.png){kind=icon}
返回
Efficient Brain Decoding Based on Adaptive EEG Channel Selection and Transformation
.png){kind=icon}
PRE
AI
DOI：10.1109/TETCI.2022.3147225.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Electroencephalography (EEG) based brain-computer interface (BCI) has a wide range of applications in neuro-rehabilitation and motor assistance. However, brain activities, acquired from a large number of EEG channels, are highly inter-correlated or irrelevant to the brain decoding task, thus reducing the decoding efficiency and accuracy. How to adaptively select the optimal channel number depend on different trials remains a big challenge. To solve this problem, an efficient end-to-end brain decoding model named AdaEEGNet, is proposed in this study. It can reduce the computational cost by adaptively controlling the number of input channels and improve the classification accuracy by reducing over-fitting. Specifically, a lightweight policy module is designed to analyze which channel is needed for decoding current EEG trial. Due to the channel selection process is indifferentiable, we propose to use the Gumbel-Estimator to back-propagate the gradient to train the whole framework. Additionally, a weight coefficient is designed to make a trade-off between brain decoding accuracy and efficiency. To validate the proposed AdaEEGNet feasibility in improving decoding efficiency and accuracy, extensive experiments were conducted on BCI competition IV dataset. The results show that our methods can improve the decoding accuracy by 2% with only 65% computational cost significantly compared with the baseline method.
Keyword:
Electroencephalography
Decoding
Brain modeling
Computational modeling
Optimized production technology
Feature extraction
Data models
Channel selection
channel transformation
brain decoding
computational cost
classification accuracy
期刊
I
IF:
6.5
论文数:
1.4K
被引数:
4.5K
机构
引用论文
Generating Realistic Users Using Generative Adversarial Network With Recommendation-Based Embedding使用具有基于推荐的嵌入的生成对抗网络生成现实用户.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏A mechanism describing polyamic acid solution viscosity change on storage at high temperaturePolymer
PolymerIF0
PREAI
分享收藏Interlayer fusion bonding of semi-crystalline polymer composites in extrusion deposition additive manufacturing挤出沉积增材制造中半结晶聚合物复合材料的层间熔融结合OAAI
OAAI分享收藏
查看更多内容