How can I distinguish between two similar EEG signals?

1. How do you identify an EEG wave?
2. How do you extract features from an EEG signal?
3. How do you analyze EEG signals?
4. What is correlation in EEG?

How do you identify an EEG wave?

However, the most frequently used method to classify EEG waveforms is by the frequency, so much so, that EEG waves are named based on their frequency range using Greek numerals. The most commonly studied waveforms include delta (0.5 to 4Hz); theta (4 to 7Hz); alpha (8 to 12Hz); sigma (12 to 16Hz) and beta (13 to 30Hz).

How do you extract features from an EEG signal?

More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on.

How do you analyze EEG signals?

There are two important methods for time domain EEG analysis: Linear Prediction and Component Analysis. Generally, Linear Prediction gives the estimated value equal to a linear combination of the past output value with the present and past input value.

What is correlation in EEG?

The correlation coefficient represents the strength of association between two variables and the direction of the relationship, its value varies between +1 and Y1. A positive value indicates a positive degree of association between the two variables, while negative value indicates a negative relationship.