Performance Research on different Machine Learning Algorithms for Detection of Sleepy Spindles from EEG signals
HemaLatha Goli1, Ch. Aparna2

1HemaLatha Goli, Research Scholar, Deptmant of CSE, Acharya Nagarjuna University & Assistant Professor ,KKR & KSR Institute of  Technology & Sciences, Guntur,  A. P, India.

2Dr. Ch. Aparna, Research Guide, Acharya Nagarjuna University & Associate Professor,  Deptmant  of CSE, R.V.R & J.C College of Engineering, Guntur, A. P, India.

Manuscript received on 09 August 2019 | Revised Manuscript received on 16 August 2019 | Manuscript Published on 31 August 2019 | PP: 203-208 | Volume-8 Issue-9S2 August 2019 | Retrieval Number: I10400789S219/19©BEIESP DOI: 10.35940/ijitee.I1040.0789S219

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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.

Keywords: Electroencephalography (EEG), Brain Computer Interface (BCI), Wavelet Transform, Fast Fourier Transform (FFT), Support Vector Machines (SVM), Neural Networks (NN), Random Forest (RF), Gaussian Naïve Bayes (GNB), K-nearest neighbour (K-NN)
Scope of the Article: Computer Graphics, Simulation, and Modelling