Open access peer-reviewed chapter

Mapping and Timing the (Healthy) Emotional Brain: A Review

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Pablo Revuelta Sanz, María José Lucía Mulas, Tomás Ortiz, José M. Sánchez Pena and Belén Ruiz-Mezcua

Submitted: November 30th, 2020 Reviewed: December 21st, 2020 Published: January 18th, 2021

DOI: 10.5772/intechopen.95574

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The study of the emotional processing in the brain began from a psychological point of view in the last decades of the 19th century. However, since the discovery of the electrical background of mental activity around 1930, a new scientific way of observing and measuring the functioning of the living brain has opened up. In addition, Functional Magnetic Resonance Imaging (fMRI) has given neuroscientists a (literally) deeper instrument to perform such measurements. With all this technological background, the last decades have produced an important amount of information about how the brain works. In this chapter, we review the latest results on the emotional response of the brain, a growing field in neuroscience.


  • brain
  • EEG
  • fMRI
  • emotions
  • stimuli
  • neuroscience

1. Introduction

The study of emotions deals with the physiological and psychological correlates of subjective experiences that are evident to conscious human beings. Emotions are present and influence our lives and even our perception of reality, making the scientific approach to their study, which has only begun in relatively recent decades, very difficult.

According to [1], the pseudoscience of phrenology brought the critical idea of the physical distribution of psychological functions in the brain, opening the door to modern neuroscience that has largely corroborated this assumption.

It is widely assumed that emotions are the subjective representations of naturally evolved primarily neural circuits and functions that helped surviving since the very first complex animals [2, 3]. This has two main consequences: on the one hand, the physical localization of emotional circuits is hidden in the ancient brain (the limbic system, the amygdalae, and other inner regions). On the other hand, these regions are largely connected to more developed areas, such as the cortex or the cerebellum. Therefore, not only should external stimuli trigger automatic motor responses, but cognitive information can be critical as well as a “brake” on these autonomous reactions (implemented in the cerebellum) and can produce more flexible and adaptive responses.

Although much research in this field focuses on damaged brains, this review covers the healthy brain that responds to emotional stimuli under laboratory conditions.

1.1 History

The connection between the physical processes of the brain and its biomarkers has been assumed since the late 19th century [4].

In 1929, German psychiatrist Hans Berger developed the novel method of Electro-Encephalography (EEG), opening a disruptive and scientific way of studying the processes of the living brain. Although a vast and unexplored field was opened, the first results using EEG to measure emotions did not occur until the 1960s [5]. However, interest in emotional studies still had to wait some years, till the mid-70s’ when some researches began to appear [6, 7].

Since then, the same basic experimental setup has been replicated in research until today: a subject connected to the EEG, or to new tomography technologies (as in [8]), is exposed to different stimuli while his brain activity is recorded.

Figure 1 shows the historic timeline developing this research field.

Figure 1.

A chronology of major events associated with the development of human brain imaging, from [9], adapted with permission.

Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI), two new techniques to access information inside the brain, and not only at scalp level, were developed in 1975 and 1979 respectively, and began to yield significant results in the 1980s (see, for example, [10]).

1.2 Brain atlases and areas’ references

The brain began to be mapped according to its anatomical differences in 1909 by Brodmann [11], who defined 52 regions that modern neuroscience considers extraordinarily accurate for those years. In fact, today most neuroscientific works still provide Brodmann’s nomenclature to specify the areas of activation.

However, it has been shown that it is not precise enough to evaluate some functional characteristics of the brain, so the Montreal Neurological Institute proposed in the 1990s a more modern division of the human brain [12], with 1 mm3 templates organized in a system of coordinates (X, Y, Z). Today, this brain atlas is considered to be a standard.

The main regions in the brain are depicted in the Figure 2.

Figure 2.

Basic brain anatomy. 1: Brain stem. 2: Limbic system. 3: Cerebellum. 4: Cerebrum. 5: Occipital lobe.6: Temporal lobe. 7: Parietal lobe. 8: Frontal lobe.

1.3 Emotional maps

Emotions are subjective feelings, but they must be quantified in some way to allow a methodical study. Since the 1980s, there have been two main approaches in the field of emotion research: the categorical approach and the dimensional approach.

The dimensional approach considers that emotions are organized along a few psychological dimensions. Step by step, a consensus was established on the representation of emotions around a two-dimensional plane, shown in Figure 3.

Figure 3.

Emotional map from [13], adapted with permission.

The debate about the separation of the emotional features of valence and arousal flows over the correlation of these two variables. Barret, for example, found weak correlation between them [14], and Lang supports this idea, inferring that some neural circuits are similarly engaged by motivationally relevant cues independently of the valence, while there may be some other hedonic circuits to discriminate valence [3, 15]. However, other researchers have found contradictory results [16], specifically with respect to valence and arousal of negative stimuli.

This paradigm presents another issue since Miller’s studies [17]. It seems that there is a distortion in the linearity of this space: a negativity bias (for equal amount of positive or negative stimulus, the negative one produces higher responses) and a positive offset (in neutral scenarios, there is a predisposition to appetitive responses).

Combinations of different scales have been used to provide representational spaces with more dimensions and, allegedly, higher accuracy [16]. Examples of these rating scales are the Bivariate Evaluation and Ambivalent Measures (BEAM), described in [18] and the three-dimensional space proposed in [19], which distinguishes between tension arousal and energy arousal.

As an example of categorical approach, we can find the Self-Assessment Manikin (SAM): SAM is a non-verbal pictorial assessment technique that directly measures the valence, arousal, and dominance associated with a person’s affective reaction to a wide variety of stimuli [20].

There is an important body of evidence supporting cross-cultural stability in the perception of emotions [21, 22], and the pleasant-unpleasant dimension seems to exists in all cultures [23]. Ekman considers a few categories of innate and universal emotions (happiness, sadness, anger, fear and disgust) from which all other emotions can be derived [24]. The categorical approach that considers some discrete emotional maps related to basic adaptive problems, has been shown to be cross-cultural or even cross-species (for a review, please refer to [25]).

1.4 Emotional stimuli

As stated in the History section, the setup of most of neuroscientific experiments involves stimuli to elicit emotions (or any other response) in the subject under study.

In addition, different stimuli trigger different and specific areas of the brain, so the choice of stimuli is crucial for the information expected to be retrieved from the experiment.

We will present the most used ones and some points about their effectiveness.

1.4.1 Emotion elicitation techniques

Out of a total of 248 articles, al-Nafjan gathers the type of stimulus used in Table 1.

TechniqueNumber of ArticlesDomain (Medical, Non-Medical)
Visual-based elicitation using images8826%, 73.9%
Prepared task4325.6%, 47.4%
Audio-visual elicitation using short film video clips3818.4%, 81.6%
Audio-based elicitation using music2917.2%, 82.8%
Multiple techniques1926.3%, 73.9%
Other1711.7%, 88.2%
Imagination techniques/memory recall1020%, 80%
Social interactions425%, 75%

Table 1.

