57 research outputs found
An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing
This paper presents an accurate and robust embedded motor-imagery
brain-computer interface (MI-BCI). The proposed novel model, based on EEGNet,
matches the requirements of memory footprint and computational resources of
low-power microcontroller units (MCUs), such as the ARM Cortex-M family.
Furthermore, the paper presents a set of methods, including temporal
downsampling, channel selection, and narrowing of the classification window, to
further scale down the model to relax memory requirements with negligible
accuracy degradation. Experimental results on the Physionet EEG Motor
Movement/Imagery Dataset show that standard EEGNet achieves 82.43%, 75.07%, and
65.07% classification accuracy on 2-, 3-, and 4-class MI tasks in global
validation, outperforming the state-of-the-art (SoA) convolutional neural
network (CNN) by 2.05%, 5.25%, and 5.48%. 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. The
scaled models are deployed on a commercial Cortex-M4F MCU taking 101ms and
consuming 4.28mJ per inference for operating the smallest model, and on a
Cortex-M7 with 44ms and 18.1mJ per inference for the medium-sized model,
enabling a fully autonomous, wearable, and accurate low-power BCI
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. It plays an important role in prosthesis control and motor
rehabilitation and is a crucial element towards the future Internet of Minds
(IoM). To improve user comfort, preserve data privacy, and reduce the system's
latency, a new trend in wearable BMIs is to embed algorithms on low-power
microcontroller units (MCUs) to process the electroencephalographic (EEG) data
in real-time close to the sensors into the wearable device. However, most of
the classification models present in the literature are too resource-demanding,
making them unfit for low-power MCUs. This paper proposes an efficient
convolutional neural network (CNN) for EEG-based MI classification that
achieves comparable accuracy while being orders of magnitude less
resource-demanding and significantly more energy-efficient than
state-of-the-art (SoA) models for a long-lifetime battery operation. We propose
an automatic channel selection method based on spatial filters and quantize
both weights and activations to 8-bit precision to further reduce the model
complexity with negligible accuracy loss. Finally, we efficiently implement and
evaluate the proposed models on a parallel ultra-low power (PULP) MCU. The most
energy-efficient solution consumes only 50.10 uJ with an inference runtime of
5.53 ms and an accuracy of 82.51% while using 6.4x fewer EEG channels, becoming
the new SoA for embedded MI-BMI and defining a new Pareto frontier in the
three-way trade-off among accuracy, resource cost, and power usage
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
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. Segmenting SAR data still requires skilled personnel,
limiting the potential for large-scale use. We show that it is possible to
automatically and reliably perform urban scene segmentation from next-gen
resolution SAR data (0.15m/px) using deep neural networks (DNNs), achieving a
pixel accuracy of 95.19% and a mean IoU of 74.67% with data collected over a
region of merely 2.2km. The presented DNN is not only effective, but is
very small with only 63k parameters and computationally simple enough to
achieve a throughput of around 500Mpx/s using a single GPU. We further identify
that additional SAR receive antennas and data from multiple flights massively
improve the segmentation accuracy. We describe a procedure for generating a
high-quality segmentation ground truth from multiple inaccurate building and
road annotations, which has been crucial to achieving these segmentation
results
Enhancing Performance, Calibration Time and Efficiency in Brain-Machine Interfaces through Transfer Learning and Wearable EEG Technology
Brain-machine interfaces (BMIs) have emerged as a transformative force in
assistive technologies, empowering individuals with motor impairments by
enabling device control and facilitating functional recovery. However, the
persistent challenge of inter-session variability poses a significant hurdle,
requiring time-consuming calibration at every new use. Compounding this issue,
the low comfort level of current devices further restricts their usage. To
address these challenges, we propose a comprehensive solution that combines a
tiny CNN-based Transfer Learning (TL) approach with a comfortable, wearable EEG
headband. The novel wearable EEG device features soft dry electrodes placed on
the headband and is capable of on-board processing. We acquire multiple
sessions of motor-movement EEG data and achieve up to 96% inter-session
accuracy using TL, greatly reducing the calibration time and improving
usability. By executing the inference on the edge every 100ms, the system is
estimated to achieve 30h of battery life. 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
ECG-TCN: Wearable Cardiac Arrhythmia Detection with a Temporal Convolutional Network
Personalized ubiquitous healthcare solutions require energy-efficient
wearable platforms that provide an accurate classification of bio-signals while
consuming low average power for long-term battery-operated use. Single lead
electrocardiogram (ECG) signals provide the ability to detect, classify, and
even predict cardiac arrhythmia. In this paper, we propose a novel temporal
convolutional network (TCN) that achieves high accuracy while still being
feasible for wearable platform use. Experimental results on the ECG5000 dataset
show that the TCN has a similar accuracy (94.2%) score as the state-of-the-art
(SoA) network while achieving an improvement of 16.5% in the balanced accuracy
score. This accurate classification is done with 27 times fewer parameters and
37 times less multiply-accumulate operations. We test our implementation on two
publicly available platforms, the STM32L475, which is based on ARM Cortex M4F,
and the GreenWaves Technologies GAP8 on the GAPuino board, based on 1+8 RISC-V
CV32E40P cores. Measurements show that the GAP8 implementation respects the
real-time constraints while consuming 0.10 mJ per inference. 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). Overall, we obtain 8.1%
higher accuracy while consuming 19.6 times less energy and being 35.1 times
faster compared to a previous SoA embedded implementation.Comment: 4 pages, 1 figure, 2 table
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
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