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Running Head: EEG ANGER & HAPPINESS RECALL PILOT STUDY Electroencephalographic study of lateralization in anger and happiness brain wave expression prompted by imagery based recall tasks: a pilot study Simon Titone University of Redlands 1

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Page 1: Titone Thesis Final Draft

Running Head: EEG ANGER & HAPPINESS RECALL PILOT STUDY

Electroencephalographic study of lateralization in anger and happiness brain wave

expression prompted by imagery based recall tasks: a pilot study

Simon Titone

University of Redlands

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Abstract:

Electroencephalographic (EEG) analysis of emotional expression in humans

is useful in a variety of capacities and can be used in conjunction with other

processes, such as neurobiofeedback, to provide alleviation to many debilitating

disorders that are suffered globally. A variety of methods of EEG analysis are

available, but knowing the correct approach to the research goal is integral to

reaching successful results. This study records 3-minute epochs of brain wave

activity, during which subjects (n=5) employ imagery recall of a neutral, anger-

provoking, and happiness-provoking imagery based recall period. The results are

then screened for artifacts, and exported as raw power data for each waveform

(alpha, beta, and gamma). The raw waveform power data is then analyzed for

lateralization effect by taking the left -- right hemisphere power values. These

values are then processed in a repeated measures ANOVA to determine if there is a

difference between experimental periods (baseline, neutral recall, anger recall,

happiness recall). 10-point questionnaires were taken after the neutral, anger,

recovery, and happiness period, to determine the efficacy of the period’s intended

effect. The questionnaire data showed that the recall tasks were not entirely

effective in producing the intended emotional effect (Happiness p= 0.068, Anger

p=0.139). There was no significant change between periods in the Beta (p=0.118) or

Alpha (p=0.155) waveforms, but significant change was found in the Gamma

waveform (p=0.043). This data suggests that the Gamma band may provide a more

effective means of measurement of emotional arousal than the Alpha or Beta bands.

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Introduction:

The Electroencephalogram originated in the works of Hans Berger, a German

psychiatrist who published 14 reports on human EEG and cognition, between 1929

and 1938. His work was built from the discoveries made by the English physician

Richard Canton, who observed in 1875 the existence of electrical currents in the

brain (Teplan, 2002). The EEG functions by using electrodes placed on the scalp,

which, through a conductive material, can detect the faint signals that originate from

the neocortex below the skull. Each scalp electrode detects the activity of

approximately one billion cortical cells, while also being distorted by layers of pia

and dura mater (protective tissue found just below the skull) as well as the other

many layers between the cortex and the electrode. The actual resulting data shows

only the difference in valence between excitatory and inhibitory neural activity

(what is left after cancellation between these two types activity) and as such EEG

readings can only account for a fraction of the actual electrocellular activity that

occurs within the brain. EEG readings are possible due to the arrangement of neural

cells in the neocortex, which consists of tightly packed columns, six neurons deep,

aligned perpendicular to the pia mater under the skull. Without this particular

arrangement, the impulses would cancel each other out before it reached the skull,

and we would be unable to perform readings of the activity (Kaiser, 2005).

Brain patterns (the electrical impulses made visible through the EEG) form

wave shapes that are typically sinusoidal, and are often measured from peak to

peak. Brain activity waves range from 0.5 to 100 µvolts in amplitude, and are

translated from complex waveforms into their component waveforms by using

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Fourier transform methods. Brain waves are quantified in five main categories:

Delta (0.5-4 Hz), Theta (4-8 Hz), Alpha (8-13 Hz), Beta (13-30 Hz) and Gamma (30-

60 Hz). Delta waves are present in deep sleep, and correlated to certain pathologies

in the waking adult. Theta waves can be observed in children and infants, and are

typically a sign of abnormal brain behavior if visible in adults. Alpha band waveform

activity present in waking adults, though it is only visible during periods of

relaxation, typically when the eyes are closed. They are most clearly visible in the

occipital region, and their origin is not entirely clear. Beta waves are most clearly

visible in the central and frontal regions of the brain, and are observable in states of

expectancy or tension (Teplan, 2002; Quiroga, 1998). Gamma wave are most often

associated with higher-order brain activity, and have been correlated to the brain’s

wiring of memories (Burke et al., 2012; Jensen, Kaiser, Lachaux, 2007).