Emotional stimuli used according to their nature, from [26].

Why using images?

Psychologists have already shown that images have strong effects on the emotions of human beings [16]. An important result found in the literature states that simple images generate better emotional responses than complex scenes [3, 27].

Please refer to [28] for a deeper review.

Why using music?

The role of music in producing emotional responses is widely accepted and is one of its defining features [29]. Moreover, this has been proven to be cross-cultural [30, 31], making it a very stable and reliable way to provoke emotions in subjects.

When using audio-visual stimuli, it is important to take into account the predominance of image over sound [32], in case of ambiguity or emotional conflict.

Why using words?

It is well known that the brain dedicates exclusive resources, organized hierarchically, to word and language processing, such as the areas of Broca and Wernicke, which demonstrates their importance in evolution and survival. It has subsequently been found that words and language stimuli function as emotional triggers [33, 34].


Among the “other” stimuli in Table 1, researchers have used olfactory [35, 36] or food [37] stimulation, for example.

1.4.2 Databases

To ease the replication, comparison and contrast of theories and results, many research institutions and authors have developed databases with normalized emotional stimuli, which are publicly available. They have labeled stimuli according to different paradigms, and they have been tested. Some of the most used ones are the following:

  • Surrey Audio-Visual Expressed Emotion (SAVEE) Database: Audio-visual clips with male actors in different emotions [38].

  • International Affective Picture System (IAPS): This database offers a “large set of standardized, emotionally-evocative, internationally-accessible, color photographs that includes contents across a wide range of semantic categories” [39].

  • International Affective Digital Sounds (IADS): The same institution and researchers have published the International Affective Digitized Sound system (IADS), with similar structure, labeling and testing parameters [40].

  • Affective Norms for English Words (ANEW): The word-based version of the previous couple of databases [41].

  • Affective Norms for English Text (ANET): In the case of using text extracts, this database “provides normative ratings of emotion (pleasure, arousal, dominance) for a large set of brief texts in the English language for use in experimental investigations of emotion and attention” [42].

  • The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): This is a multimodal database of emotional speech and songs, labeled following a discrete emotional space, with neutral stimuli included [43].

  • The Montreal Affective Voices (MAV) consist of a set of short vocal interjections expressing anger, disgust, fear, pain, sadness, surprise, happiness, sensual pleasure, and neutrality [44].

As we have seen, the databases cover language, images, sounds and combinations thereof.


2. Methods

Nowadays, almost all neuroscientific studies and findings are based on two non-invasive, biomarkers-free technologies: Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI).

2.1 EEG

The EEG is based on the evidence that massive clusters of neurons fire at the same time when they work synchronized, producing tiny voltage changes around them (in the range of millivolts to microvolts). The EEG can be measured directly on the surface of the brain surface and scalp. In both cases, the system has the following elements:

  • Electrodes: conductive elements sensitive to voltage variations.

  • Amplifier: low-noise, band-filtered amplifier to scale the small voltage measured.

  • Register: Analog or (nowadays) digital recording of the transformed signals, together with time stamps, position information and other contextual information.

Important advantages of this technique are its setup (some equipment is portable) and its price (actually, the cheapest of the techniques exposed here). In the scalp version, it is non-invasive and very safe for the subject.

The main advantage of this technique is the time resolution, around the millisecond, which measures very rapid changes in scalp potentials. Because of this, only the EEG technique allows phase measurements, synchronization computations, spectral analysis, or other time-related processing.

In the EEG, the first representational information found was the so-called Event-Related Potentials (ERPs), signals produced as a reaction to a stimulus, typically within a few hundred milliseconds to several seconds. These signals have proven to be stable between users and experiments, and some of them, as the P300 (positive peak 300 ms after stimulus onset), are universally known and used.

The EEG allows frequency analysis, and its signals can be transformed into bands. The spectral power (also called Power Spectral Density -PSD-), i.e., the amount of energy in each band-, can be used to obtain important information from the raw EEG data.

Since 1977 [45], researchers have proposed a new approach, combining the ERPs and the bands, called Event-Related Synchronization (ERS) and Desynchronization (ERD), to measure instantaneous responses to stimuli in specific bands, as shown in Figure 4.

Figure 4.

ERD/ERS detection, from [46], adapted with permission.

The main counterparts of this technique are the volume conductance effect that makes it difficult to locate internal potential sources [47], and the limitation of recording only signals on the surface of the brain (if no electrodes are placed inside the brain), which also limits the measurement of internal sources. To partially address this limitation, some novel techniques recreate inner sources from their fingerprint on the scalp voltage through complex algorithms, such as the Low Resolution Electromagnetic Tomography (LORETA) first proposed in [48], and other “reverse problem methods” summarized in [49, 50].

In addition, EEG registers suffer from artifacts (from electrical power networks, lighting, muscle movements …) that must be removed or filtered out before the recordings can be interpreted. Although some automatic approaches have been proposed, this is a craft task that many researchers still perform manually. Another source of noise is the impedance of the electrodes (as they are transductors between the scalp and the wires), which must be kept low enough to accurately measure extremely low scalp voltages, typically below 50 Ω. This requires the application of a conductive gel, cleaning with electrodes with alcohol, washing the hair before the experiment, etc.

Finally, the EEG system, as a voltage recorder, needs a reference. This does not change the relative voltage distribution on the scalp, but depending on the choice, it can lead to different absolute measures.

Another important process of standardization of EEG measurements has been the definition of electrode positions, which must be constant between studies to allow replication and falsifiability. Depending on the number of electrodes, different standard configurations are defined, the most used being, in the case of 32, the 10–20 International configuration of Figure 5.

Figure 5.

The 10–20 international EEG configuration.

The names and positions of the electrodes are defined in this standard and applied elsewhere. For a larger number of electrodes some other standards can be found (see, for example, [51]).

The correlation of EEG signals with emotions is well established, as stated in [26]: “ We found that the majority of the 130 articles used event-related potentials, whereas 48 articles used Frontal EEG asymmetry in their analysis, six articles used event-related desynchronization/synchronization, and four articles used steady-state visually evoked potentials”.

2.2 fRMI

fMRI began in the 1980s, and soon produced extremely novel results. fMRI measures differential activations of brain regions [52] according to de-oxy-hemoglobin distribution.

fMRI requires a massive magnet (typically around a few tesla), which makes the setup extremely space demanding and expensive. Besides, it cannot be used with metallic components (nor implanted in the subject’s body) so the presentation of the stimuli must be deviated with reflective screens, remote speakers, etc.

