Title
Project: Enlightening the emotional brain
Leaders
Haiping Huang (@hpphearscode)
Collaborators
NA
Project description
Everyday sounds carry rich emotional meaning: birdsong on a morning walk, a newborn’s coo, a favorite piece of music, or the impatient honk of a driver during rush hour. Our emotional responses to sound—the pleasure, comfort, excitement, or irritation we feel—are an important part of everyday life and well-being. However, behavioral studies in our lab suggest that hearing loss may dampen/compress these responses and that current hearing devices do not fully restore them. As an audiologist, my goal is to help patients regain not only access to sound, but also the joy it can bring.
To pursue this goal, I am launching a research program in my lab that uses optical brain imaging, which is compatible with electronic devices such as hearing aids and cochlear implants. We aim to investigate how the brain represents emotion in sound and how hearing loss and hearing devices alter emotion-related cortical networks. Put simply: Where and how is this compressed emotional response reflected in the brain?
The proposed Vanderbilt BrainHack 2026 project is an important first step in this research program. We will establish an analysis pipeline and evaluate whether our experimental paradigm works in adults with typical hearing. We will then use what we learn to extend the research to people with hearing loss, including hearing-aid and cochlear-implant users.
Because this research is still in its infancy, there is plenty of room to experiment, make mistakes, and solve problems together 😊 Creativity is strongly encouraged! Draft workflows and templates will be provided as starting points, but participants are welcome to modify, improve, or completely reimagine them.
Link to project repository/sources
https://github.com/hpphearscode/fNIRS-emotion-VandyBH26
Concerete goals with specific tasks for Brainhack Vanderbilt 2026
- Optode Digitization & Co-registration
- Develop or refine a FieldTrip-based workflow to transform individualized fNIRS optode locations from Structure Sensor 3 scans into standardized MNI space.
- fNIRS Preprocessing & Quality Control
- Develop or refine a reproducible MNE-NIRS preprocessing pipeline, including signal-quality assessment, bad-channel identification, filtering, and preparation of HbO/HbR signals for analysis.
- Task-Evoked Response Analysis
- Develop or refine scripts for waveform averaging and visualization of valence-related fNIRS responses across channels and brain regions.
- Connectivity Analysis (Exploratory)
- Explore the feasibility of functional and/or effective connectivity analyses between prefrontal and auditory regions using the current fNIRS dataset.
Good first issues
- First: familiarize ourselves with the project context.
- A short literature review is provided in the onboarding documentation that briefs the theoretical grounds and current findings. This sets the stage for why we are doing this.
The following issues generally follow our envisioned workflow. Depending on the participant's interest and/or expertise, they may also choose to address individual components.
- Second: Co-registration of digitized optode coordinates to MNI template using FieldTrip in MATLAB.
- Third: Data Cleaning & Integrity Check in Python. For example:
- Calculating scalp coupling index to identify bad channels.
- Checking accuracy of event annotations.
- Fourth: Pre-processing fNIRS recordings in Python. For example:
- Conversion of raw data to optical density values, then to hemoglobin values.
- Removal of non-physiological drifts, physiological noises (heart beat, etc.), motion artefacts, and so on.
Skills
- Python (All experience levels welcome):
- Our project uses MNE-Python/MNE-NIRS to preprocess fNIRS data and examine task-related responses through waveform averaging and potentially GLM analysis. We already have a working analysis pipeline adapted from MNE-NIRS example scripts, but would like to improve, validate, and better understand the workflow.
- Experience with Python, neural or physiological time-series data, signal filtering/preprocessing, visualization, or statistical modeling would be helpful, but not required. Participants who are newer to functional imaging or Python are also very welcome—the project is intended to provide an opportunity to work through an approachable neuroimaging pipeline, troubleshoot together, and learn MNE-NIRS along the way.
- MATLAB / 3D Neuroimaging Coregistration (All experience levels welcome): We are using Structure Sensor 3 scans of each participant’s head to obtain individualized fNIRS optode locations, with the goal of coregistering these locations to a standard MNI anatomical space. Our workflow is being adapted from existing FieldTrip tutorials for localizing EEG electrodes from 3D head scans.
