<p>I am working on a project that uses EEG signals of the brain to identify emotional states. While surveying the literature, I came across several references where "derived features of bispectrum" are used as features for the purpose of classifying motor and emotional signals.</p>

<p>Although I have a list of these "derived features" at my disposal, I would like to have some intuition as to which of these features I should use and why consider a bispectrum in the first place. I would highly appreciate it if the physical significance of the bispectrum could be explained in relation to EEG signals.</p>

<p><strong>What is EEG bispectrum?</strong></p>

CC BY-SA 3.0
2015-01-31T06:35:16.297Z
Welcome to CogSci.SE and thanks for your interesting question! Could you please add a reference or article link describing these derived features of the bispectrum?
AliceD
CC BY-SA 3.0
2015-01-31T09:28:08.610Z
Sure!! [Introduction to bispectrum for EEG analysis](http://www.robots.ox.ac.uk/~davidc/pubs/pgbiomed09.pdf), [Using HOS from EEG signals for developing BCI](https://www.dora.dmu.ac.uk/handle/2086/874) @ChrisStronks
Nitin Kumar
CC BY-SA 3.0
2015-01-31T11:05:59.823Z
Thanks. And what exactly is the list, as you ask which of them you should use?
AliceD
CC BY-SA 3.0
2015-01-31T11:18:26.810Z
The list is not central to the question, as there are methods of determining a favorable set of features for classification purposes. What I am not getting is, how the bispectrum of EEG would prove to be a better discriminant than other measures such as features of PSD of EEG. @ChrisStronks
Nitin Kumar
CC BY-SA 3.0
2015-01-31T14:00:47.803Z
I don't think it is a matter of better or worse, but to a matter of adding information. FFT results in frequency and phase data. Often the phase (50% of the FFT data in fact) is thrown away without bothering.
AliceD
CC BY-SA 3.0
2015-03-28T12:21:02.987Z
The second page of the paper cited defines exactly what is meant by bispectrum: "The corresponding bispectrum is defined as the 2-D
Fourier transform of the third-order cumulant." I could give this as an answer, but it seems weird.
Christian Hummeluhr
<emotion><measurement><terminology><eeg><signal-detection>
What is bispectrum?
Nitin Kumar
# What is bispectrum?

(Asked by: username_0 on 2015-01-31)

I am working on a project that uses EEG signals of the brain to identify emotional states. While surveying the literature, I came across several references where "derived features of bispectrum" are used as features for the purpose of classifying motor and emotional signals.

Although I have a list of these "derived features" at my disposal, I would like to have some intuition as to which of these features I should use and why consider a bispectrum in the first place. I would highly appreciate it if the physical significance of the bispectrum could be explained in relation to EEG signals.

**What is EEG bispectrum?**

**Tags:** emotion, measurement, terminology, eeg, signal-detection

> **Comments:**
> **username_1**: Welcome to CogSci.SE and thanks for your interesting question! Could you please add a reference or article link describing these derived features of the bispectrum?  
> **username_0**: Sure!! [Introduction to bispectrum for EEG analysis](http://www.robots.ox.ac.uk/~davidc/pubs/pgbiomed09.pdf), [Using HOS from EEG signals for developing BCI](https://www.dora.dmu.ac.uk/handle/2086/874) @ChrisStronks  
> **username_1**: Thanks. And what exactly is the list, as you ask which of them you should use?  
> **username_0**: The list is not central to the question, as there are methods of determining a favorable set of features for classification purposes. What I am not getting is, how the bispectrum of EEG would prove to be a better discriminant than other measures such as features of PSD of EEG. @ChrisStronks  
> **username_1**: I don't think it is a matter of better or worse, but to a matter of adding information. FFT results in frequency and phase data. Often the phase (50% of the FFT data in fact) is thrown away without bothering.  
> **username_2**: The second page of the paper cited defines exactly what is meant by bispectrum: "The corresponding bispectrum is defined as the 2-D Fourier transform of the third-order cumulant." I could give this as an answer, but it seems weird.  

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