Color group selection for computer interfaces

2000 ◽  
Author(s):  
Paul Lyons ◽  
Giovanni Moretti ◽  
Mark Wilson
Keyword(s):  
Group Selection ◽  
Color Group ◽  
Computer Interfaces ◽  
Selection For

Data-Driven Mode and Group Selection for Downlink MU-MIMO with Implementation in Commodity 802.11ac Network

2021 ◽  
pp. 1-1
Author(s):  
Shi Su ◽  
Wai-Tian Tan ◽  
Xiaoqing Zhu ◽  
Rob Liston ◽  
Behnaam Aazhang
Keyword(s):  
Group Selection ◽  
Data Driven ◽  
Selection For

Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm

2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Stanisław Karkosz ◽  
Marcin Jukiewicz
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Steady State ◽  
Brain Computer Interface ◽  
Morphological Features ◽  
Computer Interface ◽  
Visually Evoked Potentials ◽  
Computer Interfaces ◽  
Subject Variability ◽  
Selection For

AbstractObjectivesOptimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy.MethodsSystem of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI).ResultsThe designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects.ConclusionsIt is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.

The deep origins of society: An assessment of E.O. Wilson’s Genesis

2019 ◽  
Vol 34 (5) ◽  
pp. 536-551
Author(s):  
Jonathan H Turner ◽  
Alexandra Maryanski
Keyword(s):  
Social Insects ◽  
Group Selection ◽  
Multilevel Selection ◽  
Writing Style ◽  
Darwinian Selection ◽  
Sociological Work ◽  
Selection For ◽  
Societal Evolution

E.O. Wilson’s Genesis: The Deep Origins of Societies is one of a series of short books where the author has tried to explain human societies using ideas and concepts from biology. While Wilson is to be lauded for his recent efforts to reintroduce the notions of group selection and multilevel selection, he still sustains an emphasis on only Darwinian selection and reveals a bias toward seeing selection for groups as a result of selection on individuals (as is the case for insects), perhaps entangled with selection on groups. The effort to conceive of human societies as an example of eusocieties of social insects ignores most of the sociological works on human and societal evolution; and as a result, the book is not convincing in its argument. Despite the pleasant writing style, Wilson and other biologists writing about human societies need to engage the almost 200 years of sociological work devoted to understanding the evolution of human societies.

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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