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### Aim of the experiment | ||
• To understand and analyze the distribution of frequency in the EEG data at different time bins. | ||
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• To understand the signal processing algorithm-Fast Fourier Transform (FFT). | ||
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• To understand and analyze the average band power (alpha, beta, theta, gamma) of a given frequency band to the overall power of the signal. |
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## Experiment name | ||
## Power spectrum calculations using different windows |
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**Post Test** | ||
1. Algorithm used for converting time series EEG data to power spectral density | ||
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a) ICA | ||
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b) Bandpass filter | ||
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c) IR filter | ||
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**d) FFT** | ||
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2. Power spectrum density indicates | ||
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a) Power distribution of EEG data series in time domain | ||
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**b) Power distribution of EEG series in the frequency domain** | ||
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c) Coherence of EEG series | ||
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d) None of the above | ||
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3. Unit for PSD measurement in EEG data | ||
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a) µVolts square per dB | ||
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**b) µVolts sqaure per Hz** | ||
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c) Volts sqaure per sec | ||
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d) µVolts | ||
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4. Which of the following is a real function of frequency? | ||
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**a) PSD** | ||
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b) FFT | ||
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c) PCA | ||
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d) None of the above | ||
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5. Power spectrum density method used for feature extraction of raw EEG signal | ||
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**a) Welch’s Method** | ||
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b) K-nearest neighbors classifier | ||
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c) Bartlett method | ||
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d) None of the above |
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1. The brain electrical activity is represented as: | ||
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a) ECG Signals | ||
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b) EMG Signals | ||
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**c) EEG Signals** | ||
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d) EOG Signals | ||
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2. The unwanted signals present in EEG signals is known as: | ||
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a) Signals | ||
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**b) Noise** | ||
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c) PSD | ||
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d) None of the above | ||
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3. The EEG signal processing involves: | ||
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a) Data Acquisition | ||
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b) Preprocessing | ||
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c) Feature extraction | ||
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**d) All the above** | ||
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4. EEG signals were measured in | ||
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a) Volts | ||
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**b) Microvolts** | ||
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c) Hertz | ||
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d) Millivolts |
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### Procedure | ||
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• User can upload the raw EEG data in EDF format in the GUI platform. | ||
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• The platform allows the user to choose different time windows for power spectrum analysis. | ||
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• The user can visualize the spectral density data for the respective time windows. | ||
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• GUI allows to compute the average band power of a specific frequency. | ||
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### Link your references in here | ||
1. Al-Fahoum, A. S., & Al-Fraihat, A. A. (2014). Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices, 2014. | ||
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2. Ahirwal, M. K., & Londhe, N. (2012). Power spectrum analysis of EEG signals for estimating visual attention. International Journal of computer applications, 42(15), 22-25. | ||
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3. Sałabun, W. (2014). Processing and spectral analysis of the raw EEG signal from the MindWave. Przeglad Elektrotechniczny, 90(2), 169-174. | ||
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4. Ktonas, P. Y., & Gosalia, A. P. (1981). Spectral analysis vs. period-amplitude analysis of narrowband EEG activity: a comparison based on the sleep delta-frequency band. Sleep, 4(2), 193-206. | ||
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5. https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/power-spectrum |
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<!DOCTYPE html> | ||
<html> | ||
<head> | ||
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<link rel="stylesheet" href="./css/main.css"> | ||
</head> | ||
<body> | ||
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<!-- Add JS at the bottom of HTML file --> | ||
<script src="./js/main.js"></script> | ||
</body> | ||
</html> | ||
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