Evidence has been presented both for and against using normal EEG

frequency bands for studies of emotional recall tasks. One study that researched

sadness and happiness shows that emotional circuitry activation can be associated

with gamma band activity. The study evoked emotional response through showing

subjects pictures of happy or sad faces, and concluded that evoked-response

desynchronization/synchronization (EDR/EDS) activities in gamma band EEG can

be used to classify happiness and sadness with high time resolution (Li & Lu, 2009).

Another study suggests that regular EEG frequency bands may not be the best for

EEG emotional recognition tasks, which are closely related to emotional states. They

instead suggest a three layer EEG Emotional Recognition (EEG-ER) system, in which

the first layer includes an extraction of a set of spectral powers of different EEG

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frequency bands from multi-channel single-trial EEG signals. In the second layer

they applied the kernel Fisher’s discriminant analysis method, which further

extracted features from the EEG spectral powers, with better discrimination ability.

The third layer sends the feature vector produced in layer two into an imbalanced

quasi-conformal kernel support vector machine, which serves as the emotional

classifier (Liu, et al., 2014).

Event-related potentials (ERPs) are alterations to ongoing EEG activity,

either due to a spontaneous change or as a response to external stimulation, such as

a tone or a light flash. Such externally stimulated changes are also called evoked

potentials (EP) (Quiroga, 1998). While positron emission tomography (PET) and

function magnetic resonance imaging (fMRI) scans can give beneficial insight into

the gross anatomical structure as well as activity in various areas, ERPs are useful

for defining the timing of activity. ERPs are not recognizable in raw EEG data due to

their small amplitudes, so they are extracted by averaging epochs (recording

periods) where repeated external stimuli are presented, and correlated to the

timing in the gross EEG trace to represent a pattern between the EPR and the

stimulus (Teplan 2002). Visual ERPs have been used with positive results in studies

aiming to understand human emotional processes. They provide a means of

triggering an emotional process through the emotional valence of the scene, picture,

or other stimulus. Late positive potential (LPP), a sustained positive component of

the ERP waveform that starts approximately 400-500 ms after the stimulus is

presented, has been shown to have a positive correlation with the emotional

potency of the stimulus (as the stimulus becomes more potent, LPP increases)

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(Sobolewski, 2011). Diminished LPP has been associated with suppression of an

emotional reaction (Moser, Hajcak, Bukay, Simons, 2006).

While ERPs are useful in designating a response to an emotional stimulus,

they require either extremely precise measurement of when the response to the

stimulus occurs, or an external stimulus such as viewing emotional expression in

other faces. This pilot study aims to determine the efficacy of the method of using

imagery recall as a method of endogenously generating emotional response. Since it

is impossible to determine when exactly the neural activation correlated to the

emotion would occur within the experimental period, it is thus necessary to use

extraction of frequency bands as a general measure of activity during the recall

period.

While “train your brain” is a term that has been recently used by companies

such as Lumosity to sell games that supposedly make your mind more keen, EEG

biofeedback has been used for a variety of purposes, from positively changing

emotions by moving into alpha brain states (Stinson & Arthur, 2013), to improving

sustained attention, orientation and executive attention, spatial rotation,

intelligence, mood, well being, and more (Gruzelier 2014). Biofeedback has even

been used to attempt the use of hypnogogia (a hypnosis-like state between waking

and dreaming) for the purposes of increasing creativity both in fields of arts, and

sciences (Gruzelier 2008). Neurofeedback and biofeedback have been used since the

1960’s, but due to a lack of controlled studies and sufficient funding, its results have

been limited. Recently however, the field has shown to be one of the most

efficacious treatments of ADHD in children and adolescents. It has also been used to

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treat disorders such as insomnia, anxiety, depression, addiction, autism, Tourette’s

syndrome, and more (Brandmeyer & Delorme, 2013). These treatments can take the

form of a computer game in which the subject’s brainwaves are manipulating the

movement of an object on the screen, and the subject can attempt to direct the

object using their neural activity, which can improve attention and focus.

