Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.

• Evaluation and extraction of mismatch negativity through exploiting temporal, spectral, time-frequency, and spatial features 

  Cong, Fengyu (University of Jyväskylä, 2010)

• Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition 

  Liu, Jia; Zhang, Chi; Zhu, Yongjie; Ristaniemi, Tapani; Parviainen, Tiina; Cong, Fengyu (Elsevier B.V., 2020)

  Background and objective. It is challenging to conduct real-time identification of myocardial infarction (MI) due to artifact corruption and high dimensionality of multi-lead electrocardiogram ...

• Objective Extraction of Evoked Event-Related Oscillation from Time-Frequency Representation of Event-Related Potentials 

  Zhang, Guanghui; Li, Xueyan; Cong, Fengyu (Hindawi Limited, 2020)

  Evoked event-related oscillations (EROs) have been widely used to explore the mechanisms of brain activities for both normal people and neuropsychiatric disease patients. In most previous studies, the calculation of the ...

• Multi-domain Features of the Non-phase-locked Component of Interest Extracted from ERP Data by Tensor Decomposition 

  Zhang, Guanghui; Zhang, Chi; Cao, Shuo; Xia, Xue; Tan, Xin; Si, Lichengxi; Wang, Chenxin; Wang, Xiaochun; Zhou, Chenglin; Ristaniemi, Tapani; Cong, Fengyu (Springer, 2020)

  The waveform in the time domain, spectrum in the frequency domain, and topography in the space domain of component(s) of interest are the fundamental indices in neuroscience research. Despite the application of time–frequency ...

• Single-trial-based temporal principal component analysis on extracting event-related potentials of interest for an individual subject 

  Zhang, Guanghui; Li, Xueyan; Lu, Yingzhi; Tiihonen, Timo; Chang, Zheng; Cong, Fengyu (Elsevier, 2023)

  Background: Temporal principal component analysis (tPCA) has been widely used to extract event-related potentials (ERPs) at group level of multiple subjects ERP data and it assumes that the underlying factor loading is ...