Performance research on different machine learning algorithms for detection of sleepy spindles from eeg signals

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Abstract

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.

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Goli, H., & Aparna, C. (2019). Performance research on different machine learning algorithms for detection of sleepy spindles from eeg signals. International Journal of Innovative Technology and Exploring Engineering, 8(9 Special Issue 2), 203–208. https://doi.org/10.35940/ijitee.I1040.0789S219

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