5 citations · 11 across the 5 of their papers we have counts for
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Train-On-Request: An On-Device Continual Learning Workflow for Adaptive Real-World Brain Machine Interfaces
Lan Mei, Cristian Cioflan, Thorir Mar Ingolfsson +4
Brain-machine interfaces (BMIs) are expanding beyond clinical settings thanks to advances in hardware and algorithms. However, they still face challenges in user-friendliness and s…
An Ultra-Low Power Wearable BMI System with Continual Learning Capabilities
Lan Mei, Thorir Mar Ingolfsson, Cristian Cioflan +4
Driven by the progress in efficient embedded processing, there is an accelerating trend toward running machine learning models directly on wearable Brain-Machine Interfaces (BMIs)…
Energy-Efficient Tree-Based EEG Artifact Detection
Thorir Mar Ingolfsson, Andrea Cossettini, Simone Benatti +1
In the context of epilepsy monitoring, EEG artifacts are often mistaken for seizures due to their morphological similarity in both amplitude and frequency, making seizure detection…
Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices
Thorir Mar Ingolfsson, Andrea Cossettini, Xiaying Wang +5
We present the implementation of seizure detection algorithms based on a minimal number of EEG channels on a parallel ultra-low-power embedded platform. The analyses are based on t…
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…