2 research outputs found
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. Practical use cases
require a wearable solution where the classification of the brain signals is
done locally near the sensor using machine learning models embedded on
energy-efficient microcontroller units (MCUs), for assured privacy, user
comfort, and long-term usage. In this work, we provide practical insights on
the accuracy-cost tradeoff for embedded BMI solutions. Our proposed
Multispectral Riemannian Classifier reaches 75.1% accuracy on 4-class MI task.
We further scale down the model by quantizing it to mixed-precision
representations with a minimal accuracy loss of 1%, which is still 3.2% more
accurate than the state-of-the-art embedded convolutional neural network. We
implement the model on a low-power MCU with parallel processing units taking
only 33.39ms and consuming 1.304mJ per classification
EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces
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 achieving high classification accuracy, DL
models have also grown in size, requiring a vast amount of memory and
computational resources. This poses a major challenge to an embedded BMI
solution that guarantees user privacy, reduced latency, and low power
consumption by processing the data locally. In this paper, we propose
EEG-TCNet, a novel temporal convolutional network (TCN) that achieves
outstanding accuracy while requiring few trainable parameters. Its low memory
footprint and low computational complexity for inference make it suitable for
embedded classification on resource-limited devices at the edge. Experimental
results on the BCI Competition IV-2a dataset show that EEG-TCNet achieves
77.35% classification accuracy in 4-class MI. By finding the optimal network
hyperparameters per subject, we further improve the accuracy to 83.84%.
Finally, we demonstrate the versatility of EEG-TCNet on the Mother of All BCI
Benchmarks (MOABB), a large scale test benchmark containing 12 different EEG
datasets with MI experiments. The results indicate that EEG-TCNet successfully
generalizes beyond one single dataset, outperforming the current
state-of-the-art (SoA) on MOABB by a meta-effect of 0.25.Comment: 8 pages, 6 figures, 5 table