The temporal resolution of fMRI is poor, in the range of seconds, which makes it useless to record rapid changes or reactions to the stimuli. However, the main positive aspect is the spatial resolution and the real three-dimensionality of the recording, which generates a map of voxels (volumetric units of information) of very few mm3 if a high temporal resolution is not needed (in fact there is a trade-off between these two parameters; for example, for a voxel size of 3x3x5mm3, the sampling rate falls to about 2 s [53]). Unlike the EEG registering technique, fMRI has the difficulty of mapping different brains (of different participants) in a canonical brain in which the activations and regions can be represented. This forces a spatial transformation to standard geometries that implies loses in spatial resolution [53].

The functionality of the MRI is given, among others, by the Blood Oxygen Level Dependent (BOLD) imaging, which measures differences in oxygenated blood flowing through the brain (since oxy-hemoglobin and de-oxy-hemoglobin have different magnetic susceptibility), correlated with neural activation.

BOLD techniques have the temporal limitations of the physiological processes on which they are based (see [53] for more details). In most studies, fMRI data are statistically processed to generate a meaningful representation of changes, in so-called Statistical Parametric Maps (SPM), yielding to images as that shown in Figure 6.

Figure 6.

SPM in which the color of pixels is representative of its p-value and, thus, the statistical significance of its activation or deactivation when two or more tasks are compared. From G.Konstantina, CC BY-SA 4.0, via Wikimedia commons.

2.3 Simultaneous measuring and comparison

Since both EEG and fRMI are based on related physiological processes, it is easy to find correlations between them.

These two non-invasive techniques for exploring the interior of the living brain are not mutually exclusive, and both have advantages and disadvantages. Therefore, both are used in neuroscience research today.

Table 2 summarizes the main characteristics of each one.

TechniqueTemporal ResolutionSpatial ResolutionPortability

Table 2.

Features comparison between EEG and fMRI.

In 1996, Gerloff and others [54] combined fMRI and EEG for the first time to evaluate the co-registration of both techniques applied to the primary motor cortex and the sensory cortex.

Over the past decade, several studies using both techniques have been proposed to, among other, find new large-scale brain networks [55], examine some specific networks [56, 57, 58] or even provide neurofeedback to assist in the regulation of some circuits [59].

Babayan et al. [60] have recently published a large database with combined EEG and fMRI data from 227 healthy participants.

Unfortunately, the joint use of both techniques has its drawbacks: the signal-to-noise ratio (SNR) can be degraded [61] and interferential artifacts can be generated, as shown in [62].

For more in-depth in brain data imaging, please refer to the handbook [63].


3. Measuring emotions

The neuronal correlates of emotions present features and effects in various dimensions that interact in the living brain.

To help understand the results collected in the scientific literature on emotions, we will divide the findings into different categories, although they are mixed and sometimes inseparable.

3.1 Timing

Studies dealing with the temporal signals created by the emotional processing are, most of the time, based on EEG recordings. The reason is, as explained, the temporal resolution of this technique.

Typically, the measurement of an EEG signal follows the scheme of Figure 7.

Figure 7.

Typical EEG signal during a stimulus-based experiment, from [64], adapted with permission.

When recording the brain’s reaction to stimuli, it is important to define a control group or baseline with which to compare activations or deactivations. Koelstra [65] proposes 5 seconds prior to stimuli as such a baseline.

What happens in the reference period is not uninteresting when emotions are studied from a neuroscientific approach. It has been proven that if in this period the index of asymmetry (the difference in the global activation in each hemisphere, calculated as the left over the right) is high, the subject will present a bias towards positive stimuli, and vice versa as far as fear is concerned [66].

After the stimulus onset, Wei defines a time range of [0.5–4] s as the temporal space in which emotional signals appear [13]. This period has been divided by some authors into three sections: Early [400–1100] ms, Middle [1000-3000] ms, Late [3000–5000] ms [32].

For example, it is widely established that a high positive ERP, in the range of 200–300 ms, widely known as P300, is elicited by emotional stimuli (such as emotional words) compared to neutral ones [67, 68, 69, 70, 71]. This ERP appears in the occipital-temporal regions with an arousal-related amplitude (independent of the valence) compared to neutral stimuli [72].

As already mentioned, the emotional response is mediated or modulated by different neural systems. The P300 has proven to be a modulator of emotional processing regardless of valence when presenting emotional versus neutral pictures [5, 67, 73, 74, 75]. These effects were seen in both pleasant and unpleasant pictures [5, 75, 76].

One of the most stable and reliable neural signatures of emotional processing is the so-called Late Positive Potential (LPP), which appears after 1 second of the stimulus presentation, and can be traced for up to 6 seconds in the central-parietal region [77].

It has been shown that LPP appears with both emotional pictures or words, with an amplitude that depends on the arousal intensity [67, 70, 71], being higher in emotional (both positive and negatives) images compared to neutral ones [78] and not habit-forming [67, 79, 80], although it may decrease somewhat with repetition [15]. Another interesting feature of this signal is its ability to appear with very short exposures to visual stimuli (down to 25 ms) [3].

The LPP is independent of the characteristics of the stimuli as realism/symbolism, complexity/simplicity, etc. [27, 67] and therefore very reliable: “The late positive potential evoked by picture stimuli is a reliable, replicable index of their motivational relevance” [3], correlated with the self-reported arousal [3].

For all these reasons, the LPP has been labeled as the “motivational significance” of a stimulus [81].

The LPP has been localized in the central-parietal region, but also in the secondary visual processing sites in the lateral occipital cortex [82] with visual stimuli.

Summarizing the findings of time-analysis of EEG signals correlated with emotional processing, we can say that the P300 and LPP track emotional processes [78]. There is a golden rule that says that valence is processed before arousal [83], since it has been shown that the early ERP components are correlated with valence [28, 84]. In contrast, the long-term ERP components are correlated with the arousal [85, 86, 87].

Hajcak et al. [67] illustrate the temporal and spatial evolution of the different signals.

3.2 Mapping

The fMRI has shown the processing cores in the inner regions of the brain, such as the limbic system. In terms of arousal, it was found that the area of greatest response in the brain is the amygdalae, a couple of little clusters of nuclei belonging to the limbic system in the temporal lobes, on the internal part of the brain. Regarding the role of the amygdalae in emotional processing, survival instincts, memory, etc. Lang et al. found that this area responds to the intensity of emotional stimuli, and has a central role in enhancing sympathetic reactivity to such stimuli [15]. But the amygdalae do not react independently of the valence of the stimuli: the preferred stimuli selectively activated the right amygdala, in relation to aversive ones in some experiments [88, 89].

Valence has also been correlated with specific limbic neural circuits closely connected to the amygdalae: the mesolimbic reward system, in which the nucleus accumbens (NAc) is particularly relevant in the processing of reward and pleasure evoking stimuli (the reward, motivation and addiction circuits) [90, 91]. Another study extends this list of central processing centers to the Ventral Tegmental Area (VTA) and the hypothalamus, working together as a tripartite network that manages the responses to the emotional aspects of music [92]. Another reward network component is the ventral striatum, which, along with the cingulate cortex, has also shown correlations with the arousal of positive emotions when listening to music [93, 94].

