一种基于卷积神经网络的融合多次频带和CBAM SSVEP-BCI分类方法
Dongyang Lei1,2, Chaoyi Dong3,4,5,6, Hongfei Guo7
1College of Electric Power, Inner Mongolia University of Technology, Hohhot, 010080, China.
Scientific reports
|April 14, 2024
概括
使用融合多次次频带和卷积块注意模块 (CBAM) 的新卷积神经网络 (CNN) 方法显著改善了脑计算机接口 (BCI) 稳定状态视觉唤起潜力 (SSVEP) 信号的性能,特别是在短时间窗口中.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 传统的方法难以对脑电脑接口 (BCI) 的短时间窗口稳定状态视觉唤起潜力 (SSVEP) 信号进行分类.
- 准确和高效的SSVEP信号处理对于推进BCI应用至关重要.
研究的目的:
- 提出一种基于卷积神经网络 (CNN) 的新型分类方法,称为CBAM-CNN,用于增强SSVEP-BCI任务.
- 通过整合多次次频带和卷积块注意模块 (CBAM) 来提高分类性能,特别是对于短时间的SSVEP信号.
主要方法:
- 提取和融合多次子频带的SSVEP信号作为最初的网络输入.
- 在初始输入和特征融合阶段整合CBAM以进行自适应特征改进.
- 使用内蒙古理工大学 (IMUT) 和清华大学 (THU) 数据集进行验证.
主要成果:
- 拟议的CBAM-CNN实现了0.9813个百分点 (pp) 的最大精度.
- 与CNN,CCA-CWT-SVM,CCA-SVM,CCA-GNB,FBCCA和CCA相比,CBAM-CNN在0.1-2秒的时间窗口内显示出更高的精度 (比0.0201-0.5388 pp更高).
- 在短时间窗口 (0.1-1秒) 中观察到异常性能,最大信息传输速率 (ITR) 为503.87比特/分钟.
结论:
- 在SSVEP解码中,CBAM-CNN显著优于现有的方法,特别是在短时间限制下.
- 该方法在准确性和信息传输速度方面取得了实质性的改进,突出了其在实用SSVEP-BCI应用中的潜力.
相关概念视频
01:22Force Classification
2.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.8K
01:30Classification of Signals
1.6K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.6K
01:26Classification of Systems-I
750
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
750