Emotional states recognition, implementing a low computational complexity strategy

Abstract

This article describes a methodology to recognize emotional states through an electroencephalography signals analysis, developed with the premise of reducing the computational burden that is associated with it, implementing a strategy that reduces the amount of data that must be processed by establishing a relationship between electrodes and Brodmann regions, so as to discard electrodes that do not provide relevant information to the identification process. Also some design suggestions to carry out a pattern recognition process by low computational complexity neural networks and support vector machines are presented, which obtain up to a 90.2% mean recognition rate.

Emotion Recognition by Correlating Facial Expressions and EEG Analysis

“…Our brain interprets emotional stimuli through a series of organicistic responses from the central nervous system [9]. Several studies use ML to analyze the behavior of emotional situations by recognizing patterns in the signals collected from the brain cortex-either region-or by considering all available information [20,21]. However, one of the main challenges is that these patterns are sought in large time windows (the time the evoked potential lasts, which can range from a few seconds to minutes), which implies that many other events can affect the experimental process, such as eye movements, facial muscles or cognitive states unrelated to the experiment.…”

“…Performing an adequate EEG signals analysis is one of the most critical stages for improving the recognition rate. The signal analysis stage is based on the considerations published in [21], which can be observed in Figure 3 and are described as follows: Filtering. A bandpass filter was implemented, with a cutoff of 0.2 to 47 Hz, to exclude all those frequencies found outside of a brain rhythm, and the information obtained for each of the rhythm ranges was subdivided: delta (0.2 to 3.5 Hz), theta (3.5 to 7.5 Hz), alpha (7.5 to 13 Hz), beta (13 to 28 Hz) and gamma (>28 Hz).…”

“…• Electrode selection by Brodmann bounded regions. The electrode selection methodology proposed in [21] was implemented, where the Brodmann areas related to the areas of the cortex specialized in visual, auditory processing and the hypothalamus are linked. In this way, the analysis region is delimited only to those electrodes connected to the parietal, temporal, frontal and occipital lobes of the cortex since these could provide more information related to the emotional process.…”

Predicting Exact Valence and Arousal Values from EEG

“…Regarding brain waves, most researchers use the set comprised of theta, alpha, beta and gamma. Some also use the delta [ 15 , 21 ] or a custom set of EEG frequencies [ 22 , 23 ], while Petrantonakis et al [ 24 , 25 ] used only alpha and beta frequencies, as these had produced the best results in previous works. The same for Zhang et al [ 14 , 26 ], who used only beta frequencies.…”

“…The emotion classification problem has been done in one of three ways: (i) identification of discrete emotions such as happiness, scared or disgust [ 24 , 27 , 34 , 40 , 41 , 42 ]; (ii) distinction between high/low arousal and high/low valence [ 2 , 3 , 4 , 19 , 29 , 31 , 43 ]; and (iii) finding the quadrant, in the valence/arousal space [ 13 , 14 , 19 , 21 , 44 , 45 ]. In the last two cases, researchers create two classifiers, one to discern between high/low valence and the other for high/low arousal.…”