24 research outputs found
Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features
Accurate, fast, and reliable multiclass classification of
electroencephalography (EEG) signals is a challenging task towards the
development of motor imagery brain-computer interface (MI-BCI) systems. We
propose enhancements to different feature extractors, along with a support
vector machine (SVM) classifier, to simultaneously improve classification
accuracy and execution time during training and testing. We focus on the
well-known common spatial pattern (CSP) and Riemannian covariance methods, and
significantly extend these two feature extractors to multiscale temporal and
spectral cases. The multiscale CSP features achieve 73.7015.90% (mean
standard deviation across 9 subjects) classification accuracy that surpasses
the state-of-the-art method [1], 70.614.70%, on the 4-class BCI
competition IV-2a dataset. The Riemannian covariance features outperform the
CSP by achieving 74.2715.5% accuracy and executing 9x faster in training
and 4x faster in testing. Using more temporal windows for Riemannian features
results in 75.4712.8% accuracy with 1.6x faster testing than CSP.Comment: Published as a conference paper at the IEEE European Signal
Processing Conference (EUSIPCO), 201
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
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
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
In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory
Continually learning new classes from a few training examples without
forgetting previous old classes demands a flexible architecture with an
inevitably growing portion of storage, in which new examples and classes can be
incrementally stored and efficiently retrieved. One viable architectural
solution is to tightly couple a stationary deep neural network to a dynamically
evolving explicit memory (EM). As the centerpiece of this architecture, we
propose an EM unit that leverages energy-efficient in-memory compute (IMC)
cores during the course of continual learning operations. We demonstrate for
the first time how the EM unit can physically superpose multiple training
examples, expand to accommodate unseen classes, and perform similarity search
during inference, using operations on an IMC core based on phase-change memory
(PCM). Specifically, the physical superposition of a few encoded training
examples is realized via in-situ progressive crystallization of PCM devices.
The classification accuracy achieved on the IMC core remains within a range of
1.28%--2.5% compared to that of the state-of-the-art full-precision baseline
software model on both the CIFAR-100 and miniImageNet datasets when continually
learning 40 novel classes (from only five examples per class) on top of 60 old
classes.Comment: Accepted at the European Solid-state Devices and Circuits Conference
(ESSDERC), September 202
Binarization Methods for Motor-Imagery Brain–Computer Interface Classification
Successful motor-imagery brain–computer interface (MI-BCI) algorithms either extract a large number of handcrafted features and train a classifier, or combine feature extraction and classification within deep convolutional neural networks (CNNs). Both approaches typically result in a set of real-valued weights, that pose challenges when targeting real-time execution on tightly resource-constrained devices. We propose methods for each of these approaches that allow transforming real-valued weights to binary numbers for efficient inference. Our first method, based on sparse bipolar random projection, projects a large number of real-valued Riemannian covariance features to a binary space, where a linear SVM classifier can be learned with binary weights too. By tuning the dimension of the binary embedding, we achieve almost the same accuracy in 4-class MI (≤1.27% lower) compared to models with float16 weights, yet delivering a more compact model with simpler operations to execute. Second, we propose to use memory-augmented neural networks (MANNs) for MI-BCI such that the augmented memory is binarized. Our method replaces the fully connected layer of CNNs with a binary augmented memory using bipolar random projection, or learned projection. Our experimental results on EEGNet, an already compact CNN for MI-BCI, show that it can be compressed by 1.28x at iso-accuracy using the random projection. On the other hand, using the learned projection provides 3.89% higher accuracy but increases the memory size by 28.10x.ISSN:2156-335
Binary Models for Motor-Imagery Brain–Computer Interfaces: Sparse Random Projection and Binarized SVM
Successful motor imagery brain–computer (MI-BCI) algorithms typically rely on a large number of features used in a classifier with real-valued weights that render them unsuitable for real-time execution on a resource-limited device. We propose a new method that randomly projects a large number of real-valued Riemannian covariance features to a binary space, where a linear SVM classifier can be learned with binary weights too. Flexibly increasing the dimension of binary embedding achieves almost the same accuracy (≤1.27% lower) compared to all models with float16 in 4-class and 3-class MI, yet delivering a more compact model with simpler operations to execute