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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
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portfolio
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publications
Rat Cortical Layers Classification Extracting Evoked Local Field Potential Images with Implanted Multi-Electrode Sensor
Xiaying Wang, Michele Magno, Lukas Cavigelli, Mufti Mahmud, Claudia Cecchetto, Stefano Vassanelli, Luca Benini
Published in 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), 2018
Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array
Xiaying Wang, Michele Magno, Lukas Cavigelli, Mufti Mahmud, Claudia Cecchetto, Stefano Vassanelli, Luca Benini
Published in IEEE Biomedical Circuits and Systems Conference (BioCAS), 2018
FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things
Xiaying Wang, Michele Magno, Lukas Cavigelli, Luca Benini
Published in IEEE Internet of Things Journal, 2020
HR-SAR-Net: A Deep Neural Network for Urban Scene Segmentation from High-Resolution SAR Data
Xiaying Wang, Lukas Cavigelli, Manuel Eggimann, Michele Magno, Luca Benini
Published in IEEE Sensors Applications Symposium (SAS), 2020
InfiniWolf: Energy Efficient Smart Bracelet for Edge Computing with Dual Source Energy Harvesting
Michele Magno, Xiaying Wang, Manuel Eggimann, Lukas Cavigelli, Luca Benini
Published in IEEE Design, Automation & Test in Europe Conference & Exhibition (DATE), 2020
An Accurate EEGNet-based Motor-Imagery Brain–Computer Interface for Low-Power Edge Computing
Xiaying Wang, Michael Hersche, Batuhan Tömekce, Burak Kaya, Michele Magno, Luca Benini
Published in IEEE international symposium on medical measurements and applications (MeMeA), 2020
Q-EEGNet: An Energy-Efficient 8-bit Quantized Parallel EEGNet Implementation for Edge Motor-Imagery Brain–Machine Interfaces
Tibor Schneider, Xiaying Wang, Michael Hersche, Lukas Cavigelli, Luca Benini
Published in IEEE International Conference on Smart Computing (SMARTCOMP), 2020
EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain–-Machine Interfaces
Thorir Mar Ingolfsson, Michael Hersche, Xiaying Wang, Nobuaki Kobayashi, Lukas Cavigelli, Luca Benini
Published in IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020
Mixed-Precision Quantization and Parallel Implementation of Multispectral Riemannian Classification for Brain–Machine Interfaces
Xiaying Wang, Tibor Schneider, Michael Hersche, Lukas Cavigelli, Luca Benini
Published in IEEE International Symposium on Circuits and Systems (ISCAS), 2021
ECG-TCN: Wearable Cardiac Arrhythmia Detection with a Temporal Convolutional Network
Thorir Mar Ingolfsson, Xiaying Wang, Michael Hersche, Alessio Burrello, Lukas Cavigelli, Luca Benini
Published in IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2021
SmartHand: Towards Embedded Smart Hands for Prosthetic and Robotic Applications
Xiaying Wang, Fabian Geiger, Vlad Niculescu, Michele Magno, Luca Benini
Published in IEEE Sensors Applications Symposium (SAS), 2021
Towards Long-Term Non-Invasive Monitoring for Epilepsy via Wearable EEG Devices
Thorir Mar Ingolfsson, Andrea Cossettini, Xiaying Wang, Enrico Tabanelli, Giuseppe Tagliavini, Philippe Ryvlin, Luca Benini, Simone Benatti
Published in IEEE Biomedical Circuits and Systems Conference (BioCAS), 2021
Sub-100uW Multispectral Riemannian Classification for EEG-based Brain–Machine Interfaces
Xiaying Wang, Lukas Cavigelli, Tibor Schneider, Luca Benini
Published in IEEE Transactions on Biomedical Circuits and Systems, 2021
Leveraging Tactile Sensors for Low Latency Embedded Smart Hands for Prosthetic and Robotic Applications
Xiaying Wang, Fabian Geiger, Vlad Niculescu, Michele Magno, Luca Benini
Published in IEEE Transactions on Instrumentation and Measurement, 2022
Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering
Thorir Mar Ingolfsson, Mark Vero, Xiaying Wang, Lorenzo Lamberti, Luca Benini, Matteo Spallanzani
Published in Proceedings of the 19th ACM International Conference on Computing Frontiers, 2022
An Energy-Efficient Spiking Neural Network for Finger Velocity Decoding for Implantable Brain-Machine Interface
Jiawei Liao, Lars Widmer, Xiaying Wang, Alfio Di Mauro, Samuel R Nason-Tomaszewski, Cynthia A Chestek, Luca Benini, Taekwang Jang
