The Effectiveness of Neurofeedback and Heart Rate Variability Biofeedback for Individuals With Long-term Post-concussive Symptoms
Phase: NA
Conditions: Post-Concussion Syndrome
Sponsor: Western University, Canada

The Effectiveness of Biofeedback for Individuals With Long-term Post-concussive Symptoms 
NCT03338036 
 
October 1, 2019 
 
Study Protocol: 
1.1 
Participants 
Thirty-one individuals were recruited to participate in this study; which was approved by 
Western University Health Science Research Ethics Board (Appendix A). Participants with PPCS 
had to be 18 years of age or older, have previously suffered a clinically diagnosed concussion, 
completed the BrainEx 90 concussion rehabilitation program, and still experiencing ongoing 
symptoms. They also had to be fluent in English, hold a valid driver’s license, and be capable of 
using hand-held devices. Healthy participants also had to be 18 years of age or older, have not 
suffered a concussion in the last two years, be fluent in English, and hold a valid driver’s license.  
Participants were withdrawn from the study if they were unable to complete the entire 
baseline assessment. This resulted in the loss of seven participants that experienced a 
worsening of symptoms and could not complete the driving simulator task. Therefore, 16 
participants with PPCS were randomized into the intervention and control groups, and eight 
healthy individuals were part of the healthy control group. This resulted in seven participants in 
the intervention group (48.6  13 years old, four females), nine participants in the PPCS control 
group (54.6  7.6 years old, six females), and eight healthy control participants (50.1  15.5 
years old, four females). 
Information about the participants’ PPCS were collected during the baseline assessment. All 
PPCS individuals (intervention and control participants) reported that they continued to 
experience headaches, along with a variety of other symptoms. Seven (of eight) intervention 
participants reported experiencing emotional changes (anxiety, anger, inability to regulate 
emotions), and four (of eight) reported experiencing balance problems. Additionally, three (of 
eight) intervention participants reported experiencing dizziness, light sensitivity, memory 
problems, difficulty focusing, and feelings of overstimulation. Eight (of 10) PPCS controls 
reported experiencing noise sensitivity, six reported experiencing light sensitivity, and five 
reported experiencing emotional changes (anxiety, anger, inability to regulate emotions) and 
balance problems. 
1.2 
Intervention Protocol 
1.2.1  Baseline and Follow-Up Assessment 
Participants were initially contacted via email about interest in the study; their response 
prompted an informational email. They then met with a study investigator at the iMobile 
Research Lab at Western University, where together they reviewed the letter of information. 
Once all questions were answered and they signed the consent form, the baseline assessment 
began. 
The participant was first measured and fitted with a 19-lead EEG cap (Electro Cap International, 
Eaton, Ohio). Each electrode placement corresponded to specific locations on the scalp 
according to the 10-20 International System for electrode placement (80). The leads were then 
filled with a water-soluble conducting gel (Electro-Gel, Electro Cap International, Eaton, Ohio). 
An abrasive gel (NuPrep) was used as skin preparation for the attachment of an electrode to 

each earlobe. Additionally, one electrode was also taped to the participants chest, below the 
left clavicle, for ECG monitoring.  
The participant then completed three individual brain function assessments. First, they sat still 
and silent for five-minutes for eyes open EEG recordings. As sensitivity to light and screens can 
be problematic for individuals suffering PPCS, they were instructed to look at a spot on the wall. 
Afterwards, they had a three-minute resting EEG measurement taken with their eyes-closed. 
Finally, they performed a 10-minute reaction time test, where they pressed a button on a 
handheld toggle when a large blue circle appeared. Before they completed the actual reaction 
time test, they had to pass a pre-test that had the same rules, but provided immediate 
feedback about whether the responses were correct/incorrect in pressing/not pressing the 
button. All of this was completed with the study investigator in the neighboring room so as to 
not distract or stress the participant. There was a one-way window for the study investigator to 
observe the participant, and the participant was informed that they could either wave or call on 
the investigator if they had any questions or issues. 
Brain function assessment was followed by a break. Afterwards, the participant completed the 
RPQ and GAD questionnaires (Appendices B and C), and then proceeded to the driving 
simulation task. This was performed on a CDS-200 DriveSafety  simulator, which includes a 
steering wheel and dash display from a Ford Focus, a gas and brake pedal, and three computer 
screens for viewing. The simulator was adjusted for the participants comfort, ensuring that they 
were the appropriate distance from the screens, and they were comfortable with the height 
and tilt of the steering wheel and distance to the pedals. 
The simulation drive began with three acclimation drives, which are part of an evidence-based 
simulator sickness mitigation protocol in the iMobile laboratory (24). The acclimation drives 
included driving straight down the road at 50 km/hour with no other vehicles on the road, 
driving around a city block involving four consecutive left-hand turns while navigating traffic, 
and lastly driving around a city block involving four consecutive right-hand turns while 
navigating traffic. Between each acclimation drive, participants were screened for symptoms of 
simulator sickness using a modified version of the motion sickness assessment questionnaire 
(81). Participants rated feelings of sweatiness, dizziness, and potential to vomit on a scale of 1 
(not at all) to 10 (severely). Participants also had the option to take breaks between drives as 
needed.  
Finally, participants performed one of two simulator drives. Participants were informed that the 
drive was supposed to simulate driving in the real world, so they were informed that other 
drivers may not obey traffic laws as expected. The two simulator drives contained identical 
elements, but in different orders. Both drives ended at a highway on-ramp, and they were 
instructed to either go towards London or Toronto, depending on which simulation drive they 
were completing. The drive was approximately 10 minutes in length, and included five scripted 
events: an unexpected pedestrian crossing the street in front of the car; a car making a rapid 
lane change in front of the driver; a sudden change in traffic lights from green to yellow (go-no-
go); a way-finding task (appropriately picking the ramp to London or Toronto based on earlier 
instruction); and a car suddenly pulling out of a driveway in front of the participant. As we are 
interested in measures of driving performance that are directly related to safety, we evaluated 
the participant’s responses related to two of the scripted events that involve responding to 
critical roadway information: the unexpected pedestrian crossing the street in front of the car 

and a car suddenly pulling out of a driveway in front of the car. In specific, we quantified the 
participants’ reaction times between the onset of the scripted event (e.g. first appearance of 
the pedestrian) until the participant responded by steering or braking. We also evaluated 
whether the participants were in a collision during their driving simulator task. Involvement in a 
collision ended the driving simulation, which may have prevented the participant from 
completing the pedestrian crossing or car pull-out scripted event. The mean lane deviation for 
the duration of the driving simulation task was also evaluated, using the average deviation from 
the center of the lane.  
After eight weeks, all participants returned to complete another brain function assessment, 
RPQ and GAD, and driving simulator acclimation and drive. The final simulator drive was the 
alternate drive they had not completed in their baseline assessment. For example, if they 
completed Drive 1 in their baseline test, then they would complete Drive 2 in their follow-up 
assessment.  
 
1.2.2  LoRETA Neurofeedback and HRV Biofeedback Intervention 
Participants in the intervention group received an Android tablet (either a Craig 7" 1GB 6.0 
“Marshmallow” Tablet, New York, New York or a Samsung Galaxy Tab A 7’ 8GB Android 5.1 
“Lillipop” Tablet, Seoul, South Korea) and heart rate variability training tool (Evoke Waveband, 
Evoke Neurosciences, New York, New York) upon completion of their initial assessment. They 
were shown how to use the equipment, and instructed that they should perform a HRV 
biofeedback session every morning and night for eight weeks. Each HRV biofeedback session 
involved placing the Waveband just below their elbow, opening the application on their tablet 
(Mindja, Evoke Neurosciences, New York, New York), and doing a 5-minute exercise in which 
they were cued to breathe at their resonant frequency (approximately six breaths per minute; 
82). Points were awarded as their HRV improved. Participants were also provided with a log 
book to record the dates and times of their completed sessions. 
LoRETA neurofeedback is based on measuring EEG signals, comparing them to age-matched 
population norms, and providing feedback to normalize deviant signals. We performed these 
measurements using a 19-lead EEG cap (Evoke Neurosciences, New York, New York). 
Assessments were completed at the iMobile research laboratory at Western University, 
London, Ontario, and interventions were completed in a private room at Parkwood Institute, 
London, Ontario. Each participant in the intervention group was scheduled to participate in 
three sessions per week (usually Mondays, Wednesdays, and Fridays), for 8 consecutive weeks. 
This totaled an expected 24 LoRETA neurofeedback sessions and 112 HRV biofeedback sessions 
(24 of which to be completed with the study investigator during the regularly scheduled 
neurofeedback training sessions). Typically, their sessions were at the same time of day.  
Based on their initial assessment, an individualized LoRETA protocol was developed for each 
participant. This involved identifying the Brodmann areas of the brain and the EEG frequencies 
that were most deviant from age-based normal values, and targeting them for biofeedback. The 
set of Brodmann areas and frequencies were constant for each of the participants throughout 
the study. Each LoRETA neurofeedback session was broken up into 10 exposures of two-minute 
duration, for a total of 20 minutes of training. Participants were instructed to “relax, focus, and 
turn on the green light”, which would appear on a computer screen in front of them. The light 
turned green when the participants were appropriately activating the target cerebral areas at 

the appropriate amplitude. Throughout the duration of the study, as participants achieved 
more success (having the green light on >80% of the time), the stringency of their target (the 
magnitude of the deviation) was reduced, making it more difficult. The goal was to have the 
green light on for 70-80% of the time, creating a balance of reward and challenge.  
Following the 20-minute LoRETA neurofeedback training, participants completed a five-minute 
HRV biofeedback session. The same HRV biofeedback exercise was completed as described 
above (which included a five-minute guided breathing exercise at a rate of approximately six 
breaths per minute). This HRV biofeedback session counted as one of their two daily HRV 
biofeedback sessions, and was recorded in their log books.  
 
1.2.3  EEG Collection and LoRETA Neurofeedback 
EEG was collected using the eVox System (Evoke Neuroscience, New York, New York), which is a 
portable hardware and software system for measuring electrophysiological data and 
performing various biofeedback sessions (surface neurofeedback, LoRETA neurofeedback, and 
HRV biofeedback). The eVox system consisted of a laptop, an amplifier, a response button, and 
a 19-lead EEG cap. When using the system, the cap was placed on the participant so that the 19 
electrodes were situated on the head according to the 10-20 International System for electrode 
placement (80). The cap was then connected to the amplifier, which measured the EEG and 
ECG data and wirelessly transmitted them to the laptop. This setup was utilized for the LoRETA 
neurofeedback and also the EEG data collection.  
 
1.2.4  Driving Simulator Collection 
Performance on the CDS-200 DriveSafety  simulator was collected and stored from the entire 
drive, with metrics collected at 50 Hz. This included vehicle speed, heading, and position within 
the lane. In addition, information was collected during each scripted event (e.g. the unexpected 
pedestrian crossing onto the roadway). Data collected also included metrics such as the 
distance to objects in the scripted events, and activation of the steering wheel, brake and gas 
pedal.  
 
1.3 
Data Analysis 
1.3.1  RPQ and GAD 
Total scores on the GAD for each participant were summed and change from baseline to follow-
up was calculated. Comparisons of this change were analyzed between the intervention, PPCS 
control, and healthy control groups using a one-way analysis of variance (ANOVA). RPQ 
outcomes were tallied as two scores, similarly to previous research (41). The headache, nausea 
and dizziness scores were tallied, and the remaining questions were tallied separately. The 
statistical significance of differences from baseline to follow-up between the three participant 
groups in both RPQ sub scores were assessed using one-way ANOVAs.  
 
1.3.2  Driving Simulation Task 
Three parameters were assessed during the driving simulation, including the reaction time for 
two of the specific scripted events (the unexpected pedestrian crossing and the car pulling out 
in front of the driver’s simulated vehicle). Reaction times were assessed by evaluating the time 
difference between the start of the hazardous event and when the participant applied pressure 

to the brake or suddenly changed their lane deviation (i.e. swerving). Additionally, average lane 
deviation was calculated using the magnitude of deviation from the center of the lane at 50 Hz 
throughout the drive. This was measured in meters, and averaged over the span of the 
participant’s drive. The statistical significance of differences from baseline to follow-up 
between the three participant groups were analyzed using a one-way ANOVA. 
 
1.3.3  HRV and EEG 
HRV was represented by the SDNN parameter (48). SDNN was expressed as a change from 
baseline to follow-up, and analyzed using a one-way ANOVA. 
Participants’ EEG results included the z-scores of the EEG amplitude for frequencies  between 2 
and 30 Hz at all 47 Brodmann areas (83). This yielded a rich data set with a total of 1288 EEG 
parameters per participant. Brodmann areas with the most deviation (12 areas maximum) were 
identified for all participants in the initial assessment, and were the chosen intervention target 
areas. Mean changes from baseline to follow-up within the designated target Brodmann areas, 
at all frequencies (2-30 Hz), were calculated. The statistical significance of differences from 
baseline to follow-up between the three participant groups were analyzed using a one-way 
ANOVA. 
 
Statistical Analysis Plan: 
 
All statistical analyses were performing using commercial software (SPSS 25, IBM Corp., 
Armonk, NY). All one-way ANOVA analyses followed the same protocol. Outliers were assessed 
using boxplots, and identified outliers were considered on a case-by-case basis. Normality of 
the distribution was assessed using a Shapiro-Wilks test. Levene’s statistic was used to evaluate 
homogeneity of variances, and if the threshold for homogeneity of variances was not met, a 
Welch ANOVA and Games-Howell post hoc was used. If the homogeneity of variances 
assumption was met, a Tukey post hoc was used. The threshold for significance was set at p = 
0.05 for all tests. Normality of distribution and homogeneity of variances are assumed unless 
otherwise stated.