch/~arahimi/papers/ISSCC18-Demo.pdf PDF]
 +
 +
You can also find a collection of complemented projects with source codes/datasets here:
 +
* [https://github.com/HyperdimensionalComputing/collection Github link]
 +
-->
 
==Prerequisites and Focus==
 
==Prerequisites and Focus==
If you are an M.S. student, typically there is no special prerequisite. We can redefine and adapt the project based on your skills.  
+
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:
<!-- However, if you have background in signal processing, VLSI or linear algebra is a super plus! -->
+
 
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])-->
 +
* '''Exploring new Human Intranet/IoT applications'''
 +
* '''Algorithm design and optimizations''' (Python)
 +
* '''System-level design and testing''' (Altium, C-programming)
 +
* '''Sensory interfaces''' (analog and digital)
 +
 
 +
 
 +
<!-- 
 
* '''Theory''' of learning systems including HD computing, Hidden Markov Model (HMM), and clustering algorithms
 
* '''Theory''' of learning systems including HD computing, Hidden Markov Model (HMM), and clustering algorithms
* Exploring various embedded/IoTs '''applications'''
+
Overall, our projects cover '''algorithmic, hardware/software, and system level''' design and developments.
* '''Algorithmic''' design and optimizations (Matlab/ Python)
+
However, if you have background in signal processing, VLSI or linear algebra is a super plus! -->
* '''Hardware and digital architecture''' design
 
* '''FPGA''' prototyping (SystemVerilog/ VHDL)
 
* '''ASIC chip and accelerators''' for low signal-to-noise ratio conditions
 
  
 
===Useful Reading===
 
===Useful Reading===
*[http://redwood.berkeley.edu/vs265/kanerva09-hyperdimensional.pdf Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors]
+
*[https://ieeexplore.ieee.org/document/7030200/ The Human Intranet--Where Swarms and Humans Meet]
*[http://people.eecs.berkeley.edu/~abbas/papers/TCAS17.pdf High-dimensional Computing as a Nanoscalable Paradigm]
+
*[https://ieeexplore.ieee.org/abstract/document/8490896 Efficient Biosignal Processing Using Hyperdimensional Computing: Network Templates for Combined Learning and Classification of ExG Signals]
*[https://mitpress.mit.edu/books/sparse-distributed-memory Pentti Kanerva. 1988. Sparse Distributed Memory. MIT Press, Cambridge, MA, USA]
+
*[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]
  
 
=Available Projects=
 
=Available Projects=
Here, we provide a tiny list of related projects just for your information. The new directions and details of the projects can be adapted based on your interests and skills. Specifically, you can also take a look at the list of publications [https://people.eecs.berkeley.edu/~abbas/Publications.html here] to find other active projects we are working on. Please do not hesitate to contact 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.
 +
 
 +
==Wearables for health and physiology==
 +
[[File:Cardiorespiratory.JPG|thumb|right|200px]]
 +
===Short Description===
 +
In this research area, we develop wearable systems, algorithms, and applications for monitoring health- and physiological-related parameters in innovative ways. Examples include (but are not limited to): heart rate and respiration rate monitoring, blood pressure monitoring, bladder monitoring, drowsiness detection, monitoring of muscle contractions and identification of innervations, ...
 +
 
 +
For wearables based on ultrasound, see also the dedicated [[Digital_Medical_Ultrasound_Imaging | '''Ultrasound section''']]
 +
 
 +
===Available Projects===
 +
<DynamicPageList>
 +
category = Available
 +
category = Digital
 +
category = WearablesHealth
 +
</DynamicPageList>
 +
 
 +
==Brain-Machine Interfaces and wearables==
 +
<!--  [[File:BCI.png|thumb|center]]
 +
[[File:BCI-dryEEG.jpg|thumb|right]] -->
 +
[[File:Emotiv-epoc-14-channel-mobile-eeg.jpg|thumb|right|200px]]
 +
[[File:In_ear_EEG.jpg|thumb|right|200px]]
  