Brain-Computer Interface (BCI) is a system in which the subject’s brain

interacts with the computer through recognition of their brainwaves, and

subsequent reaction from the computer. The system is often trained to respond to a

particular brainwave by performing a linked task, and as such the mental process of

imagining one’s hand to move can be reflected by the movement of an object on the

screen (Teplan 2002). Non-invasive BCIs have been shown to provide

communication and control to people with paralysis, with similar results to invasive

BCIs. One study shows results that could allow for people with severe motor

disabilities to use a scalp-EEG to control a robotic arm or neuroprosthesis (Wolpaw,

McFarland, 2004). This study will be using the Emotiv EEG system, which was

originally designed as a BCI, and has programs that allow the user to interact with

the computer using their brainwaves.

Primary investigations into cortical asymmetry were headed by J.

Hallervorden in his 1902 study that investigated the possibility of divergent

emotional expression in the right and left hemi-faces (Kowner, 1994). However, it

wasn’t until the advent of EEG technology that this area of research could be fully

explored. One of the earliest articles that noted lateralization effects of emotion

(Ahern & Schwartz, 1979) used lateral eye movements to determine the

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engagement of the left or right hemisphere when responding to an emotionally

charged question. They found that positive emotional questions evoked movements

suggestive of relative left hemisphere involvement, while negative emotional

questions caused movement suggesting right hemisphere involvement. An EEG

study done around the same time (Harman & Ray, 1976) showed similar results,

with left hemisphere increasing over time for positive affect, and decreasing in

power for negative affect.

In his 1992 article, Davidson investigated numerous effects of cerebral

asymmetry, and discovered that damage to the left hemisphere led to a significantly

increased predisposition to depressive symptoms, especially when the damage was

closer to the frontal pole. Contrastingly, damage to the right hemisphere resulted in

a consequent mania in which depressive characteristics were not seen (Davidson

1992). These results are concurrent with previous studies that suggested left

hemisphere activity being related to positive emotion, and right hemisphere activity

being linked to negative emotion.

A variety of methods can be used to elicit emotional states. While this study

uses imagery to prompt emotional recall, there are many other options to get a

similar result. One study by Egidi and Nusbaum (2012) used the method of as

having subjects listen to a story with a happy or sad ending while being recorded

with an EEG. The results the study offered evidence that it may be more appropriate

use a one-stage model of language and emotional comprehension rather than a two

stage model, where the compositional meaning of a sentence is first constructed and

then integrated with contextual factors. It also further demonstrated the

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lateralization of language and emotional processing, as its results showed that mood

congruence effect is mostly lateralized to the left hemisphere for happy mood, and

to the right for sad mood, showing that the cognitive processes for each are likely to

be different (Egidi, 2012). This provides an important affirmation in regards to the

lateralization effect of emotion, both in endogenously and exogenously generated

contexts.

Other studies used visual recognition of emotions in other faces to elicit an

emotional response in subjects. One previously mentioned study associated gamma

band EEG activity with emotional response (Li & Lu, 2009) A similar study showed

subjects pictures of happy faces, and correlated emotional response to brain

regions. It was noted that the left ventral regions were highly activated by happy

faces when recognizing the expression for its emotional value, while the right

ventral regions were activated when categorizing between happy faces and picture

of hands (Nakamura, Maess, Knosche, Friederici, 2014). This is interesting to

consider when investigating research that claims to record emotional activation in

the brain, as it suggests a separate neural site of activation for emotional recognition

vs. recognition of objects. While my study focuses on the difference in activation

between the two hemispheres, this information is still important to keep in mind, as

it provides a further template for what data could be expected from the

experimental process. This study’s finding of different lateralization effects when

viewing the emotional value of happiness vs. happy faces suggests that the data in

this study may be conflated by the type of emotional recall the subject is engaged in.

If the subject is imagining happy faces rather than the more feeling-based emotional

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experience of happiness, there may be a slight increase in right hemisphere

brainwave activity, which could skew the resulting data.

