Objective: Motor Imagery (MI) serves as a crucial experimental paradigm
within the realm of Brain Computer Interfaces (BCIs), aiming to decoding motor
intentions from electroencephalogram (EEG) signals. Method: Drawing inspiration
from Riemannian geometry and Cross-Frequency Coupling (CFC), this paper
introduces a novel approach termed Riemann Tangent Space Mapping using
Dichotomous Filter Bank with Convolutional Neural Network (DFBRTS) to enhance
the representation quality and decoding capability pertaining to MI features.
DFBRTS first initiates the process by meticulously filtering EEG signals
through a Dichotomous Filter Bank, structured in the fashion of a complete
binary tree. Subsequently, it employs Riemann Tangent Space Mapping to extract
salient EEG signal features within each sub-band. Finally, a lightweight
convolutional neural network is employed for further feature extraction and
classification, operating under the joint supervision of cross-entropy and
center loss. To validate the efficacy, extensive experiments were conducted
using DFBRTS on two well-established benchmark datasets: the BCI competition IV
2a (BCIC-IV-2a) dataset and the OpenBMI dataset. The performance of DFBRTS was
benchmarked against several state-of-the-art MI decoding methods, alongside
other Riemannian geometry-based MI decoding approaches. Results: DFBRTS
significantly outperforms other MI decoding algorithms on both datasets,
achieving a remarkable classification accuracy of 78.16% for four-class and
71.58% for two-class hold-out classification, as compared to the existing
benchmarks.

使用 Riemann Tangent Space Mapping 和 Dichotomous Filter Bank with Convolutional Neural Network（DFBRTS）这种新方法，该论文在 Motor Imagery（MI）和 Brain Computer Interfaces（BCIs）领域进行研究，以提高 MI 特征的表示质量和解码能力，经过对两个基准数据集的广泛实验，DFBRTS 在 MI 解码方面表现出显著优于其他方法的结果。

利用 Riemann 切空间映射和交叉频率耦合提升脑机接口中的运动想象解码

Enhancing Motor Imagery Decoding in Brain Computer Interfaces using  Riemann Tangent Space Mapping and Cross Frequency Coupling

The Motor Imagery (MI) electroencephalography (EEG) based Brain Computer
Interfaces (BCIs) allow the direct communication between humans and machines by
exploiting the neural pathways connected to motor imagination. Therefore, these
systems open the possibility of developing applications that could span from
the medical field to the entertainment industry. In this context, Artificial
Intelligence (AI) approaches become of fundamental importance especially when
wanting to provide a correct and coherent feedback to BCI users. Moreover,
publicly available datasets in the field of MI EEG-based BCIs have been widely
exploited to test new techniques from the AI domain. In this work, AI
approaches applied to datasets collected in different years and with different
devices but with coherent experimental paradigms are investigated with the aim
of providing a concise yet sufficiently comprehensive survey on the evolution
and influence of AI techniques on MI EEG-based BCI data.

本文研究了应用于不同年份和不同设备采集的数据集的人工智能方法在运动想象电生理信号脑机接口数据上的演变和影响，提供了简洁而全面的调查。

基于运动意向脑电的脑机接口的人工智能方法的演变

The evolution of AI approaches for motor imagery EEG-based BCIs

Brain computer interfaces (BCI) enable direct communication with a computer,
using neural activity as the control signal. This neural signal is generally
chosen from a variety of well-studied electroencephalogram (EEG) signals. For a
given BCI paradigm, feature extractors and classifiers are tailored to the
distinct characteristics of its expected EEG control signal, limiting its
application to that specific signal. Convolutional Neural Networks (CNNs),
which have been used in computer vision and speech recognition, have
successfully been applied to EEG-based BCIs; however, they have mainly been
applied to single BCI paradigms and thus it remains unclear how these
architectures generalize to other paradigms. Here, we ask if we can design a
single CNN architecture to accurately classify EEG signals from different BCI
paradigms, while simultaneously being as compact as possible. In this work we
introduce EEGNet, a compact convolutional network for EEG-based BCIs. We
introduce the use of depthwise and separable convolutions to construct an
EEG-specific model which encapsulates well-known EEG feature extraction
concepts for BCI. We compare EEGNet to current state-of-the-art approaches
across four BCI paradigms: P300 visual-evoked potentials, error-related
negativity responses (ERN), movement-related cortical potentials (MRCP), and
sensory motor rhythms (SMR). We show that EEGNet generalizes across paradigms
better than the reference algorithms when only limited training data is
available. We demonstrate three different approaches to visualize the contents
of a trained EEGNet model to enable interpretation of the learned features. Our
results suggest that EEGNet is robust enough to learn a wide variety of
interpretable features over a range of BCI tasks, suggesting that the observed
performances were not due to artifact or noise sources in the data.

本文主要介绍一种基于深度卷积神经网络的脑 - 计算机接口通信模型 EEGNet，该模型考虑到不同 BCI 范式中 EEG 信号的特点，成功解决了不同信号间的分类问题。