  
==Extremely Resilient HD Processor==
 
[[File:BrainChip.jpg|thumb|center]]
 
 
===Short Description===
 
===Short Description===
The most important aspect of HD computing, for hardware realization, is its robustness against noise and variations in the computing platforms. Principles of HD computing allows to implement resilient controllers and state machines for extreme noisy conditions. Its tolerance in operating with faulty components and low signal-to-noise ratio (SNR) conditions is achieved by brain-inspired properties of hypervectors: (pseudo)randomness, high-dimensionality, and fully distributed holographic representations.
+
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. BMIs are a special kind of HMI, focused on the brain. What makes BMIs particularly challenging is their susceptibility to errors over time in the recognition of human intentions.
  
In this project, your goal would be to design and develop an end-to-end robust HD processor with extremely resilient controller based on principles of HD computing, and measure its resiliency against noisy environment and faulty components.
+
In these projects, 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.
 +
 
 +
*WATCH OUR DEMO: EEG-HEADBAND CONTROLLING A DRONE: https://www.youtube.com/watch?v=3-DysFptdRI
 +
 
 +
===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/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]
 +
 
 +
===Available Projects===
 +
<DynamicPageList>
 +
category = Available
 +
category = Digital
 +
category = BCI
 +
</DynamicPageList>
 +
 
 +
==Epilepsy Seizure Detection Device==
 +
[[File:Non-EEG Seizure.jpg|border|text-top|300px]]
 +
[[File:NeuroPace.jpg|border|text-top|400px]]
 +
<!-- Seizure-prediction.png -->
 +
===Short Description===
 +
Epilepsy is a brain disease that affects more than 50 million people worldwide. Conventional treatments are primarily pharmacological, but they can require surgery or invasive neurostimulation in the case of drug-resistant subjects. In these cases, personalized patient treatments are necessary and can be achieved with the help of long-term recording of brain activity. In this context, seizure detection systems hold promise for improving the quality of life for patients with epilepsy, providing non-stigmatizing and reliable continuous monitoring during real-life conditions. 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.
 +
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.
  
 
===Links===
 
===Links===
* [http://people.eecs.berkeley.edu/~abbas/papers/ISLPED16.pdf A Robust and Energy-Efficient Classifier Using Brain-Inspired Hyperdimensional Computing]
+
* [http://ieeg-swez.ethz.ch/ The SWEC-ETHZ iEEG Database and Algorithms]
* [http://people.eecs.berkeley.edu/~abbas/papers/DAC18.pdf PULP-HD: Accelerating Brain-Inspired High-Dimensional Computing on a Parallel Ultra-Low Power Platform]
+
* [https://www.wysscenter.ch/project/epilepsy-monitoring-seizure-forecasts/ Epilepsy monitoring and seizure forecasts at Wyss Center]
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8216554 Associative Synthesis of Finite State Automata Model of a Controlled Object with Hyperdimensional Computing]
+
* [https://www.youtube.com/watch?time_continue=87&v=ouyPXkEud40 Controlling tinnitus with neurofeedback]
 +
 
 +
 
 +
 
 +
===Available Projects===
 +
<DynamicPageList>
 +
category = Available
 +
category = Digital
 +
category = Epilepsy
 +
</DynamicPageList>
  
==Epilepsy Seizure Detection/Prediction==
+
==Foundation models and LLMs for Health==
[[File:Seizure-prediction.png|thumb|center]]
+
[[File:EEG_ECG.png|border|text-top|400px]]
 +
[[File:LLM.png|border|text-top|400px]]
 +
<!-- Seizure-prediction.png -->
 