An alternative method of producing emotional activation is the viewing of

silent color films designed to create emotions in the subject. This study used

positron emission tomography (PET) scans to establish that film-generated

emotional activation (happiness, sadness, and disgust) was noted bilaterally in the

occipito-temporo-parietal cortex, lateral cerebellum, and a region that includes the

anterior temporal cortex, amygdala, and hippocampal formation (Reiman et al.,

1997). Another successful study used the creation of personalized autobiographical

scripts by the subject, which were later read to the subject during the testing period.

This study was also attempting to establish brain regions using PET scans, and

noted significant activation for anger recall in the orbital frontal cortex, superior

temporal gyrus, insula, cerebellum, and middle temporal gyrus. Sadness recall was

associated with the cuneus and caudate regions, and the middle temporal gyrus,

while happiness was noted to cause activation in the ventral striatum, middle,

anterior, and superior temporal gyrus (Marci, Glick, Loh, Dougherty, 2007). This

data opposes the previously established conclusion by Davidson, and suggests that

neural activity during emotion may be less correlated to hemispheric regions, and

instead isolated to distinct brain regions. However, because this experiment differs

in both mechanism of observation (PET vs. EEG), and prompt of emotional recall

(autobiographical script vs. imagery recall) the results may suggest that there are

much more complicated neural mechanisms at work. Because PET is slightly more

invasive than EEG, and autobiographical scripts require auditory attention, the

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study may be influenced by a variety of factors that may not influence this current

EEG study. The results presented in this study are regardless important for EEG

investigations, as they may suggest a reason for data that opposes Davidson’s

theory, or that would inspire a different approach to experimental methods, that

focuses on specific brain regions rather than lateralization effects.

While there are many different options for testing emotional activation in the

brain, it is also important to tailor the method of recall to the method of collecting

the data. In this study, since the data is collected using an EEG, the method of

emotional recall would be best if it did not require other areas of neural activation,

as the visual, and auditory stimuli do.

This pilot EEG study aims to determine lateralization effects of EEG output

response for the emotional states of anger, happiness, and neutral baseline. The data

gathered may be valuable for later studies that use EEG technology, and may

provide further backing for studies that show lateralization in emotional recall

tasks. The hypothesis of the study is that there will be a distinguishable difference in

each frequency band as measured by bi-lateral power differentials, between neutral,

anger, and happiness imagery based emotional recall periods. The null hypothesis is

that there will be no difference in each frequency band between the neutral, anger,

and happiness recall periods.

Methods:

Participants:

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Because this pilot study was classified as an in-class demonstration, and thus

did not involve the IRB board, the participants of this study were limited to the

students in the BIO 460-03 Research Topics in Biology course, in the Fall and Spring

of 2014/15 (n=5, mean age=21). Participants were not recorded or videotaped,

during the experimental process, and only EEG data was taken. Due to equipment

failure the data of one subject’s happiness period was not recorded, but the rest of

their data was included in the study. The subjects were not compensated for their

time, and only verbal consent was received.

Experimental Design:

This study is a interventional, one time assessment study, which uses human

subjects and EEG-based measures to correlate anger, happiness, and emotionally

neutral states to determine the level of alpha, beta and gamma wave activity

throughout imagery-based recall tasks. The independent variable is the imagery

recall type (neutral, anger, and happiness), while the dependent variable is the EEG

data.

The goal of this experiment was to determine an establishing baseline for

anger and happiness emotion readings in EEG data. The hope was that an average

would be found between subjects, and that the data for the neutral, anger, and

happiness tasks would be distinguishable and separate.

Before the EEG was placed on the subject, they were informed that they

would not be videotaped or audio recorded, and oral consent was received. Baseline

tests was taken in which the subject sat silently with their eyes closed. The subject

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was read a prompt for a neutral recall task, which involved using mental imagery to

nonverbally go through the steps that they take to prepare for the day in the

morning, or a similarly monotonous routine in their daily life. This period lasted for

3 minutes, then a questionnaire was given which asked the subject to rate their

current level of happiness and anger on a scale of 1-10.

The subject was then given directions to silently recall the event that

occurred in the past 2 months that caused them to be angry at a level of 7 or above

on a 10 point scale, for 3 minutes. A 10-point questionnaire was given here to

determine the efficacy of the emotional recall.

