Artificial Immune System–Negative Selection Classification Algorithm (NSCA) for Four Class Electroencephalogram (EEG) Signals

Artificial immune systems (AIS) are intelligent algorithms derived from the principles inspired by the human immune system. In this study, electroencephalography (EEG) signals for four distinct motor movements of human limbs are detected and classified using a negative selection classification algor...

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Veröffentlicht in:	Frontiers in human neuroscience Jg. 12; S. 439
Hauptverfasser:	Rashid, Nasir, Iqbal, Javaid, Mahmood, Fahad, Abid, Anam, Khan, Umar S., Tiwana, Mohsin I.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland Frontiers Research Foundation 20.11.2018 Frontiers Media S.A
Schlagworte:	Accuracy Algorithms artificial immune system (AIS) brain computer interface (BCI) Brain research Classification Clonal selection Discriminant analysis EEG electroencephalogram Electroencephalography Emulation genetic algorithm Geometry Immune system Lymphocytes mel frequency cepstral coefficients (MFCC) Methods Negative selection Neural networks Neuroscience Principal components analysis Sensors Signal processing staked auto-encoder System theory Pakistan genetic algorithm staked auto-encoder mel frequency cepstral coefficients (MFCC) brain computer interface (BCI) artificial immune system (AIS) electroencephalogram
ISSN:	1662-5161, 1662-5161
Online-Zugang:	Volltext
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