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Found 19 papers, 7 papers with code
SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms
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.
Enhancing Performance, Calibration Time and Efficiency in Brain-Machine Interfaces through Transfer Learning and Wearable EEG Technology
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.
EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems
The EpiDeNet-SSWCE method demonstrates effective and accurate seizure detection performance on heavily imbalanced datasets, while being suited for implementation on energy-constrained platforms.
Exploring Automatic Gym Workouts Recognition Locally On Wearable Resource-Constrained Devices
We also introduced an open data set composed of fifty sessions of eleven gym workouts collected from ten subjects that is publicly available.
Nonlinear and Machine Learning Analyses on High-Density EEG data of Math Experts and Novices
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.
An Energy-Efficient Spiking Neural Network for Finger Velocity Decoding for Implantable Brain-Machine Interface
Brain-machine interfaces (BMIs) are promising for motor rehabilitation and mobility augmentation.
Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering
The computational demands of neural architecture search (NAS) algorithms are usually directly proportional to the size of their target search spaces.
MI-BMInet: An Efficient Convolutional Neural Network for Motor Imagery Brain--Machine Interfaces with EEG Channel Selection
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.
Sub-100uW Multispectral Riemannian Classification for EEG-based Brain--Machine Interfaces
Motor imagery brain--machine interfaces enable us to control machines by merely thinking of performing a motor action.
Practical Adversarial Attacks on Brain--Computer Interfaces
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.
Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices
We present the implementation of seizure detection algorithms based on a minimal number of EEG channels on a parallel ultra-low-power embedded platform.
ECG-TCN: Wearable Cardiac Arrhythmia Detection with a Temporal Convolutional Network
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).
Mixed-Precision Quantization and Parallel Implementation of Multispectral Riemannian Classification for Brain--Machine Interfaces
With Motor-Imagery (MI) Brain--Machine Interfaces (BMIs) we may control machines by merely thinking of performing a motor action.
EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces
Experimental results on the BCI Competition IV-2a dataset show that EEG-TCNet achieves 77. 35% classification accuracy in 4-class MI.
Q-EEGNet: an Energy-Efficient 8-bit Quantized Parallel EEGNet Implementation for Edge Motor-Imagery Brain--Machine Interfaces
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.
An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing
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.
InfiniWolf: Energy Efficient Smart Bracelet for Edge Computing with Dual Source Energy Harvesting
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.
HR-SAR-Net: A Deep Neural Network for Urban Scene Segmentation from High-Resolution SAR Data
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.
FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things
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.