A 3-minute recovery period was taken after that, during which the subject

listened to beach sounds (track 04-seaside-Ocean Sounds from a Golden Sand

Beach.mp3). No activity was recorded during this period. The beach sound stimulus

was used because a silent recovery period may have allowed for further rumination.

Another 10-point questionnaire was then given to determine how much the subject

had cooled down.

Following the recovery period, the subject then used mental imagery to

remember a situation in the past 2 months that made them feel happy or content at

a 7 or above on a scale from 1 to 10. After the 3-minute happiness epoch, another

10-point questionnaire was administered. The subject was then disconnected from

the EEG, and asked if they had any questions, and permitted to leave.

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Figure 1: Experimental Design

Emotiv Systems:

The EEG system used was Emotiv EPOC (Emotiv Systems, San Francisco), a

commercially available EEG headset with BCI capabilities. The headset has 14

channels (plus two references pads which serve as a ground) and in the 10-20-

location system are named as AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8,

and AF4. The sampling rate for all trials was 128 Hz. The EEG system connected to

the computer through 4.0 LE Bluetooth®. The Emotiv headset attaches felt-based

sensor assemblies that use gold plated contacts to establish connection from the

scalp to the headset (Emotiv EPOC, 2014).

Software:

TestBenchTM was used to record the data in real time, and saved with .csv

format. The Emotiv EPOC package (Emotive EPOC, 2004) includes the TestBenchTM

software, as well as a BCI program called EmotivControlPanel. Analysis was done

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using Vivosense (Vivonetics, San Diego, CA), an application that also provides

analysis for heart rate variability. The fast Fourier transform method was used to

extract power levels from raw EEG waveforms. Data was exported from Vivosense

to Excel (Microsoft, Redmond, WA), where basic calculations were done. Statistical

Package for the Social Sciences (SPSS) was used to provide statistical analysis of the

power level data.

Methods of Analysis

The analyzed data focused on the brain wave formations created during

baseline, anger, happiness, and neutral trials. Data collected focused on alpha, beta,

and gamma wave activation, as those waveforms are most associated with active

thinking. Delta and theta waves were not focused on, as they are typically related

with deep sleep or meditative activity. The power levels from the gamma, beta, and

alpha waves were compared between the neutral, anger, and happiness recall

periods.

Data was imported to Vivosense as a EDF file. Since Vivosense can only

analyze 8 data channels at once, the 14 electrodes from Emotiv EEG system were

uploaded by hemisphere, with 7 “left hemisphere” electrodes (AF3, F7, F3, FC5, T7,

P7, and O1) first being uploaded. Once those were analyzed and exported, the

mirroring 7 electrodes from the right hemisphere (AF4, F8, F4, FC6, T8, P8, and O2)

were uploaded, analyzed and exported.

Artifact isolation was done manually, and every raw waveform from each of

the electrodes was inspected for artifacts. References of artifacts were obtained

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from Practical Approach to Encephalography (Libenson, 2010). Activity was

considered an artifact if it consisted of a spike in data that was present in the same

time and location across all electrodes. Additionally if a spike was more than 1000

mV, it was considered to be an artifact and was removed from the data.

Power differentials were then obtained by calculating the power value of the

left hemisphere electrode - right hemisphere electrode (L-R). This indicated which

hemisphere showed more activation during the period. A positive value indicated

that there was more left hemisphere activation, while a negative value showed more

right hemisphere activation. The values for each electrode pairing power differential

were then averaged and standard deviation was obtained, then those values were

compared to the other subjects. A repeated measures ANOVA was used to determine

the significance level of each frequency band across the four periods, and was done

for each frequency.

To determine the efficacy of the recall task period, the self-administered 10-

point emotional questionnaires were subjected to a one way repeated measures

ANOVA.

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Results:

Happin

ess Q

uestio

nnaire

Anger Q

uestio

nnaire

02468

Averages of 10-point ques-tionnaire responses

NeutralAngerRecoveryHappiness

Figure 2:

No significant difference was found in self-reports between the periods

(Happiness p= 0.068, Anger p=0.139), though the data showed a trend towards

emotional arousal.

Table 1: Power differential (L-R) of one subject with electrodes locations in the 10-20-location system, during the baseline period.