===Short Description===
 
===Short Description===
Seizure detection/prediction systems hold promise for improving the quality of life for patients with epilepsy that afflicts nearly 1% of the world's population.
+
Incorporating Foundation Models and Large Language Models (LLMs) within artificial intelligence is gaining significant traction, particularly due to their potential applications in the health sector. This project is dedicated to developing sophisticated methodologies for utilizing foundation models and LLMs in health-related applications, specifically analyzing electroencephalogram (EEG) brain signals.
In this project, your goal would be to develop an efficient hardware for EEG signals to detect, or eventually predict an upcoming seizure in an ultra-low-power device. This project focuses on efficient techniques in analog front-end and digital signal processing. The abilities of HD computing for one-shot and online learning can be exploited as well.
+
 
 +
In healthcare and biomedical research, implementing advanced computational models, notably Foundation Models and Large Language Models (LLMs), revolutionizes the understanding and interpretation of intricate biosignals. We stand at the vanguard of this revolutionary change, delving into the capabilities of these models for the analysis and interpretation of critical biosignals, including electroencephalograms (EEG) and electrocardiograms (ECG).
  
 +
Foundation Models, encompassing a spectrum of robust, pre-trained models, are transforming our ability to process and interpret large datasets. Initially trained on extensive and diverse datasets, these models are adaptable for specific tasks, offering remarkable accuracy and efficiency. This adaptability renders them particularly beneficial for biosignal analysis, where the intricacies of EEG and ECG data demand both precision and contextual understanding.
 +
 +
As a subset of Foundation Models, LLMs have demonstrated efficacy in processing and generating human language. At IIS, we are pioneering the application of LLMs in the domain of biosignal interpretation, extending beyond textual data. This entails training the models to interpret the 'language' of biosignals, translating complex patterns into actionable insights.
 +
 +
Our emphasis on EEG and ECG signals is motivated by these biosignals' profound insights into human health. EEGs, capturing brain activity, and ECGs, monitoring heart rhythms, are instrumental in diagnosing and managing various health conditions. By leveraging Foundation Models and LLMs, our objective is to refine diagnostic accuracy, predict health outcomes, and customize patient care.
 +
 +
IIS invites Master's students to immerse themselves in this pioneering area. Our projects offer avenues to engage with state-of-the-art technologies, apply them to real-world health challenges, and contribute to shaping a future where healthcare is more predictive, preventive, and personalized. We encourage your participation in this exhilarating endeavor to redefine the confluence of healthcare and technology.
  
as well as non-EEG
 
 
===Links===
 
===Links===
* [https://www.kaggle.com/c/seizure-prediction American Epilepsy Society Seizure Prediction Challenge]
+
* [https://braingpt.org/ BrainGPT]
 +
 
 +
 
 +
===Available Projects===
 +
<DynamicPageList>
 +
category = Available
 +
category = Digital
 +
category = HealthGPT
 +
</DynamicPageList>
 +
 
 +
 +
 
 +
<!--
 +
=Extremely Resilient Hyperdimensional Processor=
 +
[[File:BrainChip.jpg|thumb|left]]
  
==Online Brain-Computer Interfaces==
 
[[File:BCI.png|thumb|center]]
 
 
===Short Description===
 
===Short Description===
Noninvasive brain–computer interfaces 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.
+
The most important aspect of hyperdimensional (HD) computing, for hardware realization, is its robustness against noise and variations in the computing platforms. Principles of HD computing allows to implement resilient controllers and state machines for extreme noisy conditions. Its tolerance in operating with faulty components and low signal-to-noise ratio (SNR) conditions is achieved by brain-inspired properties of hypervectors: (pseudo)randomness, high-dimensionality, and fully distributed holographic representations.
  
In this project, your goal would be to develop an efficient and fast learning method based on HD computing that replaces the traditional signal processing and classification methods by directly operating with raw data from electrodes in an online fashion.  
+
In this project, your goal would be to design and develop an end-to-end robust HD processor with extremely resilient controller based on principles of HD computing, and measure its resiliency against noisy environment and faulty components.
  
