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)
- 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
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.
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.
- 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
- Hardware Constrained Neural Architechture Search
- Data Augmentation Techniques in Biosignal Classification
- BCI-controlled Drone
- Towards global Brain-Computer Interfaces
Epilepsy Seizure Detection Device
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.
- The SWEC-ETHZ iEEG Database and Algorithms
- Epilepsy monitoring and seizure forecasts at Wyss Center
- Controlling tinnitus with neurofeedback
- Data Augmentation Techniques in Biosignal Classification
- Compression of iEEG Data
- Contrastive Learning for Self-supervised Clustering of iEEG Data for Epileptic Patients
- Deep neural networks for seizure detection
Projects in Progress
These are projects that were recently completed:
- 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