Open AccessPerformance Research on different Machine Learning Algorithms for Detection of Sleepy Spindles from EEG signals
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i1040.0789s219
Subject(s) - electroencephalography , brain–computer interface , alpha wave , computer science , transient (computer programming) , alpha (finance) , brain activity and meditation , brain waves , amplitude , artificial intelligence , pattern recognition (psychology) , psychology , neuroscience , physics , developmental psychology , optics , psychometrics , construct validity , operating system
Now a days spindles caused by drowsiness and it has become a very serious issue to accidents. A constant and long driving makes the human brain to a transient state between sleepy and awake. In this BCI plays a major role, where the captured signals from brain neurons are transferred to a computer device. In this paper, I considered the data which are collected from single Electroencephalography (EEG) using Brain Computer Interface (BCI) from the electrodes C3-A1 and C4- A1.Generally these sleepy spindles are present in the theta waves, whose are slower and high amplitude when compared to Alpha and Beta waves and the frequency in ranges from 4 – 8 Hz. The aim of this paper to analyse the accuracy of different machine learning algorithms to identify the spindles.