LGL-BCI: A Lightweight Geometric Learning Framework for Motor
Imagery-Based Brain-Computer Interfaces
- URL: http://arxiv.org/abs/2310.08051v3
- Date: Tue, 21 Nov 2023 12:36:49 GMT
- Title: LGL-BCI: A Lightweight Geometric Learning Framework for Motor
Imagery-Based Brain-Computer Interfaces
- Authors: Jianchao Lu, Yuzhe Tian, Yang Zhang, Jiaqi Ge, Quan Z. Sheng and Xi
Zheng
- Abstract summary: Brain-Computer Interfaces (BCIs) are a groundbreaking technology for interacting with external devices using brain signals.
EEG-based Motor Imagery (MI) tasks face challenges like amplitude and phase variability, and complex spatial correlations.
This study introduces the LGL-BCI framework, employing a Geometric Deep Learning Framework for EEG processing in non-Euclidean metric spaces.
- Score: 14.913592381049552
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Brain-Computer Interfaces (BCIs) are a groundbreaking technology for
interacting with external devices using brain signals. Despite advancements,
electroencephalogram (EEG)-based Motor Imagery (MI) tasks face challenges like
amplitude and phase variability, and complex spatial correlations, with a need
for smaller model size and faster inference. This study introduces the LGL-BCI
framework, employing a Geometric Deep Learning Framework for EEG processing in
non-Euclidean metric spaces, particularly the Symmetric Positive Definite (SPD)
Manifold space. LGL-BCI offers robust EEG data representation and captures
spatial correlations. We propose an EEG channel selection solution via a
feature decomposition algorithm to reduce SPD matrix dimensionality, with a
lossless transformation boosting inference speed. Extensive experiments show
LGL-BCI's superior accuracy and efficiency compared to current solutions,
highlighting geometric deep learning's potential in MI-BCI applications. The
efficiency, assessed on two public EEG datasets and two real-world EEG devices,
significantly outperforms the state-of-the-art solution in accuracy ($82.54\%$
versus $62.22\%$) with fewer parameters (64.9M compared to 183.7M).
Related papers
- Geometric Neural Network based on Phase Space for BCI-EEG decoding [2.8196015357423376]
The integration of Deep Learning algorithms on brain signal analysis is still in its nascent stages.
EEG is a widely adopted choice for designing BCI systems due to its non-invasive and cost-effective nature and excellent temporal resolution.
We propose the Phase-SPDNet architecture and analyze its performance and the interpretability of the results.
arXiv Detail & Related papers (2024-03-08T19:36:20Z) - ESTformer: Transformer Utilizing Spatiotemporal Dependencies for EEG
Super-resolution [14.2426667945505]
ESTformer is an EEG framework utilizingtemporal dependencies based on the Transformer.
The ESTformer applies positional encoding methods and the Multi-head Self-attention mechanism to the space and time dimensions.
arXiv Detail & Related papers (2023-12-03T12:26:32Z) - Bayesian Inference on Brain-Computer Interfaces via GLASS [4.04514704204904]
Low signal-to-noise ratio (SNR) and complex spatial/temporal correlations of EEG signals present challenges in modeling and computation.
We introduce a novel Gaussian Latent channel model with Sparse time-varying effects (GLASS) under a fully Bayesian framework.
We demonstrate GLASS substantially improves BCI's performance in participants with amyotrophic lateral sclerosis (ALS)
For broader accessibility, we develop an efficient gradient-based variational inference (GBVI) algorithm for posterior computation.
arXiv Detail & Related papers (2023-04-14T21:29:00Z) - A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on
Convolutional Neural Network [0.9176056742068814]
We propose a two-stream convolutional neural network (TSCNN) based hybrid brain-computer interface.
It combines steady-state visual evoked potential (SSVEP) and motor imagery (MI) paradigms.
TSCNN automatically learns to extract EEG features in the two paradigms in the training process.
arXiv Detail & Related papers (2022-12-10T12:34:36Z) - Collaborative Intelligent Reflecting Surface Networks with Multi-Agent
Reinforcement Learning [63.83425382922157]
Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks.
In this paper, we investigate a multi-user communication system assisted by cooperative IRS devices with the capability of energy harvesting.
arXiv Detail & Related papers (2022-03-26T20:37:14Z) - EEG-Inception: An Accurate and Robust End-to-End Neural Network for
EEG-based Motor Imagery Classification [123.93460670568554]
This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based motor imagery (MI) classification.
The proposed CNN model, namely EEG-Inception, is built on the backbone of the Inception-Time network.
The proposed network is an end-to-end classification, as it takes the raw EEG signals as the input and does not require complex EEG signal-preprocessing.
arXiv Detail & Related papers (2021-01-24T19:03:10Z) - Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with
Heterogeneous Learning Tasks [53.1636151439562]
Mobile edge computing (MEC) provides a natural platform for AI applications.
We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS)
Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
arXiv Detail & Related papers (2020-12-25T07:08:50Z) - Performance of Dual-Augmented Lagrangian Method and Common Spatial
Patterns applied in classification of Motor-Imagery BCI [68.8204255655161]
Motor-imagery based brain-computer interfaces (MI-BCI) have the potential to become ground-breaking technologies for neurorehabilitation.
Due to the noisy nature of the used EEG signal, reliable BCI systems require specialized procedures for features optimization and extraction.
arXiv Detail & Related papers (2020-10-13T20:50:13Z) - Optimization-driven Machine Learning for Intelligent Reflecting Surfaces
Assisted Wireless Networks [82.33619654835348]
Intelligent surface (IRS) has been employed to reshape the wireless channels by controlling individual scattering elements' phase shifts.
Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity.
In this article, we focus on machine learning (ML) approaches for performance in IRS-assisted wireless networks.
arXiv Detail & Related papers (2020-08-29T08:39:43Z) - Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A
Complete Pipeline [54.73337667795997]
Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject.
This paper proposes that TL could be considered in all three components (spatial filtering, feature engineering, and classification) of MI-based BCIs.
arXiv Detail & Related papers (2020-07-03T23:44:21Z) - EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded
Motor-Imagery Brain-Machine Interfaces [15.07343602952606]
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.
arXiv Detail & Related papers (2020-05-31T21:45:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.