This research aims to decode hand grasps from Electroencephalograms (EEGs)
for dexterous neuroprosthetic development and Brain-Computer Interface (BCI)
applications, especially for patients with motor disorders. Particularly, it
focuses on distinguishing two complex natural power and precision grasps in
addition to a neutral condition as a no-movement condition using a new
EEG-based BCI platform and wavelet signal processing. Wavelet analysis involved
generating time-frequency and topographic maps from wavelet power coefficients.
Then, by using machine learning techniques with novel wavelet features, we
achieved high average accuracies: 85.16% for multiclass, 95.37% for No-Movement
vs Power, 95.40% for No-Movement vs Precision, and 88.07% for Power vs
Precision, demonstrating the effectiveness of these features in EEG-based grasp
differentiation. In contrast to previous studies, a critical part of our study
was permutation feature importance analysis, which highlighted key features for
grasp classification. It revealed that the most crucial brain activities during
grasping occur in the motor cortex, within the alpha and beta frequency bands.
These insights demonstrate the potential of wavelet features in real-time
neuroprosthetic technology and BCI applications.

本研究旨在使用脑电图解码手部抓握动作，以用于熟练的神经临床和脑机接口应用，特别是对于运动障碍患者。通过使用新的基于脑电图的脑机接口平台和小波信号处理来区分两种复杂的自然力度和精准的抓握，并与无运动状态（中性条件）进行比较，利用机器学习技术和新颖的小波特征，我们实现了高平均准确率：对于多分类为 85.16％，对于无运动与力度抓握的区分为 95.37％，对于无运动与精准抓握的区分为 95.40％，对于力度与精准抓握之间的区分为 88.07％，展示了这些特征在基于脑电图的抓握区分中的有效性。与以往研究相比，我们研究的关键部分是排列特征重要性分析，它突出了抓握分类的关键特征。结果显示，在抓握过程中，最关键的脑活动发生在脑运动皮层中的阿尔法和贝塔频带内。这些见解展示了小波特征在实时神经临床技术和脑机接口应用中的潜力。

无创脑电波小波分析鉴别复杂与自然掌握类型

Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and  Natural Grasp Types

Sleep, a fundamental physiological process, occupies a significant portion of
our lives. Accurate classification of sleep stages serves as a crucial tool for
evaluating sleep quality and identifying probable sleep disorders. This work
introduces a novel methodology that utilises a SE-Resnet-Bi-LSTM architecture
to classify sleep into five separate stages. The classification process is
based on the analysis of single-channel electroencephalograms (EEGs). The
framework that has been suggested consists of two fundamental elements: a
feature extractor that utilises SE-ResNet, and a temporal context encoder that
use stacks of Bi-LSTM units.The effectiveness of our approach is substantiated
by thorough assessments conducted on three different datasets, namely
SLeepEDF-20, SleepEDF-78, and SHHS. Significantly, our methodology attains
notable levels of accuracy, specifically 87.5\%, 83.9\%, and 87.8\%, along with
macro-F1 scores of 82.5, 78.9, and 81.9 for the corresponding datasets.
Notably, we introduce the utilization of 1D-GradCAM visualization to shed light
on the decision-making process of our model in the realm of sleep stage
classification. This visualization method not only provides valuable insights
into the model's classification rationale but also aligns its outcomes with the
annotations made by sleep experts. One notable feature of our research is the
integration of an expedited training approach, which effectively preserves the
model's resilience in terms of performance. The experimental evaluations
conducted provide a comprehensive evaluation of the effectiveness of our
proposed model in comparison to existing approaches, highlighting its potential
for practical applications.

使用 SE-Resnet-Bi-LSTM 架构对睡眠进行五个阶段的分类，方法基于对单通道脑电图（EEG）的分析，利用 1D-GradCAM 可视化方法解释模型决策过程，研究结果表明该模型在睡眠阶段分类上具有较高的准确率和 F1 分数。

探索睡眠之深：基于单通道脑电图的睡眠阶段分类与模型可解释性

A Deep Dive into Sleep: Single-Channel EEG-Based Sleep Stage  Classification with Model Interpretability

Brain-computer interface (BCI) is a communication system between humans and
computers reflecting human intention without using a physical control device.
Since deep learning is robust in extracting features from data, research on
decoding electroencephalograms by applying deep learning has progressed in the
BCI domain. However, the application of deep learning in the BCI domain has
issues with a lack of data and overconfidence. To solve these issues, we
proposed a novel data augmentation method, CropCat. CropCat consists of two
versions, CropCat-spatial and CropCat-temporal. We designed our method by
concatenating the cropped data after cropping the data, which have different
labels in spatial and temporal axes. In addition, we adjusted the label based
on the ratio of cropped length. As a result, the generated data from our
proposed method assisted in revising the ambiguous decision boundary into
apparent caused by a lack of data. Due to the effectiveness of the proposed
method, the performance of the four EEG signal decoding models is improved in
two motor imagery public datasets compared to when the proposed method is not
applied. Hence, we demonstrate that generated data by CropCat smooths the
feature distribution of EEG signals when training the model.

本研究提出了一种数据增强方法 CropCat 来改善深度学习在脑机接口领域中因缺乏数据和过度自信的问题。通过对空间和时间轴上标记不同的数据进行裁剪以及调整标签，CropCat 方法生成的数据有效地改善了 EEG 信号决策边界的不确定因素，并显著提高了两个采用运动想象模式的公共数据集中的四种 EEG 信号解码模型的性能。