A Logistic Regression Based Framework for Spatio-Temporal Feature Representation and Classification of Single-Trial EEG

2021 ◽  
pp. 387-394
Author(s):  
Feifei Qi ◽  
Wei Wu ◽  
Ke Liu ◽  
Tianyou Yu ◽  
Yang Cao
Keyword(s):  
Logistic Regression ◽  
Feature Representation ◽  
Single Trial ◽  
Spatio Temporal ◽  
Temporal Feature

Extracting Spatio-temporal Feature for Classification of Event-related Potentials*

2011 ◽  
Vol 38 (9) ◽  
pp. 866-871 ◽  
Author(s):  
Zhi-Hua HUANG ◽  
Ming-Hong LI ◽  
Yuan-Ye MA ◽  
Chang-Le ZHOU
Keyword(s):  
Event Related Potentials ◽  
Related Potentials ◽  
Spatio Temporal ◽  
Temporal Feature

Histogram-based spatio-temporal feature classification of vegetation indices time-series for crop mapping

2018 ◽  
Vol 72 ◽  
pp. 34-41 ◽  
Author(s):  
Saeid Niazmardi ◽  
Saeid Homayouni ◽  
Abdolreza Safari ◽  
Heather McNairn ◽  
Jiali Shang ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Indices ◽  
Feature Classification ◽  
Crop Mapping ◽  
Spatio Temporal ◽  
Temporal Feature

scholarly journals Pyramidal Framework: Guidance for the Next Generation of GIS Spatial-Temporal Models

2021 ◽  
Vol 10 (3) ◽  
pp. 188
Author(s):  
Cyril Carré ◽  
Younes Hamdani
Keyword(s):  
Conceptual Models ◽  
Future Generations ◽  
Temporal Data ◽  
Temporal Modeling ◽  
Temporal Models ◽  
Gis Data ◽  
Spatio Temporal ◽  
New Interpretation ◽  
Conventional Literature

Over the last decade, innovative computer technologies and the multiplication of geospatial data acquisition solutions have transformed the geographic information systems (GIS) landscape and opened up new opportunities to close the gap between GIS and the dynamics of geographic phenomena. There is a demand to further develop spatio-temporal conceptual models to comprehensively represent the nature of the evolution of geographic objects. The latter involves a set of considerations like those related to managing changes and object identities, modeling possible causal relations, and integrating multiple interpretations. While conventional literature generally presents these concepts separately and rarely approaches them from a holistic perspective, they are in fact interrelated. Therefore, we believe that the semantics of modeling would be improved by considering these concepts jointly. In this work, we propose to represent these interrelationships in the form of a hierarchical pyramidal framework and to further explore this set of concepts. The objective of this framework is to provide a guideline to orient the design of future generations of GIS data models, enabling them to achieve a better representation of available spatio-temporal data. In addition, this framework aims at providing keys for a new interpretation and classification of spatio-temporal conceptual models. This work can be beneficial for researchers, students, and developers interested in advanced spatio-temporal modeling.

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