The relation between the mesolimbic networks and some frontal regions (as the Orbito-Frontal Cortex (OFC) and the Interior Frontal Cortex (IFC)), more in charge of cognitive processing, has led some researchers to establish a close relationship between “affective” and “cognitive” processing involved in music listening [92]. Another hypothesis is that the interactions between the OFC and the NAc may be related to the control of emotions [95].

Overall, although it belongs to the cognitive cortex, the role of the OFC in the emotional processing is beyond doubt, and is supported by many studies dealing with music [32, 92, 96, 97], images [98, 99] or decisions [95]. Close to the OFC, the IFC has also been considered relevant, producing a bilateral activation when listening to music, according to [92].

Other important cortex cores for emotional processing are the parietal and temporal areas. The centro-parietal area has shown an activation proportional to the arousal of emotional pictures in the first moments (in a range of 300 to 700 ms) [3, 92]. Positive centro-parietal signals [300–6000 ms] have shown valence independence [3]. Furthermore, the link between the frontal and right parieto-temporal areas with the arousal of a stimulus has been also established [100].

It is worth mentioning the anterior insula, belonging to the temporal lobe, which has been thoroughly studied and defined as a relay between the limbic (specifically the human mirror neuron system) and the motor system (in the cortex) [31, 101], and may be the physiological support for subjective states, like pain, hunger, heart rate perception or emotional awareness [102, 103, 104].

Early lateralization has been found to be correlated with the valence of sounds [32], and this effect does not exist with neutral sounds. One of the first findings in this field is due to Schwartz [7], who found a lateralization in the brain activity.

Back to the surface of the cortex, there are areas specially engaged in emotional processing, measured with both EEG and fMRI.

In the first case, there is a discussion about which electrodes are the most representative of the undergoing emotional processes. For example, we can find the work of Wei et al., who proposes moving from F1, F2, T3 and T4 to F1, F2, F7 and F8 respectively (shifting the registering area from bilateral front-temporal to pre-frontal medial areas), obtaining much better predictions [13]. This change is also proposed independently (and partially) by Lin et al., stating that fronto-central electrodes are specially relevant when measuring theta asymmetry (F7-F8 and FC3-FC4) as a correlate of arousal [105].

By focusing on synchronizations between different areas, it has been found that there is a phase synchronization between frontal and right temporo-parietal areas depending on the valence and the energetic arousal [106]. In another study [107], a beta-band synchronization was found between the pre-frontal and posterior areas when observing high-arousal images. Finally, unpleasant images caused a phase synchronization in the gamma band according to [108]. Please refer to [109] for further details about synchronization.

We have shown the interactions of the limbic system with the cognitive areas of the brain, in relation to images, sounds or decisions. But we have also found some interactions with other unrelated areas, mainly the motor areas, when empathy in involved. It seems that in the processing of emotions, many different and specialized areas need to interact to account for such a subjective experience. This cross modality has been studied in depth. For example, [32] shows that emotional sounds modulate visual primary cortex (P1). In the same region, relationships have been found between emotional processing cores and visuo-spatial and visuo-motor regions [110], or even premotor regions including the intra-parietal sulcus and the ventral premotor cortex [111]. The ventral premotor and the posterior parietal cortex were elicited during the observation of Classical and Renaissance sculptures suggesting, as Di Dio state, “motor resonance congruent with the implied movements portrayed in the sculptures.” [88].

For a final summary, please refer to Table 1 in [87], which provides a detailed review of the EEG spatial correlates of emotions.


4. Final considerations and conclusions

It has been shown how the way we react to emotional situations depends on very different and scattered areas in the brain. Both initial reactions and the later dependencies lay on different systems [112]. Reward calculation and empathy have been identified as being involved in fear or appetite reactions, and are also the most primitive, but they interact with more evolved areas of the cortex that deal with visual and auditory processing, decision making and even motor activation.

Many aspects of the emotional brain remain open. For instance, global or synchronized processing networks beyond the cortical surface have yet to be described, as the limitations of fMRI and non-invasive EEG do not allow this unknown field to be addressed.

Furthermore, it is not clear whether and how gender affects emotional processing. Although responses to musical stimuli, recorded with EEG, have shown no significant gender differences in brain frontal regions [100], gender differences have been observed during verbal learning tasks [113], in emotional networks in adolescents [114] and with esthetic stimuli producing bilateral parietal activation in women, but lateralized in men [115].

Other limitations are due to the stimuli used. Real life involves interaction with various external and internal sources of information that modulate our feelings, and laboratory conditions barely address such complex situations.

This branch of neuroscience is not new (compared to neuroscience itself), but still presents many open doors to be explored.

The way emotions mobilize resources in the brain seems to be large and deep, and many other functions (such as memory) depend on it, showing their pre-eminent position to survive and behave in human (and many other animals’) life.