 
===Links===
 
===Links===
* [http://people.eecs.berkeley.edu/~abbas/papers/MONET17.pdf Hyperdimensional Computing for Blind and One-Shot Classification of EEG Error-Related Potentials (paper)]
+
* [https://iis-people.ee.ethz.ch/~arahimi/papers/ISLPED16.pdf A Robust and Energy-Efficient Classifier Using Brain-Inspired Hyperdimensional Computing]
* [https://github.com/abbas-rahimi/HDC-EEG-ERP Related Matlab code]  
+
* [https://iis-people.ee.ethz.ch/~arahimi/papers/DAC18.pdf PULP-HD: Accelerating Brain-Inspired High-Dimensional Computing on a Parallel Ultra-Low Power Platform]
+
* [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8216554 Associative Synthesis of Finite State Automata Model of a Controlled Object with Hyperdimensional Computing]
  
==Flexible High-Density EMG Hand Gesture Recognition==
+
=Flexible High-Density Sensors for Hand Gesture Recognition=
 
[[File:Hyperdimensional_EMG.png|thumb|center]]
 
[[File:Hyperdimensional_EMG.png|thumb|center]]
 +
[[File:FlexEMG.png|thumb|right|500px]]
 +
 
===Short Description===
 
===Short Description===
The surface EMG signals are the superposition of the electrical activity of underneath muscles when contractions occur.
+
The surface electromyography (EMG) signals are the superposition of the electrical activity of underneath muscles when contractions occur.
 
Wearable surface EMG devices have a wide range of applications in controlling the upper limb prostheses and hand gesture recognition systems intended for consumer human-machine interaction. High-density EMG electrode array covering the whole arm can ease targeting the most desired muscle locations and cope the issues with sensors misplacement.
 
Wearable surface EMG devices have a wide range of applications in controlling the upper limb prostheses and hand gesture recognition systems intended for consumer human-machine interaction. High-density EMG electrode array covering the whole arm can ease targeting the most desired muscle locations and cope the issues with sensors misplacement.
For robust gesture recognition from such EMG arrays, we rely on brain-inspired HD computing.
+
For robust gesture recognition from such EMG sensors, we rely on brain-inspired HD computing.
  
In this project, your goal would be to develop an RTL implementation of HD computing for one-shot gesture learning in an ultra low-power device.
+
In this project, your goal would be to develop new sensors and RTL implementation of HD computing for one-shot gesture learning in an ultra low-power device.
  
 
===Links===
 
===Links===
 
* [https://bwrc.eecs.berkeley.edu/sites/default/files/files/u2630/flexemg_v2_lq.mp4#t=2 Flexible EMG Demo]
 
* [https://bwrc.eecs.berkeley.edu/sites/default/files/files/u2630/flexemg_v2_lq.mp4#t=2 Flexible EMG Demo]
* [http://people.eecs.berkeley.edu/~abbas/papers/ICRC16.pdf Hyperdimensional Biosignal Processing: A Case Study for EMG-based Hand Gesture Recognition (paper)]
+
* [https://iis-people.ee.ethz.ch/~arahimi/papers/ISCAS2018.pdf Gesture Recognition System with Flexible High-Density Sensors]
* [https://github.com/abbas-rahimi/HDC-EMG Related Matlab code]  
+
* [https://iis-people.ee.ethz.ch/~arahimi/papers/papers/ICRC16.pdf Hyperdimensional Biosignal Processing: A Case Study for EMG-based Hand Gesture Recognition (paper)]
 
+
* [https://arxiv.org/abs/1901.00234 Adaptive EMG-based hand gesture recognition using hyperdimensional computing (paper)]
 +
* [https://github.com/abbas-rahimi/HDC-EMG Related Matlab code]
  
 
==Robot Learning by Demonstration==
 
==Robot Learning by Demonstration==
[[File:Robot-VSA.png|thumb|center|Image source: Neubert et al, IROS 2016]]
+
[[File:Robot-VSA.png|thumb|left|Image source: Neubert et al, IROS 2016]]
 
===Short Description===
 
===Short Description===
 
Robot learning from demonstration is a paradigm for enabling robots to autonomously perform new tasks.  
 
