scholarly journals Entropy-based EEG time interval selection for improving motor imagery classification

2016 ◽  
Author(s):  
Rahim Soleymanpour ◽  
Mahnaz Arvaneh
Keyword(s):  
Motor Imagery ◽  
Time Interval ◽  
Interval Selection ◽  
Selection For

Utility of CT perfusion scanning in patient selection for acute stroke intervention: experience at University at Buffalo Neurosurgery–Millard Fillmore Gates Circle Hospital

2011 ◽  
Vol 30 (6) ◽  
pp. E4 ◽  
Author(s):  
Peter T. Kan ◽  
Kenneth V. Snyder ◽  
Parham Yashar ◽  
Adnan H. Siddiqui ◽  
L. Nelson Hopkins ◽  
...  
Keyword(s):  
Acute Stroke ◽  
Patient Selection ◽  
Ct Perfusion ◽  
Brain Parenchyma ◽  
National Institutes Of Health ◽  
Time Interval ◽  
Case Examples ◽  
Flow Parameters ◽  
Selection For ◽  
The Brain

Computed tomography perfusion scanning generates physiological flow parameters of the brain parenchyma, allowing differentiation of ischemic penumbra and core infarct. Perfusion maps, along with the National Institutes of Health Stroke Scale score, are used as the bases for endovascular stroke intervention at the authors' institute, regardless of the time interval from stroke onset. With case examples, the authors illustrate their perfusion-based imaging guidelines in patient selection for endovascular treatment in the setting of acute stroke.

Channel Selection for Optimal EEG Measurement in Motor Imagery-Based Brain-Computer Interfaces

2020 ◽  
pp. 2150003
Author(s):  
Pasquale Arpaia ◽  
Francesco Donnarumma ◽  
Antonio Esposito ◽  
Marco Parvis
Keyword(s):  
Motor Imagery ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Binary Classification ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Selection For ◽  
A Performance

A method for selecting electroencephalographic (EEG) signals in motor imagery-based brain-computer interfaces (MI-BCI) is proposed for enhancing the online interoperability and portability of BCI systems, as well as user comfort. The attempt is also to reduce variability and noise of MI-BCI, which could be affected by a large number of EEG channels. The relation between selected channels and MI-BCI performance is therefore analyzed. The proposed method is able to select acquisition channels common to all subjects, while achieving a performance compatible with the use of all the channels. Results are reported with reference to a standard benchmark dataset, the BCI competition IV dataset 2a. They prove that a performance compatible with the best state-of-the-art approaches can be achieved, while adopting a significantly smaller number of channels, both in two and in four tasks classification. In particular, classification accuracy is about 77–83% in binary classification with down to 6 EEG channels, and above 60% for the four-classes case when 10 channels are employed. This gives a contribution in optimizing the EEG measurement while developing non-invasive and wearable MI-based brain-computer interfaces.

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