  1. 1. A. Damasio, “Descartes Error And The Future Of Human Life,” Scientific American, vol. 271, no. 4, pp. 144-144, Oct 1994, 1994
  2. 2. N. Frijda, “The Current Status Of Emotion Theory,” Bulletin of the British Psychological Society, vol. 39, pp. A75-A75, May 1986, 1986
  3. 3. P. Lang, and M. Bradley, “Emotion and the motivational brain,” Biological Psychology, vol. 84, no. 3, pp. 437-450, JUL 2010, 2010
  4. 4. C. S. Roy, and C. S. Sherrington, “On the Regulation of the Blood-supply of the Brain,” J. Physiol., vol. Jan. 11, no. 1-2, pp. 85-158.17., 1890
  5. 5. K. Lifshitz, “The Averaged Evoked Cortical Response To Complex Visual Stimuli,” Psychophysiology, vol. 3, no. 1, pp. 55-68, 1966
  6. 6. Z. Drohocki, “Effect Of Emotion On Amplitude Spectrogram Of EEG,” Electroencephalography and Clinical Neurophysiology, vol. 36, no. 4, pp. 426-426, 1974, 1974
  7. 7. G. Schwartz, R. Davidson, And F. Maer, “Right Hemisphere Lateralization For Emotion In Human Brain - Interactions With Cognition,” Science, vol. 190, no. 4211, pp. 286-288, 1975, 1975
  8. 8. S. Petersen, P. Fox, M. Posner, M. Mintun, And M. Raichle, “Positron Emission Tomographic Studies Of The Cortical Anatomy Of Single-Word Processing,” Nature, vol. 331, no. 6157, pp. 585-589, Feb 18 1988, 1988
  9. 9. M. Raichle, “A brief history of human brain mapping,” Trends in Neurosciences, vol. 32, no. 2, pp. 118-126, FEB 2009, 2009
  10. 10. G. Bradac, W. Schorner, A. Bender, And R. Felix, “MRI (NMR) IN THE Diagnosis Of Brain-Stem Tumors,” Neuroradiology, vol. 27, no. 3, pp. 208-213, 1985, 1985
  11. 11. K. Brodmann, Vergleichende Lokalisationslehre der Großhirnrinde : in ihren Prinzipien dargestellt auf Grund des Zellenbaues, Leipzig: Barth, 1909
  12. 12. A. Evans, M. Kamber, D. Collins, D. Macdonald, S. Shorvon, F. Andermann, G. Bydder, H. Stefan, And D. Fish, “An MRI-Based Probabilistic Atlas Of Neuroanatomy,” Magnetic Resonance Scanning and Epilepsy, vol. 264, pp. 263-274, 1994, 1994
  13. 13. Y. Wei, Y. Wu, and J. Tudor, “A real-time wearable emotion detection headband based on EEG measurement,” Sensors and Actuators a-Physical, vol. 263, pp. 614-621, AUG 15 2017, 2017
  14. 14. L. Barrett, and J. Russell, “Independence and bipolarity in the structure of current affect,” Journal of Personality and Social Psychology, vol. 74, no. 4, pp. 967-984, Apr 1998, 1998
  15. 15. P. Lang, M. Bradley, B. Cuthbert, R. Simons, and M. Balaban, “Motivated attention: Affect, activation, and action,” Attention and Orienting: Sensory and Motivational Processes, pp. 97-135, 1997, 1997
  16. 16. T. Ito, J. Cacioppo, and P. Lang, “Eliciting affect using the international affective picture system: Trajectories through evaluative space,” Personality and Social Psychology Bulletin, vol. 24, no. 8, pp. 855-879, AUG 1998, 1998
  17. 17. N. E. Miller, "Liberalization of the basic S-R concepts: Extensions to conflict behavior, motivation and social learning," Psychology: a study of a science, S. Kock, ed., pp. 198-292, New York: McGraw-Hill, 1959
  18. 18. J. T. Cacioppo, W. L. Gardner, and G. G. Bernston, “Attitudes and evaluative space: Beyond bipolar conceptualizations and measures,” Personality and Social Psychology Review, vol. 1, pp. 3-25, 1997
  19. 19. U. Schimmack, and A. Grob, “Dimensional models of core affect: a quantitative comparison by means of structural equation modeling,” European Journal of Personality, vol. 14, no. 4, pp. 325-345, 2000
  20. 20. M. Bradley, And P. Lang, “Measuring Emotion - The Self-Assessment Mannequin And The Semantic Differential,” Journal of Behavior Therapy and Experimental Psychiatry, vol. 25, no. 1, pp. 49-59, MAR 1994, 1994
  21. 21. P. Ekman, W. Friesen, M. Osullivan, A. Chan, I. Diacoyannitarlatzis, K. Heider, R. Krause, W. Lecompte, T. Pitcairn, P. Riccibitti, K. Scherer, M. Tomita, And A. Tzavaras, “Universals And Cultural-Differences In The Judgments Of Facial Expressions Of Emotion,” Journal Of Personality And Social Psychology, Vol. 53, No. 4, Pp. 712-717, Oct 1987, 1987
  22. 22. P. Ekman, and W. V. Friesen, “Constants Across Cultures in the Face and Emotion,” Journal of Personality and Social Psychology, vol. 17, no. 2, pp. 124-129., 1971
  23. 23. J. Russell, “Culture And The Categorization Of Emotions,” Psychological Bulletin, Vol. 110, No. 3, Pp. 426-450, Nov 1991, 1991
  24. 24. P. Ekman, “Are There Basic Emotions,” Psychological Review, vol. 99, no. 3, pp. 550-553, Jul 1992, 1992
  25. 25. R. Levenson, “Basic Emotion Questions,” Emotion Review, vol. 3, no. 4, pp. 379-386, Oct 2011, 2011
  26. 26. A. Al-Nafjan, M. Hosny, Y. Al-Ohali, and A. Al-Wabil, “Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review,” Applied Sciences-Basel, vol. 7, no. 12, Dec 2017, 2017
  27. 27. M. Bradley, S. Hamby, A. Low, and P. Lang, “Brain potentials in perception: Picture complexity and emotional arousal,” Psychophysiology, vol. 44, no. 3, pp. 364-373, May 2007, 2007
  28. 28. J. Olofsson, S. Nordin, H. Sequeira, and J. Polich, “Affective picture processing: An integrative review of ERP findings,” Biological Psychology, vol. 77, no. 3, pp. 247-265, Mar 2008, 2008
  29. 29. A. Gabrielsson, "Emotions in strong experiences with music.," Music and emotion: Theory and research, P. Juslin, Sloboda, J.A., ed., pp. 431-449, Oxford, UK: Oxford University Press, 2001
  30. 30. L. Balkwill, and W. Thompson, “A cross-cultural investigation of the perception of emotion in music: Psychophysical and cultural cues,” Music Perception, vol. 17, no. 1, pp. 43-64, Fal 1999, 1999
  31. 31. I. Molnar-Szakacs, and K. Overy, “Music and mirror neurons: from motion to 'e'motion,” Social Cognitive and Affective Neuroscience, vol. 1, no. 3, pp. 235-241, Dec 2006, 2006
  32. 32. D. Brown, and J. Cavanagh, “The sound and the fury: Late positive potential is sensitive to sound affect,” Psychophysiology, vol. 54, no. 12, pp. 1812-1825, Dec 2017, 2017
  33. 33. L. Thomas, and K. LaBar, “Emotional arousal enhances word repetition priming,” Cognition & Emotion, vol. 19, no. 7, pp. 1027-1047, Nov 2005, 2005