Robot learning from demonstration is a paradigm for enabling robots to autonomously perform new tasks.  
Line 89: Line 177:
 
* [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)]
 +
--->
  
=Other Available Projects=
+
= Projects in Progress=
 
<DynamicPageList>
 
<DynamicPageList>
category = Available
+
supresserrors = true
category = Digital
+
category = In progress
category = Hyperdimensional Computing
+
category = Human Intranet
suppresserrors=true
 
 
</DynamicPageList>
 
</DynamicPageList>
  
* Based on your interested, HD computing projects can be also combined with projects available in [http://iis-projects.ee.ethz.ch/index.php/Biomedical_System_on_Chips Biomedical System on Chips] as well as [http://iis-projects.ee.ethz.ch/index.php/Deep_Learning_Projects Deep Learning] pages.
+
=Completed Projects=
 
+
These are projects that were recently completed:
=Projects In Progress=
 
 
<DynamicPageList>
 
<DynamicPageList>
category = In progress
+
category = Completed
 
category = Digital
 
category = Digital
category = Hyperdimensional Computing
+
category = Human Intranet
suppresserrors=true
+
 
 
</DynamicPageList>
 
</DynamicPageList>
  
 
+
=Where to find us=
=Contact Information=
+
{|
* [https://people.eecs.berkeley.edu/~abbas/ Dr. Abbas Rahimi]
+
| style="padding: 10px" | [[File:Thorir.jpg|frameless|left|100px]]
** '''e-mail''': [mailto:abbas@ee.ethz.ch abbas@ee.ethz.ch]
+
| style="padding: 10px" | [[File:SebiFrey.jpg|frameless|left|100px]]
** '''e-mail''': [mailto:abbas@eecs.berkeley.edu abbas@eecs.berkeley.edu]
+
| style="padding: 10px" |
** ETZ J85
+
| style="padding: 10px" | [[File:Andrea_Cossettini.jpg|frameless|left|100px]]
* [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]
+
| [[:User:Thoriri | Thorir Mar Ingolfsson]]
** ETZ J84
+
| Sebastian Frey
 
+
| [[:User:Xiaywang | Dr. Xiaying Wang]]
 +
| [[:User:Cosandre | Dr. Andrea Cossettini]]
 +
|-
 +
| '''Office''': OAT U21
 +
| '''Office''': ETZ J69.2
 +
| '''Office''': OAT U24 / ETZ J68.2
 +
| '''Office''': OAT U27 / ETZ J69.2
 +
|-
 +
| '''e-mail''': [mailto:thoriri@iis.ee.ethz.ch thoriri@iis.ee.ethz.ch]
 +
| '''e-mail''': [mailto:sefrey@iis.ee.ethz.ch sefrey@iis.ee.ethz.ch]
 +
| '''e-mail''': [mailto:xiaywang@iis.ee.ethz.ch xiaywang@iis.ee.ethz.ch]
 +
| '''e-mail''': [mailto:cossettini.andrea@iis.ee.ethz.ch cossettini.andrea@iis.ee.ethz.ch]
 +
|}
  
  
  
<!--
+
[[Category:Digital]]
===Available Projects===
+
[[Category:Human Intranet]]
<DynamicPageList>
+
[[Category:ASIC]]
category = Available
+
[[Category:FPGA]]
category = Digital
+
[[Category:Semester Thesis]]
category = Hyperdimensional Computing
+
[[Category:Master Thesis]]
suppresserrors=true
 
</DynamicPageList>
 
 
 
 
 
===Completed Projects===
 
<DynamicPageList>
 
category = Completed
 
category = Digital
 
category = Hyperdimensional Computing
 
suppresserrors=true
 
</DynamicPageList>
 
 
 
-->
 

Latest revision as of 19:09, 10 March 2024

HI.png

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

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.

Wearables for health and physiology

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Short Description

In this research area, we develop wearable systems, algorithms, and applications for monitoring health- and physiological-related parameters in innovative ways. Examples include (but are not limited to): heart rate and respiration rate monitoring, blood pressure monitoring, bladder monitoring, drowsiness detection, monitoring of muscle contractions and identification of innervations, ...

For wearables based on ultrasound, see also the dedicated Ultrasound section

Available Projects


Brain-Machine Interfaces and wearables

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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. BMIs are a special kind of HMI, focused on the brain. What makes BMIs particularly challenging is their susceptibility to errors over time in the recognition of human intentions.

In these projects, 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

Available Projects


Epilepsy Seizure Detection Device

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Short Description

Epilepsy is a brain disease that affects more than 50 million people worldwide. Conventional treatments are primarily pharmacological, but they can require surgery or invasive neurostimulation in the case of drug-resistant subjects. In these cases, personalized patient treatments are necessary and can be achieved with the help of long-term recording of brain activity. In this context, seizure detection systems hold promise for improving the quality of life for patients with epilepsy, providing non-stigmatizing and reliable continuous monitoring during real-life conditions. 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. 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.

Links


Available Projects


Foundation models and LLMs for Health

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Short Description

Incorporating Foundation Models and Large Language Models (LLMs) within artificial intelligence is gaining significant traction, particularly due to their potential applications in the health sector. This project is dedicated to developing sophisticated methodologies for utilizing foundation models and LLMs in health-related applications, specifically analyzing electroencephalogram (EEG) brain signals.

In healthcare and biomedical research, implementing advanced computational models, notably Foundation Models and Large Language Models (LLMs), revolutionizes the understanding and interpretation of intricate biosignals. We stand at the vanguard of this revolutionary change, delving into the capabilities of these models for the analysis and interpretation of critical biosignals, including electroencephalograms (EEG) and electrocardiograms (ECG).

Foundation Models, encompassing a spectrum of robust, pre-trained models, are transforming our ability to process and interpret large datasets. Initially trained on extensive and diverse datasets, these models are adaptable for specific tasks, offering remarkable accuracy and efficiency. This adaptability renders them particularly beneficial for biosignal analysis, where the intricacies of EEG and ECG data demand both precision and contextual understanding.

As a subset of Foundation Models, LLMs have demonstrated efficacy in processing and generating human language. At IIS, we are pioneering the application of LLMs in the domain of biosignal interpretation, extending beyond textual data. This entails training the models to interpret the 'language' of biosignals, translating complex patterns into actionable insights.

Our emphasis on EEG and ECG signals is motivated by these biosignals' profound insights into human health. EEGs, capturing brain activity, and ECGs, monitoring heart rhythms, are instrumental in diagnosing and managing various health conditions. By leveraging Foundation Models and LLMs, our objective is to refine diagnostic accuracy, predict health outcomes, and customize patient care.

IIS invites Master's students to immerse themselves in this pioneering area. Our projects offer avenues to engage with state-of-the-art technologies, apply them to real-world health challenges, and contribute to shaping a future where healthcare is more predictive, preventive, and personalized. We encourage your participation in this exhilarating endeavor to redefine the confluence of healthcare and technology.

Links


Available Projects



Projects in Progress


Completed Projects

These are projects that were recently completed:


Where to find us

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Thorir Mar Ingolfsson Sebastian Frey Dr. Xiaying Wang Dr. Andrea Cossettini
Office: OAT U21 Office: ETZ J69.2 Office: OAT U24 / ETZ J68.2 Office: OAT U27 / ETZ J69.2
e-mail: thoriri@iis.ee.ethz.ch e-mail: sefrey@iis.ee.ethz.ch e-mail: xiaywang@iis.ee.ethz.ch e-mail: cossettini.andrea@iis.ee.ethz.ch