BaselineAlpha Beta Gamma

AF3-AF4 -63.63 -82.59 -17.57F7-F8 -5.26 -12.25 -8.94F3-F4 -40.01 -43.23 -8.41FC5-FC6 -39.65 -40.67 -10.39T7-T8 -2.48 3.75 2.09P7-P8 -115.78 -68.3 -11.26O1-O2 -43.1 -22.09 -3.96Average -44.27 -37.91 -8.35Standard Deviation 38.29 30.55 6.14

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Table 1 shows the variability of the power differential values between

electrodes during a 2-minute baseline period. A very high standard deviation was

found for the power differential data, suggesting that the values vary widely within

the data set. However a comparison between frequency power bands shows the

ratio at which power differentials from certain electrodes are either higher or lower

in value seems to be relatively consistent. The data clearly shows a higher right

hemisphere alpha wave activation in the parietal region (P7-P8), and suggests that

higher left hemisphere activation is found in the frontal (F7-F8) and temporal (T7-

T8) regions. However, as the majority of values are negative, a right-lateralized

effect is still visible, aside from the beta and gamma waves in the T7-T8 regions.

This figure illustrates the significant variance between brain areas in the different

gamma bands.

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Figure 3: Power differential (left – right hemisphere) averages of 7 electrodes in the Alpha frequency for all subjects during baseline and various recall tasks. No significant difference was found between periods for the alpha frequency (p=0.155).

Figure 3 illustrates the differences between subjects in the various

experimental periods, and shows that while there was no significant difference

between the periods in the alpha frequency range, there does appear to be a trend of

increased left hemisphere activity during the imagery recall periods in comparison

to the baseline period, as is especially visible in the total average. Data from subject

2 happiness trial was excluded due to technical failure. The lateralization values

differed visibly between subjects, and while a trend of increased left hemisphere

activity between the baseline and proceeding recall tasks is visible, there are no

conclusive trends between all subjects during recall tasks in the alpha power band.

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-10

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

Average gamma band power differentials in all subjects

Baseline

Neutral

Anger

Happiness

Subject 1 Subject 2 Subject 3 Subject 4 Subject 5 Total Average

Figure 4: Power differential (left – right hemisphere) averages of seven electrodes in the Gamma frequency for five subjects during baseline and various recall tasks. Statistical significance was found between the four periods (p=0.043).

Figure 4 shows that while the standard deviations from each subject are high,

and sometimes exceed the mean score, there is relatively little variance between

subjects. The trend of increased left hemisphere or decreased right hemisphere

activity during the imagery recall tasks that could be observed in Figure 3 is also

present in Figure 4. Beta power differentials were not graphed, as they had no

statistical significance, and similar traits to the alpha band data. Statistical

significance was found between periods in the gamma frequency. Difference is

visible between the baseline and imagery recall tasks, but not between the different

emotional tasks themselves. Paired t-tests show significant difference between

baseline and neutral periods (p= 0.005), baseline and anger (p= 0.025) but not

between baseline and happiness periods (p=0.055).

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Baseline Neutral Anger Happiness

-40.00

-35.00

-30.00

-25.00

-20.00

-15.00

-10.00

-5.00

0.00

AlphaBetaGamma

Figure 5: Average Power Differential During Baseline and Recall Tasks. Significant differences found between periods in the Gamma frequency (p=0.043) but not between periods in the Alpha (p=0.155) or Beta (p=0.118) frequencies.

Figure 5 shows the average power differential values of all subjects in each

recorded EEG period. While it appears as if the power differential is smaller for the

gamma frequency band, this effect is due to the raw values of the gamma

frequencies being smaller. As previously mentioned, alpha band activation is

associated with an eyes-closed relaxed state, beta activity marks anxious or agitated

thought, and gamma activity suggests high order thought, active cognitive

processing, and the process of memory formation.

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Discussion:

Upon analysis of the data, gamma wave power differential appears to have

significant difference between the experimental periods of emotional recall and the

baseline period. This finding is consistent with previous research that has connected

gamma wave activity to awareness (Dressler, 2004) and higher cognitive processing

(Kucewicz et al., 2014). It is likely that the increased gamma activity in the left

hemisphere during the imagery recall tasks is due to increased focus and

concentration.