  34. 34. M. Zhang, Y. Ge, C. Kang, T. Guo, and D. Peng, “ERP evidence for the contribution of meaning complexity underlying emotional word processing,” Journal of Neurolinguistics, vol. 45, pp. 110-118, Feb 2018, 2018
  35. 35. J. Kline, G. Blackhart, K. Woodward, S. Williams, and G. Schwartz, “Anterior electroencephalographic asymmetry changes in elderly women in response to a pleasant and an unpleasant odor,” Biological Psychology, vol. 52, no. 3, pp. 241-250, Apr 2000, 2000
  36. 36. E. Kroupi, J. Vesin, and T. Ebrahimi, “Subject-Independent Odor Pleasantness Classification Using Brain and Peripheral Signals,” IEEE Transactions on Affective Computing, vol. 7, no. 4, pp. 422-434, Oct-Dec 2016, 2016
  37. 37. A. Novosel, N. Lackner, H. Unterrainer, M. Dunitz-Scheer, P. Scheer, S. Wallner-Liebmann, and C. Neuper, “Motivational processing of food cues in anorexia nervosa: a pilot study,” Eating and Weight Disorders-Studies on Anorexia Bulimia and Obesity, vol. 19, no. 2, pp. 169-175, Jun 2014, 2014
  38. 38. U. o. Surrey. "Surrey Audio-Visual Expressed Emotion (SAVEE) database,"
  39. 39. P. J. Lang, M. M. Bradley, and B. N. Cuthbert, International affective picture system (IAPS): Affective ratings of pictures and instruction manual. Technical Report A-8., University ofFlorida, Gainesville, FL., 2008
  40. 40. M. M. Bradley, and P. J. Lang, The International Affective Digitized Sounds (2nd Edition; IADS-2): Affective ratings of sounds and instruction manual. Technical report B-3., University of Florida, Gainesville, Fl., 2007
  41. 41. M. M. Bradley, and P. J. Lang, Affective Norms for English Words (ANEW): Instruction manual and affective ratings. Technical Report C-3., UF Center for the Study of Emotion and Attention. Gainesville, FL., 2017
  42. 42. M. M. Bradley, and P. J. Lang, Affective Norms for English Text (ANET): Affective ratings of text and instruction manual. (Tech. Rep. No. D-1). University of Florida, Gainesville, FL., 2007
  43. 43. S. Livingstone, and F. Russo, “The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English,” Plos One, vol. 13, no. 5, May 16 2018, 2018
  44. 44. S. Paquette, I. Peretz, and B. Pascal, “Can We Dissociate The Musical Emotional Pathway From The Vocal One?,” Journal of Cognitive Neuroscience, pp. 214-214, 2013, 2013
  45. 45. G. Pfurtscheller, And A. Aranibar, “Event-Related Cortical Desynchronization Detected By Power Measurements Of Scalp EEG,” Electroencephalography and Clinical Neurophysiology, vol. 42, no. 6, pp. 817-826, 1977, 1977
  46. 46. G. Pfurtscheller, and F. da Silva, “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clinical Neurophysiology, vol. 110, no. 11, pp. 1842-1857, Nov 1999, 1999
  47. 47. S. van den Broek, F. Reinders, M. Donderwinkel, and M. Peters, “Volume conduction effects in EEG and MEG,” Electroencephalography and Clinical Neurophysiology, vol. 106, no. 6, pp. 522-534, JUN 1998, 1998
  48. 48. R. Pascualmarqui, C. Michel, And D. Lehmann, “Low-Resolution Electromagnetic Tomography - A New Method For Localizing Electrical-Activity In The Brain,” International Journal of Psychophysiology, vol. 18, no. 1, pp. 49-65, Oct 1994, 1994
  49. 49. R. Khemakhem, W. Zouch, A. Ben Hamida, A. Taleb-Ahmed, and I. Feki, “EEG Source Localization Using the Inverse Problem Methods,” International Journal of Computer Science and Network Security, vol. 9, no. 4, pp. 408-415, Apr 30 2009, 2009
  50. 50. C. Michel, M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. de Peralta, “EEG source imaging,” Clinical Neurophysiology, vol. 115, no. 10, pp. 2195-2222, Oct 2004, 2004
  51. 51. A. Neuro. "Electrodes Layout," 06/06/2019;
  52. 52. A. Cohen, D. Fair, N. Dosenbach, F. Miezin, D. Dierker, D. Van Essen, B. Schlaggar, and S. Petersen, “Defining functional areas in individual human brains using resting functional connectivity MRI,” Neuroimage, vol. 41, no. 1, pp. 45-57, May 15 2008, 2008
  53. 53. M. Lindquist, “The Statistical Analysis of fMRI Data,” Statistical Science, vol. 23, no. 4, pp. 439-464, Nov 2008, 2008
  54. 54. C. Gerloff, W. Grodd, E. Altenmuller, R. Kolb, T. Naegele, U. Klose, K. Voigt, and J. Dichgans, “Coregistration of EEG and fMRI in a simple motor task,” Human Brain Mapping, vol. 4, no. 3, pp. 199-209, 1996, 1996
  55. 55. R. Labounek, D. Bridwell, R. Marecek, M. Lamos, M. Mikl, P. Bednarik, J. Bastinec, T. Slavicek, P. Hlustik, M. Brazdil, and J. Jan, “EEG spatiospectral patterns and their link to fMRI BOLD signal via variable hemodynamic response functions,” Journal of Neuroscience Methods, vol. 318, pp. 34-46, Apr 15 2019, 2019
  56. 56. V. Salmela, E. Salo, J. Salmi, and K. Alho, “Spatiotemporal Dynamics of Attention Networks Revealed by Representational Similarity Analysis of EEG and fMRI,” Cerebral Cortex, vol. 28, no. 2, pp. 549-560, FEB 2018, 2018
  57. 57. R. Ahmad, A. Malik, N. Kamel, F. Reza, H. Amin, and M. Hussain, “Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach,” Technology and Health Care, vol. 25, no. 3, pp. 471-485, 2017, 2017
  58. 58. Q . Guo, T. Zhou, W. Li, L. Dong, S. Wang, and L. Zou, “Single-trial EEG-informed fMRI analysis of emotional decision problems in hot executive function,” Brain and Behavior, vol. 7, no. 7, Jul 2017, 2017
  59. 59. J. Bodurka, “Amygdala Emotional Regulation Training With Real-Time fMRI Neurofeedback and Concurrent EEG Recordings,” Biological Psychiatry, vol. 83, no. 9, pp. S58-S58, May 1 2018, 2018