Published in IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2022
Exploring Automatic Gym Workouts Recognition Locally on Wearable Resource-Constrained Devices
Sizhen Bian, Xiaying Wang, Tommaso Polonelli, Michele Magno
Published in IEEE 13th International Green and Sustainable Computing Conference (IGSC), 2022
Physically-Constrained Adversarial Attacks on Brain-Machine Interfaces
Xiaying Wang, Rodolfo Octavio Siller Quintanilla, Michael Hersche, Luca Benini, Gagandeep Singh
Published in Workshop on Trustworthy and Socially Responsible Machine Learning at NeurIPS 2022, 2022
Minimizing artifact-induced false-alarms for seizure detection in wearable EEG devices with gradient-boosted tree classifiers
Thorir Mar Ingolfsson, Simone Benatti, Xiaying Wang, Adriano Bernini, Pauline Ducouret, Philippe Ryvlin, Sandor Beniczky, Luca Benini, Andrea Cossettini
Published in Nature Scientific Reports, 2023
Nonlinear and machine learning analyses on high-density EEG data of math experts and novices
Hanna Poikonen, Tomasz Zaluska, Xiaying Wang, Michele Magno, Manu Kapur
Published in Nature Scientific Reports, 2023
Enhancing Performance, Calibration Time and Efficiency in Brain-Machine Interfaces through Transfer Learning and Wearable EEG Technology
Xiaying Wang, Lan Mei, Victor Kartsch, Andrea Cossettini, Luca Benini
Published in 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2023
EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems
Thorir Mar Ingolfsson, Upasana Chakraborty, Xiaying Wang, Sandor Beniczky, Pauline Ducouret, Simone Benatti, Philippe Ryvlin, Andrea Cossettini, Luca Benini
Published in 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2023
MI-BMInet: An Efficient Convolutional Neural Network for Motor Imagery Brain–Machine Interfaces With EEG Channel Selection
Xiaying Wang, Michael Hersche, Michele Magno, Luca Benini
Published in IEEE Sensors Journal, 2024
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Biological Methods for Engineers (Basic Lab)
Graduate course, Biomedical Engineering, ETH Zurich, 2017
Teaching and lab assistant at Laboratory of Biosensors and Bioelectronics from Sep 2017 to Jun 2018.
This course teaches basic techniques of cell and molecular biology including basic laboratory skills and safety, cell culture, protein analysis, RNA/DNA Isolation and RT-PCR.
Neuromorphic Engineering I
Graduate course, Institute of Neuroinformatics, UZH and ETH Zurich, 2017
Teaching and lab assistant at Institute of Neuroinformatics from Sep 2017 to Feb 2018.
This course covers analog circuits with emphasis on neuromorphic engineering: MOS transistors in CMOS technology, static circuits, dynamic circuits, systems (silicon neuron, silicon retina, silicon cochlea) with an introduction to multi-chip systems. The lectures are accompanied by weekly laboratory sessions.
Cell and Molecular Biology for Engineers I
Graduate course, Biomedical Engineering, ETH Zurich, 2017
Three-hour lecture on imaging techniques for visualizing cells on Nov 16, 2017.
Machine Learning on Microcontrollers
Graduate course, Electrical Engineering, ETH Zurich, 2019
Course coordinator and assistant at Integrated Systems Laboratory from Feb 2019 to Jun 2020.
I contributed to the creation of slides for two lectures and led eight lab exercises.
This course gives an overview of machine learning methods and algorithms to process and extract useful near-sensor information in end-nodes of the Internet-of-things. We use low-power microcontrollers/processors based on ARM Cortex-M and RISC-V.
Projects & Seminars: Machine Learning for Brain-Computer Interfaces
Undergraduate and graduate practical course, Electrical Engineering, ETH Zurich, 2020
Course co-responsible and lecturer at Integrated Systems Laboratory from 2020 to 2022.
This course was created by Michael Hersche and I in 2020. We created the lecture slides and designed the lab exercises. I then led the course until 2022.
This course teaches signal processing and machine learning techniques applied to brain-computer interfaces by having hands-on experience in brain signal acquisition, data processing, feature extraction, and machine learning.