39 citations · 84 across the 23 of their papers we have counts for
4 papers · 1 filter
Mixed-Precision Quantization and Parallel Implementation of Multispectral Riemannian Classification for Brain--Machine Interfaces
Xiaying Wang, Tibor Schneider, Michael Hersche +2
With Motor-Imagery (MI) Brain--Machine Interfaces (BMIs) we may control machines by merely thinking of performing a motor action. Practical use cases require a wearable solution wh…
EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces
Thorir Mar Ingolfsson, Michael Hersche, Xiaying Wang +3
In recent years, deep learning (DL) has contributed significantly to the improvement of motor-imagery brain-machine interfaces (MI-BMIs) based on electroencephalography(EEG). While…
ChewBaccaNN: A Flexible 223 TOPS/W BNN Accelerator
Renzo Andri, Geethan Karunaratne, Lukas Cavigelli +1
Binary Neural Networks enable smart IoT devices, as they significantly reduce the required memory footprint and computational complexity while retaining a high network performance…
Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features
Michael Hersche, Tino Rellstab, Pasquale Davide Schiavone +3
Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interfa…