  60. 60. A. Babayan, M. Erbey, D. Kumral, J. Reinelt, A. Reiter, J. Roebbig, H. Schaare, M. Uhlig, A. Anwander, P. Bazin, A. Horstmann, L. Lampe, V. Nikulin, H. Okon-Singer, S. Preusser, A. Pampel, C. Rohr, J. Sacher, A. Thoene-Otto, S. Trapp, T. Nierhaus, D. Altmann, K. Arelin, M. Bloechl, E. Bongartz, P. Breig, E. Cesnaite, S. Chen, R. Cozatl, S. Czerwonatis, G. Dambrauskaite, M. Dreyer, J. Enders, M. Engelhardt, M. Fischer, N. Forschack, J. Golchert, L. Golz, C. Guran, S. Hedrich, N. Hentschel, D. Hoffmann, J. Huntenburg, R. Jost, A. Kosatschek, S. Kunzendorf, H. Lammers, M. Lauckner, K. Mahjoory, A. Kanaan, N. Mendes, R. Menger, E. Morino, K. Naethe, J. Neubauer, H. Noyan, S. Oligschlaeger, P. Panczyszyn-Trzewik, D. Poehlchen, N. Putzke, S. Roski, M. Schaller, A. Schieferbein, B. Schlaak, R. Schmidt, K. Gorgolewski, H. Schmidt, A. Schrimpf, S. Stasch, M. Voss, A. Wiedemann, D. Margulies, M. Gaebler, and A. Villringer, “A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults,” Scientific Data, vol. 6, Feb 12 2019, 2019
  61. 61. M. Schrooten, R. Vandenberghe, R. Peeters, and P. Dupont, “Quantitative Analyses Help in Choosing Between Simultaneous vs. Separate EEG and fMRI,” Frontiers in Neuroscience, vol. 12, Jan 10 2019, 2019
  62. 62. R. Abreu, A. Leal, and P. Figueiredo, “EEG-Informed fMRI: A Review of Data Analysis Methods,” Frontiers in Human Neuroscience, vol. 12, Feb 6 2018, 2018
  63. 63. K. Friston, J. Ashburner, S. Kiebel, T. Nichols, and W. Penny, “Statistical Parametric Mapping: The Analysis of Functional Brain Images,” Statistical Parametric Mapping: the Analysis of Functional Brain Images, pp. 1-680, 2007, 2007
  64. 64. iMotions, EEG (Eectroencephalography): The Complete Pocket Guide, 2017
  65. 65. S. Koelstra, C. Muhl, M. Soleymani, J. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt, and I. Patras, “DEAP: A Database for Emotion Analysis Using Physiological Signals,” IEEE Transactions on Affective Computing, vol. 3, no. 1, pp. 18-31, Jan-Mar 2012, 2012
  66. 66. J. Coan, and J. Allen, “Frontal EEG asymmetry as a moderator and mediator of emotion,” Biological Psychology, vol. 67, no. 1-2, pp. 7-49, OCT 2004, 2004
  67. 67. G. Hajcak, A. MacNamara, and D. Olvet, “Event-Related Potentials, Emotion, and Emotion Regulation: An Integrative Review,” Developmental Neuropsychology, vol. 35, no. 2, pp. 129-155, 2010, 2010
  68. 68. L. Carretie, J. Hinojosa, M. Martin-Loeches, F. Mercado, and M. Tapia, “Automatic attention to emotional stimuli: Neural correlates,” Human Brain Mapping, vol. 22, no. 4, pp. 290-299, AUG 2004, 2004
  69. 69. H. Schupp, M. Junghofer, A. Weike, and A. Hamm, “Emotional facilitation of sensory processing in the visual cortex,” Psychological Science, vol. 14, no. 1, pp. 7-13, Jan 2003, 2003
  70. 70. H. Schupp, B. Cuthbert, M. Bradley, C. Hillman, A. Hamm, and P. Lang, “Brain processes in emotional perception: Motivated attention,” Cognition & Emotion, vol. 18, no. 5, pp. 593-611, AUG 2004, 2004
  71. 71. H. Schupp, A. Ohman, M. Junghofer, A. Weike, J. Stockburger, and A. Hamm, “The facilitated processing of threatening faces: An ERP analysis,” Emotion, vol. 4, no. 2, pp. 189-200, Jun 2004, 2004
  72. 72. I. Franken, L. Gootjes, and J. van Strien, “Automatic processing of emotional words during an emotional Stroop task,” Neuroreport, vol. 20, no. 8, pp. 776-781, May 27 2009, 2009
  73. 73. R. Johnson, “For Distinguished Early Career Contribution To Psychophysiology - Award Address, 1985 - A Triarchic Model Of P300 Amplitude,” Psychophysiology, vol. 23, no. 4, pp. 367-384, Jul 1986, 1986
  74. 74. A. Mini, D. Palomba, A. Angrilli, and S. Bravi, “Emotional information processing and visual evoked brain potentials,” Perceptual and Motor Skills, vol. 83, no. 1, pp. 143-152, Aug 1996, 1996
  75. 75. J. Radilova, “The Late Positive Component Of Visual Evoked-Response Sensitive To Emotional Factors,” Activitas Nervosa Superior, pp. 334-337, 1982, 1982
  76. 76. D. Palomba, A. Angrilli, and A. Mini, “Visual evoked potentials, heart rate responses and memory to emotional pictorial stimuli,” International Journal of Psychophysiology, vol. 27, no. 1, pp. 55-67, Jul 1997, 1997
  77. 77. B. Cuthbert, M. Bradley, and P. Lang, “Probing picture perception: Activation and emotion,” Psychophysiology, vol. 33, no. 2, pp. 103-111, Mar 1996, 1996
  78. 78. T. Suo, L. Liu, C. Chen, and E. Zhang, “The Functional Role of Individual-Alpha Based Frontal Asymmetry in the Evaluation of Emotional Pictures: Evidence from Event-Related Potentials,” Frontiers in Psychiatry, vol. 8, Sep 27 2017, 2017
  79. 79. M. Codispoti, V. Ferrari, and M. Bradley, “Repetition and event-related potentials: Distinguishing early and late processes in affective picture perception,” Journal of Cognitive Neuroscience, vol. 19, no. 4, pp. 577-586, Apr 2007, 2007
  80. 80. J. Olofsson, and J. Polich, “Affective visual event-related potentials: Arousal, repetition, and time-on-task,” Biological Psychology, vol. 75, no. 1, pp. 101-108, Apr 2007, 2007
  81. 81. M. Bradley, “Natural selective attention: Orienting and emotion,” Psychophysiology, vol. 46, no. 1, pp. 1-11, Jan 2009, 2009
  82. 82. P. Lang, M. Bradley, J. Fitzsimmons, B. Cuthbert, J. Scott, B. Moulder, and V. Nangia, “Emotional arousal and activation of the visual cortex: An fMRI analysis,” Psychophysiology, vol. 35, no. 2, pp. 199-210, Mar 1998, 1998
  83. 83. S. Moon, and J. Lee, “Implicit Analysis of Perceptual Multimedia Experience Based on Physiological Response: A Review,” IEEE Transactions on Multimedia, vol. 19, no. 2, pp. 340-353, Feb 2017, 2017
  84. 84. L. Gianotti, P. Faber, M. Schuler, R. Pascual-Marqui, K. Kochi, and D. Lehmann, “First valence, then arousal: The temporal dynamics of brain electric activity evoked by emotional stimuli,” Brain Topography, vol. 20, no. 3, pp. 143-156, Mar 2008, 2008
  85. 85. E. Bernat, S. Bunce, and H. Shevrin, “Event-related brain potentials differentiate positive and negative mood adjectives during both supraliminal and subliminal visual processing,” International Journal of Psychophysiology, vol. 42, no. 1, pp. 11-34, Aug 2001, 2001
  86. 86. R. Roschmann, And W. Wittling, “Topographic Brain Mapping Of Emotion-Related Hemisphere Asymmetries,” International Journal of Neuroscience, vol. 63, no. 1-2, pp. 5-16, 1992, 1992
  87. 87. M. Kim, M. Kim, E. Oh, and S. Kim, “A Review on the Computational Methods for Emotional State Estimation from the Human EEG,” Computational and Mathematical Methods in Medicine, 2013, 2013
