no code implementations • 3 May 2024 • Jiawei Liao, Oscar Toomey, Xiaying Wang, Lars Widmer, Cynthia A. Chestek, Luca Benini, Taekwang Jang
In this paper, we propose a novel spiking neural network (SNN) decoder for regression tasks for implantable BMIs.
3 code implementations • 20 Feb 2024 • Jonathan Dan, Una Pale, Alireza Amirshahi, William Cappelletti, Thorir Mar Ingolfsson, Xiaying Wang, Andrea Cossettini, Adriano Bernini, Luca Benini, Sándor Beniczky, David Atienza, Philippe Ryvlin
Based on existing guidelines and recommendations, the framework introduces a set of recommendations and standards related to datasets, file formats, EEG data input content, seizure annotation input and output, cross-validation strategies, and performance metrics.
no code implementations • 14 Sep 2023 • Xiaying Wang, Lan Mei, Victor Kartsch, Andrea Cossettini, Luca Benini
The comfortable BMI setup with tiny CNN and TL paves the way to future on-device continual learning, essential for tackling inter-session variability and improving usability.
no code implementations • 28 Aug 2023 • Thorir Mar Ingolfsson, Upasana Chakraborty, Xiaying Wang, Sandor Beniczky, Pauline Ducouret, Simone Benatti, Philippe Ryvlin, Andrea Cossettini, Luca Benini
The EpiDeNet-SSWCE method demonstrates effective and accurate seizure detection performance on heavily imbalanced datasets, while being suited for implementation on energy-constrained platforms.
no code implementations • 13 Jan 2023 • Sizhen Bian, Xiaying Wang, Tommaso Polonelli, Michele Magno
We also introduced an open data set composed of fifty sessions of eleven gym workouts collected from ten subjects that is publicly available.
no code implementations • 1 Dec 2022 • Hanna Poikonen, Tomasz Zaluska, Xiaying Wang, Michele Magno, Manu Kapur
Our results clarify the different neural signature, analyzed by HFD, of math experts and novices during complex math and suggest machine learning as a promising data-driven approach to understand the brain processes in expertise and mathematical cognition.
1 code implementation • 7 Oct 2022 • Jiawei Liao, Lars Widmer, Xiaying Wang, Alfio Di Mauro, Samuel R. Nason-Tomaszewski, Cynthia A. Chestek, Luca Benini, Taekwang Jang
Brain-machine interfaces (BMIs) are promising for motor rehabilitation and mobility augmentation.
no code implementations • 29 Apr 2022 • Thorir Mar Ingolfsson, Mark Vero, Xiaying Wang, Lorenzo Lamberti, Luca Benini, Matteo Spallanzani
The computational demands of neural architecture search (NAS) algorithms are usually directly proportional to the size of their target search spaces.
no code implementations • 28 Mar 2022 • Xiaying Wang, Michael Hersche, Michele Magno, Luca Benini
A brain--machine interface (BMI) based on motor imagery (MI) enables the control of devices using brain signals while the subject imagines performing a movement.
no code implementations • 18 Dec 2021 • Xiaying Wang, Lukas Cavigelli, Tibor Schneider, Luca Benini
Motor imagery brain--machine interfaces enable us to control machines by merely thinking of performing a motor action.
no code implementations • 29 Sep 2021 • Rodolfo Octavio Siller Quintanilla, Xiaying Wang, Michael Hersche, Luca Benini, Gagandeep Singh
We propose new methods to induce denial-of-service attacks and incorporate domain-specific insights and constraints to accomplish two key goals: (i) create smooth adversarial attacks that are physiologically plausible; (ii) consider the realistic case where the attack happens at the origin of the signal acquisition and it propagates on the human head.
no code implementations • 15 Jun 2021 • Thorir Mar Ingolfsson, Andrea Cossettini, Xiaying Wang, Enrico Tabanelli, Giuseppe Tagliavini, Philippe Ryvlin, Luca Benini, Simone Benatti
We present the implementation of seizure detection algorithms based on a minimal number of EEG channels on a parallel ultra-low-power embedded platform.
1 code implementation • 25 Mar 2021 • Thorir Mar Ingolfsson, Xiaying Wang, Michael Hersche, Alessio Burrello, Lukas Cavigelli, Luca Benini
With 9. 91 GMAC/s/W, it is 23. 0 times more energy-efficient and 46. 85 times faster than an implementation on the ARM Cortex M4F (0. 43 GMAC/s/W).
1 code implementation • 22 Feb 2021 • Xiaying Wang, Tibor Schneider, Michael Hersche, Lukas Cavigelli, Luca Benini
With Motor-Imagery (MI) Brain--Machine Interfaces (BMIs) we may control machines by merely thinking of performing a motor action.
1 code implementation • 31 May 2020 • Thorir Mar Ingolfsson, Michael Hersche, Xiaying Wang, Nobuaki Kobayashi, Lukas Cavigelli, Luca Benini
Experimental results on the BCI Competition IV-2a dataset show that EEG-TCNet achieves 77. 35% classification accuracy in 4-class MI.
1 code implementation • 24 Apr 2020 • Tibor Schneider, Xiaying Wang, Michael Hersche, Lukas Cavigelli, Luca Benini
We quantize weights and activations to 8-bit fixed-point with a negligible accuracy loss of 0. 4% on 4-class MI, and present an energy-efficient hardware-aware implementation on the Mr. Wolf parallel ultra-low power (PULP) System-on-Chip (SoC) by utilizing its custom RISC-V ISA extensions and 8-core compute cluster.
no code implementations • 31 Mar 2020 • Xiaying Wang, Michael Hersche, Batuhan Tömekce, Burak Kaya, Michele Magno, Luca Benini
Our novel method further scales down the standard EEGNet at a negligible accuracy loss of 0. 31% with 7. 6x memory footprint reduction and a small accuracy loss of 2. 51% with 15x reduction.
no code implementations • 28 Feb 2020 • Michele Magno, Xiaying Wang, Manuel Eggimann, Lukas Cavigelli, Luca Benini
This work presents InfiniWolf, a novel multi-sensor smartwatch that can achieve self-sustainability exploiting thermal and solar energy harvesting, performing computationally high demanding tasks.
no code implementations • 10 Dec 2019 • Xiaying Wang, Lukas Cavigelli, Manuel Eggimann, Michele Magno, Luca Benini
Synthetic aperture radar (SAR) data is becoming increasingly available to a wide range of users through commercial service providers with resolutions reaching 0. 5m/px.
1 code implementation • 8 Nov 2019 • Xiaying Wang, Michele Magno, Lukas Cavigelli, Luca Benini
The growing number of low-power smart devices in the Internet of Things is coupled with the concept of "Edge Computing", that is moving some of the intelligence, especially machine learning, towards the edge of the network.