I am building a project to visualise brain data from EEG, specifically emotions. I have found a significant numbers of papers that describe methods that include pre-processing, feature extraction and classification to get a 2 dimensional affect-valence measure.

However the methods in the papers are too complex for a programmer who doesn't specialise in EEG and they don't describe the details of the process.

Is there a way that one can turn either raw data or frequency band data into an emotional readout? I have access to the Emotiv Insight (5 channel) and the Muse (4 channel). It's not for a scientific purpose, so it doesn't need to be too exact. A crude guess would do.

I have found this paper from 2001 which describes a method using the frontal lobes which I might be able to reproduce. However the reason it is easy to implement is because it lacks the machine learning and processing that more contemporary papers have to classify EEG data into emotion.

Detecting Emotion from EEG Signals Using the Emotiv Epoc Device (2012)

Emotion Classification based on Gamma EEG

Classifying Different Emotional States by Means of EEG-Based Functional Connectivity Patterns

EEG-based emotion recognition during watching movies

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    $\begingroup$ Preprocessing is a vital part of EEG analysis. With EEG you measure more noise than data and preprocessing cannot be skipped. There are algorithms and programs that have automatic artifact rejection but those must also be implemented (unless you buy some software). When you've done this, then you can start doing analyses in either the time/frequency spectrum. $\endgroup$ Aug 29, 2016 at 16:49


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