While gamma power showed significant results in an ANOVA, paired t-tests

show that the most significant difference in gamma power is between the baseline

and neutral periods, suggesting that rather than providing an accurate marker for

emotional reactivity, this experiment instead was measuring how hard the subject

was concentrating. The neutral period was intended to be free of emotional content,

as such the high statistical difference between the baseline and neutral periods

shows that either the neutral period was not as significantly free of emotional

valence, or that the measured difference in gamma power between periods is

instead a marker of focus rather than emotional valence. There are, however, many

factors that could influence the results of this experiment.

The overall general trend of the data from this study suggests little

correlation between the experimental period and change in power differential.

While the small sample size does influence the reliability of the results, this trend

has been found in previous studies. In a meta-analysis of 65 studies involving

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neuroimaging of emotions, it was found that there was no evidence for general

right-lateralization of emotion, and that the left-positive/right-negative

lateralization effect was in fact much more complex and varied in different brain

regions, though it was found that withdrawal/negative emotional activity is mostly

left-lateralized in the limbic system (Wager, Phan, Liberzon, Taylor, 2003). This

finding of high variability between brain areas is consistent with the data in this

research, as the variance between electrodes was very high, with a standard

deviation that was sometimes greater than the values averages.

Since the baseline and recall periods were 2 and 3 minutes respectively, it is

important to question whether the subject would be likely to have kept the recall

task in mind for the whole period, or if their mind would wander. The increased

power differential values in the gamma frequency band suggests that there may be a

generally increased left-hemisphere activation during the periods, but it is possible

that a smaller epoch of analysis may produce more significant results. In a study that

verbally prompted subjects to imagine an event in their own lives that caused an

emotional reaction, activity was recorded for 60 seconds, and separated into 5-

second epochs for analysis (Kostyunina & Kulikov, 1996). The methods of

Kostyunina’s research involved waited for subjects to give verbal conformation that

they were actively engaging in the recall task before recording began, which may

have given more accurate or significant data. The data from this 1996 study also

corroborated previous research that associated change in frontal cortex activation

to emotional experience. Specifically, these researchers found a decrease in the

alpha frequency rhythm in the left frontal lobe in the presence of emotions.

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Running Head: EEG ANGER & HAPPINESS RECALL PILOT STUDY

As this research was designed as a pilot study, there are certain inherent

limitations to the data collected. Primary among the potential sources of errors in

this study is the small sample size. The subjects involved in the study were also

previously made aware of the information that would be gathered, and the process

involved in the study, which may have affected the results. Very limited data was

collected on the subjects before beginning the study, and important variables such

as any medication used, overall stress levels, the time of day in which the study took

place, and others were not considered as co-factors in the data analysis. Numerous

equipment errors may have conflated the data as well. At times electrodes would

not appear to be receiving a full signal from the scalp, and for some subjects (n=2)

there were not enough electrode leads to attach to the headset, and the noise from

those missing electrodes may have affected the overall power levels obtained.

Since both the director of the study and the principal researcher were novice

to the field of EEG artifact analysis, there is a high propensity for error in which

epochs were selected for exclusion. Since the principle investigator was working

alone for the majority of the experimental and analytical process, there is a higher

possibility of human error. The principle investigator also was not experienced in

the field of statistical analysis, which may have led to further error.

Studies regarding emotion are very important, and if proper research is

performed, the data collected can affect and potentially alleviate a myriad of health

disorders that affect large portions of the population. EEG analysis of the brain at

rest may provide information as to particular propensities to feeling certain

emotions more strongly. Davidson found that lower activity in the left hemisphere

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could be a signifier of an increased disposition towards depression (Davidson,

1992). If preemptive investigations into the brain’s behavior at rest could give a sign

of potential future mental illnesses, treatment could start earlier, which would

increase the chance of efficacy. This study was designed keeping in mind the future

studies that may be performed using the same materials, and as such provided a

thorough methods section by which to replicate the process of this study. Eventually

the Emotiv EEG headset may be used for further research at the University of

Redlands, and would provide increased insight into correlations between anger,

mindfulness meditation, and health risk behaviors.

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