[PDF][PDF] 基于固有时间尺度分解和自适应Huber 损失的脑电特征学习模型
杨利军， 蒋淑月， 魏小鸽， 肖运海 - 数学季刊, 2022 - sxjk.magtechjournal.com
EEG Feature Learning Model Based on Intrinsic Time-Scale Decomposition and Adaptive Huber
Loss Page 1 Chin. Quart. J. of Math. 2022, 37(3): 281–300 EEG Feature Learning Model Based …
Loss Page 1 Chin. Quart. J. of Math. 2022, 37(3): 281–300 EEG Feature Learning Model Based …
[PDF][PDF] EEG Feature Learning Model Based on Intrinsic Time-Scale Decomposition and Adaptive Huber Loss
L YANG, S JIANG, X WEI… - … Quarterly Journal of …, 2022 - sxjk.magtechjournal.com
According to the World Health Organization, about 50 million people worldwide suffer from
epilepsy. The detection and treatment of epilepsy face great challenges …
epilepsy. The detection and treatment of epilepsy face great challenges …
[PDF][PDF] 分层向量自回归的多通道脑电信号的特征提取研究
王金甲， 陈春 - 自动化学报, 2016 - aas.net.cn
摘要有效的特征提取方法能提高脑机接口(Brain-computer interface, BCI) 系统对脑电(
Electroencephalogram, EEG) 信号的识别率. 因脑电信号都是多通道的 …
Electroencephalogram, EEG) 信号的识别率. 因脑电信号都是多通道的 …
Detailed Evaluation of Spatiotemporal Learning Rule Based on Hamming Distances Among Output Vectors
T Takeru, O Takemori - IEICE Proceedings Series, 2023 - ieice.org
A spatio-temporal learning rule was proposed as a model of hippocampal memory. Although
it can be confirmed that the spatio-temporal learning rule separates input patterns in the …
it can be confirmed that the spatio-temporal learning rule separates input patterns in the …
[HTML][HTML] Time-frequency feature extraction for classification of episodic memory
R Anderson, M Sandsten - EURASIP Journal on Advances in Signal …, 2020 - Springer
This paper investigates the extraction of time-frequency (TF) features for classification of
electroencephalography (EEG) signals and episodic memory. We propose a model based …
electroencephalography (EEG) signals and episodic memory. We propose a model based …
[PDF][PDF] A Universal Generalization for Temporal-Difference Learning Using Haar Basis Functions
S Katayama, H Kimura, S Kobayashi - ICML, 2000 - Citeseer
We propose an algorithm e ciently implementing TD () using (the in nite tree of) Haar basis
functions. The algorithm can maintain and update the information of the in nite tree of coe …
functions. The algorithm can maintain and update the information of the in nite tree of coe …