  88. 88. C. Di Dio, E. Macaluso, and G. Rizzolatti, “The Golden Beauty: Brain Response to Classical and Renaissance Sculptures,” Plos One, vol. 2, no. 11, Nov 21 2007, 2007
  89. 89. E. Phelps, and J. LeDoux, “Contributions of the amygdala to emotion processing: From animal models to human behavior,” Neuron, vol. 48, no. 2, pp. 175-187, OCT 20 2005, 2005
  90. 90. B. Knutson, C. Adams, G. Fong, and D. Hommer, “Anticipation of increasing monetary reward selectively recruits nucleus accumbens,” Journal of Neuroscience, vol. 21, no. 16, pp. art. no.-RC159, Aug 15 2001, 2001
  91. 91. H. Breiter, I. Aharon, D. Kahneman, A. dale, and P. Shizgal, “Functional imaging of neural responses to expectancy and experience of monetary gains and losses,” Neuron, vol. 30, no. 2, pp. 619-639, May 2001, 2001
  92. 92. V. Menon, and D. Levitin, “The rewards of music listening: Response and physiological connectivity of the mesolimbic system,” Neuroimage, vol. 28, no. 1, pp. 175-184, Oct 15 2005, 2005
  93. 93. A. Blood, and R. Zatorre, “Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 20, pp. 11818-11823, Sep 25 2001, 2001
  94. 94. G. Bush, P. Luu, and M. Posner, “Cognitive and emotional influences in anterior cingulate cortex,” Trends in Cognitive Sciences, vol. 4, no. 6, pp. 215-222, Jun 2000, 2000
  95. 95. A. Bechara, H. Damasio, and A. Damasio, “Emotion, decision making and the orbitofrontal cortex,” Cerebral Cortex, vol. 10, no. 3, pp. 295-307, Mar 2000, 2000
  96. 96. S. Koelsch, T. Fritz, K. Schulze, D. Alsop, and G. Schlaug, “Adults and children processing music: An fMR1 study,” Neuroimage, vol. 25, no. 4, pp. 1068-1076, May 1 2005, 2005
  97. 97. S. Koelsch, T. Fritz, D. Von Cramon, K. Muller, and A. Friederici, “Investigating emotion with music: An fMRI study,” Human Brain Mapping, vol. 27, no. 3, pp. 239-250, Mar 2006, 2006
  98. 98. H. Kawabata, and S. Zeki, “Neural correlates of beauty,” Journal of Neurophysiology, vol. 91, no. 4, pp. 1699-1705, Apr 2004, 2004
  99. 99. D. Cinzia, and G. Vittorio, “Neuroaesthetics: a review,” Current Opinion in Neurobiology, vol. 19, no. 6, pp. 682-687, Dec 2009, 2009
  100. 100. L. Schmidt, and L. Trainor, “Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions,” Cognition & Emotion, vol. 15, no. 4, pp. 487-500, Jul 2001, 2001
  101. 101. L. Carr, M. Iacoboni, M. Dubeau, J. Mazziotta, and G. Lenzi, “Neural mechanisms of empathy in humans: A relay from neural systems for imitation to limbic areas,” Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 9, pp. 5497-5502, Apr 29 2003, 2003
  102. 102. A. Craig, “How do you feel? Interoception: the sense of the physiological condition of the body,” Nature Reviews Neuroscience, vol. 3, no. 8, pp. 655-666, Aug 2002, 2002
  103. 103. A. Craig, “Human feelings: why are some more aware than others?,” Trends in Cognitive Sciences, vol. 8, no. 6, pp. 239-241, Jun 2004, 2004
  104. 104. H. Critchley, S. Wiens, P. Rotshtein, A. Ohman, and R. Dolan, “Neural systems supporting interoceptive awareness,” Nature Neuroscience, vol. 7, no. 2, pp. 189-195, Feb 2004, 2004
  105. 105. Y. Lin, Y. Yang, and T. Jung, “Fusion of electroencephalographic dynamics and musical contents for estimating emotional responses in music listening,” Frontiers in Neuroscience, vol. 8, May 1 2014, 2014
  106. 106. M. Wyczesany, S. Grzybowski, R. Barry, J. Kaiser, A. Coenen, and A. Potoczek, “Covariation of EEG Synchronization and Emotional State as Modified by Anxiolytics,” Journal of Clinical Neurophysiology, vol. 28, no. 3, pp. 289-296, Jun 2011, 2011
  107. 107. V. Miskovic, and L. Schmidt, “Cross-regional cortical synchronization during affective image viewing,” Brain Research, vol. 1362, pp. 102-111, Nov 29 2010, 2010
  108. 108. N. martini, D. Menicucci, L. Sebastiani, R. Bedini, A. Pingitore, N. Vanello, M. Milanesi, L. Landini, and A. Gemignani, “The dynamics of EEG gamma responses to unpleasant visual stimuli: From local activity to functional connectivity,” Neuroimage, vol. 60, no. 2, pp. 922-932, Apr 2 2012, 2012
  109. 109. C. Han, J. Lee, J. Lim, Y. Kim, and C. Im, “Global Electroencephalography Synchronization as a New Indicator for Tracking Emotional Changes of a Group of Individuals during Video Watching,” Frontiers in Human Neuroscience, vol. 11, Dec 1 2017, 2017
  110. 110. L. Fogassi, and G. Luppino, “Motor functions of the parietal lobe,” Current Opinion in Neurobiology, vol. 15, no. 6, pp. 626-631, DEC 2005, 2005
  111. 111. T. Jacobsen, R. Schubotz, L. Hofel, and D. Von Cramon, “Brain correlates of aesthetic judgment of beauty,” Neuroimage, vol. 29, no. 1, pp. 276-285, Jan 1 2006, 2006
  112. 112. D. Jackson, C. Mueller, I. Dolski, K. Dalton, J. Nitschke, H. Urry, M. Rosenkranz, C. Ryff, B. Singer, and R. Davidson, “Now you feel it, now you don't: Frontal brain electrical asymmetry and individual differences in emotion regulation,” Psychological Science, vol. 14, no. 6, pp. 612-617, Nov 2003, 2003
  113. 113. S. Edwards, D. Everhart, H. Demaree, and D. Harrison, “Sex-related electroencephalographic differences observed during positive and negative affective verbal learning,” Psychophysiology, vol. 43, pp. S36-S36, 2006, 2006
  114. 114. J. Hardee, L. Cope, E. Munier, R. Welsh, R. Zucker, and M. Heitzeg, “Sex differences in the development of emotion circuitry in adolescents at risk for substance abuse: a longitudinal fMRI study,” Social Cognitive and Affective Neuroscience, vol. 12, no. 6, pp. 965-975, Jun 2017, 2017
  115. 115. C. Cela-Conde, F. Ayala, E. Munar, F. Maestu, M. Nadal, M. Capo, D. del Rio, J. Lopez-Ibor, T. Ortiz, C. Mirasso, and G. Marty, “Sex-related similarities and differences in the neural correlates of beauty,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 10, pp. 3847-3852, Mar 10 2009, 2009

Written By

Pablo Revuelta Sanz, María José Lucía Mulas, Tomás Ortiz, José M. Sánchez Pena and Belén Ruiz-Mezcua

Submitted: November 30th, 2020 Reviewed: December 21st, 2020 Published: January 18th, 2021