.png){kind=icon}
返回
A causal perspective on brainwave modeling for brain-computer interfaces
.png){kind=icon}
OA
AI
DOI：10.1088/1741-2552/ad3eb5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Objective. Machine learning (ML) models have opened up enormous opportunities in the field of brain-computer Interfaces (BCIs). Despite their great success, they usually face severe limitations when they are employed in real-life applications outside a controlled laboratory setting. Approach. Mixing causal reasoning, identifying causal relationships between variables of interest, with brainwave modeling can change one's viewpoint on some of these major challenges which can be found in various stages in the ML pipeline, ranging from data collection and data pre-processing to training methods and techniques. Main results. In this work, we employ causal reasoning and present a framework aiming to breakdown and analyze important challenges of brainwave modeling for BCIs. Significance. Furthermore, we present how general ML practices as well as brainwave-specific techniques can be utilized and solve some of these identified challenges. And finally, we discuss appropriate evaluation schemes in order to measure these techniques' performance and efficiently compare them with other methods that will be developed in the future.
Keyword:
brain-computer interface (BCI)
brainwaves
causal reasoning
representation learning
electroencephalography (EEG)
期刊
IF:
3.8
论文数:
4.0K
被引数:
1.4W
机构
引用论文
.png){kind=icon}
A novel deep learning approach for classification of EEG motor imagery signals一种新的用于脑电运动想象信号分类的深度学习方法
PREAI
分享收藏Semi-Supervised EEG Signals Classification System for Epileptic Seizure Detection用于癫痫发作检测的半监督脑电信号分类系统
PREAI
分享收藏Structural validity of the Serious Leisure Inventory & Measure (SLIM) in different sets of athletes
PREAI
分享收藏
查看更多内容