Motor

Classifying motor imagery from EEG data Feature selection

Classifying motor imagery from EEG data Feature selection
  1. How do you classify an EEG signal?
  2. What is motor imagery classification?
  3. What is motor imagery EEG?
  4. What are features in EEG data?

How do you classify an EEG signal?

The types of EEG waves[2,3] are identified according to their frequency range – delta: below 3.5 Hz (0.1–3.5 Hz), theta: 4–7.5 Hz, alpha: 8–13 Hz, beta: 14–40 Hz, and gamma: above 40 Hz.

What is motor imagery classification?

Motor imagery classification is an important topic in brain-computer interface (BCI) research that enables the recognition of a subject's intension to, e.g., implement prosthesis control.

What is motor imagery EEG?

A motor imagery-based brain-computer interface (MI-BCI) creates a path through which the brain interacts with the external environment by recording and processing electroencephalograph (EEG) signals made by imagining the movement of a particular limb. From: Artificial Intelligence-Based Brain-Computer Interface, 2022.

What are features in EEG data?

The simplest features of the EEG signal are statistical features, like mean, median, variance, standard deviation, skewness, kurtosis, and similar [50].

Algorithm DFT algorithm in Matlab
DFT algorithm in Matlab
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Offset Get rid of DC offset by subtracting mean, but still have impulse at f = 0 Hz
Get rid of DC offset by subtracting mean, but still have impulse at f = 0 Hz
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Correlation How to interpret cross correlation output?
How to interpret cross correlation output?
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