09:35Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
12:59Three and Four-Dimensional Visualization and Analysis Approaches to Study Vertebrate Axial Elongation and Segmentation
08:01Tuning a Parallel Segmented Flow Column and Enabling Multiplexed Detection
11:38Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench
04:57Comparative Analysis of Automatic Fecal Analyzer versus Direct Wet Smear Microscopy for Detecting Parasitic Infections in Stool Samples
18:11A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
You might also read
Articles linked to this work by shared authors, journal, and citation graph.
Updated: Jan 20, 2026

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
Published on: March 10, 2017
Aitziber Atutxa1, Kepa Bengoetxea1, Arantza Diaz de Ilarraza2
1Ixa Group, Language and Computer Systems, University of the Basque Country (UPV/EHU), Bilbao, Basque Country.
This study introduces a new tool for discourse analysis, improving text segmentation and identifying the Central Unit. This aids natural language processing (NLP) tasks by enhancing inter-sentence relation detection.
Area of Science:
Background:
Purpose of the Study:
Main Methods:
Main Results:
Conclusions: