Neuroimaging techniques have been and continue to be a dominating avenue of science since their introduction in the late nineteenth century. These techniques have diversified in today’s world, and many different kinds of imaging tools exist, each having its own strengths and weaknesses, such as spatial/temporal resolution, invasiveness, and cost.
Assignment 6: Envisioning the Ideal Brain Imaging Technique
Introduction
Electroencephalography (EEG)
Among these techniques is Electroencephalography (EEG), which is a physiological imaging technique that utilizes electrodes to measure the brain’s electrical activity non-invasively. It possesses very competent temporal resolution (within milliseconds) but does not have great spatial resolution (five to ten centimetres) due to the thickness of the skull and meninges through which the device needs to pass in order to record (Warbrick, 2022). This also means that deeper brain structures cannot always be measured with an EEG since the electrodes will only pick up signals that are so deep.
Functional Near-Infrared Spectroscopy (fNIR)
Another advancement in neuroimaging is Functional Near-Infrared Spectroscopy (fNIR), which is used to detect blood oxygenation changes within the brain non-invasively by utilizing light. By shining light that is near infrared in wavelength, it is possible to detect how much of the light is absorbed by oxygenated and deoxygenated blood, and this difference will tell us which areas of the brain are most active relative to the others. In terms of the capabilities of fNIR, it has decent spatial resolution (around one centimetre) and poor temporal resolution (within seconds) (Li et al., 2022).
Hybrid Imaging Technique
Since these imaging techniques both happen to be non-invasive due to their operation on the outside of the head, they could be compatible in terms of how they are used. The strengths and weaknesses of these imaging techniques are reversed in comparison to one another, and by fusing these techniques into one tool, it may be possible to create an ideal form of brain imaging. The integration of EEG and fNIR together not only seems possible but also beneficial to the overall design of the hybrid device.
Device Design and Functionality
Both techniques work by placing the device attachments on the outside of the patient’s head. For EEG, electrodes are placed on the head, whereas fNIR uses visual sensors called optodes. The sensors can coexist on the head since it would be possible to house each pair of electrodes and optodes in the same capsules, which saves space for additional sensors. These techniques do not interfere with each other when they are in use; this is due to their modes of measurement, where one is measuring electrical signals and the other is measuring optical signals.
Data Synchronization and Visualization
It is possible for the EEG and fNIR to acquire their data simultaneously since they are performing different actions on the brain that do not interfere with each other. They do, however, still differ in their respective temporal resolutions, with EEG having great temporal resolution and fNIR having a much slower temporal resolution. A potential method to make the collection of data closer in time is to set a clock that signals the fNIR to acquire at its normal rate while the EEG collects on a slower rate to better match data across time.
Challenges and Considerations
There are some computational errors that may arise when fusing two imaging techniques into one modality. Both EEG and fNIR gather data and display them as varying amounts of digital activity for the researcher to view and gather information from. Having two datasets feed into one system poses the risk of overloading and stalling the database that collects the imaging results (Ahn and Jun, 2021). There is also the concern of aligning the data collection temporally, as data will be collected at two different rates, and the computer will need to be capable of recognizing this.
Applications and Implications
This application of dual neuroimaging has relevance in research fields that are looking into the relationship of two processes within the brain, and in this case, research that is concerned with the relationship between electrical signals in the brain and the hemodynamic response seen in those same areas. By seeing how these two forms of data imply about a specific part of the brain, researchers can look into a further depth of insight regarding neural activity during events or certain external stimuli acting on the patient.
Conclusion
Overall, a hybridized EEG-fNIR device would benefit both the research and clinical worlds, and it is worth looking into as the domain of neuroimaging is constantly evolving and improving as time goes on.
References
- Ahn, S., & Jun, S. C. (2021). Multi-modal integration of EEG-fNIRS for brain-computer interfaces — current limitations and future directions. Frontiers in Human Neuroscience, 15, 645869. https://doi.org/10.3389/fnhum.2021.645869
- Fazli, S., Mehnert, J., Steinbrink, J., Curio, G., Villringer, A., Müller, K.-R., & Blankertz, B. (2012). Enhanced performance by a hybrid NIRS – EEG brain computer interface. NeuroImage, 59(1), 519–529. https://doi.org/10.1016/j.neuroimage.2011.07.084
- Flanagan, K., & Saikia, M. J. (2023). Consumer-grade electroencephalogram and functional near-infrared spectroscopy neurofeedback technologies for mental health and well-being. Sensors, 23(20), 8482. https://doi.org/10.3390/s23208482
- LaRocco, J., Le, M. D., & Paeng, D.-G. (2020). A systemic review of available low-cost EEG headsets used for drowsiness detection. Frontiers in Neuroinformatics, 14, Article 553352. https://doi.org/10.3389/fninf.2020.553352
- Li, R., Yang, D., Fang, F., Hong, K.-S., Reiss, A. L., & Zhang, Y. (2022). Concurrent fNIRS and EEG for brain function investigation: A systematic, methodology-focused review. Sensors, 22(15), 5865. https://doi.org/10.3390/s22155865
- Muñoz, V., Muñoz-Caracuel, M., Angulo-Ruiz, B. Y., & Gómez, C. M. (2023). Neurovascular coupling during auditory stimulation: Event-related potentials and fNIRS hemodynamic. Brain Structure and Function, 228, 1943–1961. https://doi.org/10.1007/s00429-023-02698-9
- Pinti, P., Aichelburg, C., Gilbert, S., Hamilton, A. F. D., Hirsch, J., Burgess, P. W., & Tachtsidis, I. (2018). The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience. Annals of the New York Academy of Sciences, 1464(1), 5-29. https://doi.org/10.1111/nyas.13948
- Su, W.-C., Dashtestani, H., Miguel, H. O., Condy, E., Buckley, A., Park, S., Perreault, J. B., Nguyen, T., Zeytinoglu, S., Millerhagen, J., Fox, N., & Gandjbakhche, A. (2023). Simultaneous multimodal fNIRS-EEG recordings reveal new insights in neural activity during motor execution, observation, and imagery. Scientific Reports, 13, 31609. https://doi.org/10.1038/s41598-023-31609-5
- Warbrick, T. (2022). Simultaneous EEG – fMRI: What Have we Learned and What Does the Future Hold? Sensors, 22(6), 2262. https://doi.org/10.3390/s22062262
Other Course Work
📊 Assignment 1: EEG Analysis
📄 assignment1_plots - Mohamed Jomha.pdf
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📄 PSYCH 403 Assignment 1 - Mohamed Jomha - Mohamed Jomha.pdf
View Original PDF📝 Terminal Saved Output - Mohamed Jomha.txt
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🎨 Assignment 2: BrainImation
📄 PSYCH 403 Assignment 2 Write Up - Mohamed Jomha - Mohamed Jomha.pdf
View Original PDFMohamed Jomha
10/ 17/ 25
PSYCH 403
Assignment 2 - BrainImation — Create Your Brain - Controlled Animation
For this assignment, I decided to create a brain art visualization to demonstrate EEG data
in a visible way. The visualization I created is an array of randomly moving particles that change
their size and colour depending on the amount of calmness perceived by the recording device. The
particles increase in size in a pulsing fashion and go from blue to purple when the person being
recorded becomes calmer. This calculation is determined from the person’s alpha waves that the
EEG device is picking up. Alpha waves fluctuate in response to a person’s calmness, and that
means we are able to visualize a person’s relaxation state based on this metric. In order to make
this code for the visualization, I first started with the ‘Brain Particles’ sample featured on the
BrainImation website. From there, I utilized ChatGPT to implement and edit the code to increase
particle size and hue on scale based on the alpha wave levels perceived. From there, I tweaked the
values within the code to generate an animation that is of an appropriate size, colour, and speed so
that it was both function al and appealing to look at.
Looking back at the start of this assignment, the biggest challenge I faced was being able
to take the sample code of ‘Brain Particles’ and alter it via the third - party tools. Doing this would
often generate codes that had issues running in BrainImation, a nd when this occurred, it would
show a black screen with no indication as to why the code could not run. Luckily, it did not take
much trial and error to eventually acquire a desirable code, and in the end, I got the finalized
product I had envisioned. Som ething I learned from this assignment was that coded animations
and systems could be implemented in brain imaging applications quite easily, and something like
this, at face value, seemed like a task that would be difficult to execute when in reality it wasn’t.
It did not take a lot of time or understanding to create s omething that actually serves an innovative
and useful purpose. Another thing I learnt was that the EEG measures data that can be used in
numerous ways regarding a person’s state or current thinking. I imagine there will be more
innovations regarding EEG a nd even more ways these ideas can be implemented into technology.
🎥 Screen Recording 2025-10-19 at 2.07.35 AM - Mohamed Jomha.mov
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🎯 Midterm Project
📄 PSYCH 403 Midterm 1 - Mohamed Jomha - Mohamed Jomha.pdf
View Original PDFMohamed Jomha
Student ID: 1782584
PSYCH 403
10 / 20 / 25
PSYCH 403 Midterm 1 - Brain - Computer Interface Innovation & Event - Related Potentials
Part 1: BCI Innovation: Beyond Basic Control
For part one, I decided to take my BCI BrainImation project from assignment two and
build upon that. The project I made for assignment two was a BrainImation project that used the
‘Brain Particles’ sample and with this, I had coded the animation to change in size and hue
depending on the amount of alpha waves registered over time with the orbs increasing in size and
turning purple the calmer a person was. This worked because alpha waves a person emits is
proportional to the amount of calmness they happen to be. The BCI concept I incorporated with
this project is the Target Brain State Training (Neurofeedback) which encourages the user to
work toward achieving the specific brain state of calm by visualizing their progress and change
in real time. T he way this loop works is by altering the animation the user sees as they progress
toward the target goal. The individual particles become larger, turn redder, and move more
smoothly as the user becomes calmer and generates stronger alpha waves. This allows the user to
visibly see their progress as they try to calm themselves. Once the user has reached a state of
calm that surpasses the set target level for alpha waves, I have included a “ding” sound effect
that signals that they have passed the set target level, and this dinging sound plays on a two
second cooldown that increases in pitch the longer the user remains above the target threshold.
This rendition of my BrainImation project is different from basic parameter control in the
way it adds an interactive goal for the user instead of just visualizing their EEG data on the
screen. The animation is giving the user a sense of their progress as they approach the goal and it
is also providing feedback in the changing visualizations seen, the new progress bar that tracks
their completion, and the audio chime that plays when the target h as been met and surpassed.
These visible and audible cues all serve as sensory feedback for the user and help guide their
learning and focus; this was not a feature in the passive visualization from assignment two. One
thing I learnt from this part of the midterm assignment was how easy it can be to code something
that has a real impact on someone’s learning and practice . The alterations I made were novel but
also realistic changes in the coding that allowed for some helpful features that could better help
someone work toward the goal of reaching a calm state.
Part 2: Event - Related Potentials: Build Your Own ERP Experiment
For part two, I chose to build an ERP experiment that measures the classic component
P300. P300 is a positive deflection in the EEG recording that appears between 300 - 600 ms; this
peak occurs in response to an unexpected stimulus appearing and the brain’s attempt to reorient
itself with the new incoming information. T o demonstrate and measure this ERP , I coded the
traditional Oddball Paradigm, which is typically used, where there are consecutive stimuli
displayed, with 80% of them being a common stimulus a nd 20% being a rare stimulus. In my
case, I manipulated the presented colour of the stimulus in which a blue annulus - shaped flash is
displayed for the common stimulus, and a pink annulus is displayed for the rare stimulus. Either
one of the stimuli is presented every 30ms, and this creates a rare, occasional difference in
expectation for the viewer, elicitin g the P300 peak in their brain activity.
The EEG data w ere simulated over four different channels, TP9, AF7, AF8, and TP10.
For every stimulus trial, a one - second epoch ( - 200ms to 800ms) was created, and this included
the baseline data from the common trials (shown in blue) and data with a peak for rare trials
(sh own in pink). The system collected 20 trials as it ran the code and averaged them out for each
of the four channels; these wavelengths were registered and plotted onto the screen in real time ,
which allows us to visualize the change in wa veform as the rare, unexpected stimuli appear
throughout the trials. If I were testing this with real EEG data, I would expect to see the same
result that was depicted in my BrainImation measurement, an averaged waveform that shows a
notable deflection near the 300 - ms region of the wave. The unexpected stimuli would cause the
brain to orient itself appropriately to what was suddenly registered, and this is why a peak
between 300ms to 600ms is expected to be present. I would also expect to see a relatively l ow
activity level for the rest of the epoch, as the P300 range creates a large peak relative to the rest
of the wave. Overall, the EEG data should measure up to what Sutton et al. (1965) described
when they first discovered the P300 component in 1965.
🎥 Jomha_Midterm_Part1 Video - Mohamed Jomha.mov
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🎥 Jomha_Midterm_Part2 Video - Mohamed Jomha.mov
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📝 Jomha_Midterm_Part2 - Mohamed Jomha.txt
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📝 Jomha_Midterm_Part1 - Mohamed Jomha.txt
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