- Experience with MATLAB, FieldTrip, or neuroimaging coregistration would be helpful, but not required. Participants who are newer to MATLAB or neuroimaging coregistration are also very welcome. A major goal of the project is to adapt and better understand an existing workflow together while developing a reproducible approach for localizing fNIRS optodes across participants.
- Non-Coding Knowledge: Basic knowledge of neuroscience/affective neuroscience, auditory processing, may be helpful for understanding the project context, but no specific neuroscience background is required.
Onboarding documentation
Please see README.md
What will participants learn?
Participants will gain hands-on experience with:
- fNIRS fundamentals: How fNIRS measures cortical activity and how raw optical signals are converted into HbO/HbR time-series data.
- fNIRS preprocessing in Python: Using MNE-Python/MNE-NIRS for signal-quality assessment, filtering, artifact handling, epoching, and visualization.
- Task-evoked brain responses: Creating and interpreting waveform averages and exploring GLM-based analysis of responses to sounds with different emotional valence.
- Neuroimaging coregistration: Using MATLAB/FieldTrip and 3D head scans to localize individual fNIRS optodes and transform them into standardized MNI space.
- Collaborative neuroimaging workflows: Working with existing scripts and tutorials, troubleshooting analysis decisions, and learning from collaborators with different backgrounds.
Public data to use
Raw data
Note that data collection is ongoing and the folder will be populated by 9/4/2026
Number of collaborators
2
Credit to collaborators
All contributors will be prominently listed on the project’s primary repository documentation. Outstanding contributions that alter or improve the underlying methodology will be offered formal co-authorship on future academic abstracts or manuscript updates, as appropriate and in accordance with standard scientific authorship and contribution practices.
Image
Project Summary
How does the brain respond to emotion in sound? Together, we will build an open-source pipeline for analyzing functional near-infrared spectroscopy (fNIRS) data and investigate how pleasant and unpleasant sounds shape activity in auditory and prefrontal brain regions.
Type
pipeline_development
Development status
1_basic structure
Topic
other
Tools
MNE, FieldTrip
Programming language
Python, Matlab
Modalities
fNIRS
Git skills
1_commit_push
Anything else?
Things to do after the project is submitted and ready to review.
Title
Project: Enlightening the emotional brain
Leaders
Haiping Huang (@hpphearscode)
Collaborators
NA
Project description
Everyday sounds carry rich emotional meaning: birdsong on a morning walk, a newborn’s coo, a favorite piece of music, or the impatient honk of a driver during rush hour. Our emotional responses to sound—the pleasure, comfort, excitement, or irritation we feel—are an important part of everyday life and well-being. However, behavioral studies in our lab suggest that hearing loss may dampen/compress these responses and that current hearing devices do not fully restore them. As an audiologist, my goal is to help patients regain not only access to sound, but also the joy it can bring.
To pursue this goal, I am launching a research program in my lab that uses optical brain imaging, which is compatible with electronic devices such as hearing aids and cochlear implants. We aim to investigate how the brain represents emotion in sound and how hearing loss and hearing devices alter emotion-related cortical networks. Put simply: Where and how is this compressed emotional response reflected in the brain?
The proposed Vanderbilt BrainHack 2026 project is an important first step in this research program. We will establish an analysis pipeline and evaluate whether our experimental paradigm works in adults with typical hearing. We will then use what we learn to extend the research to people with hearing loss, including hearing-aid and cochlear-implant users.
Because this research is still in its infancy, there is plenty of room to experiment, make mistakes, and solve problems together 😊 Creativity is strongly encouraged! Draft workflows and templates will be provided as starting points, but participants are welcome to modify, improve, or completely reimagine them.
Link to project repository/sources
https://github.com/hpphearscode/fNIRS-emotion-VandyBH26
Concerete goals with specific tasks for Brainhack Vanderbilt 2026
Good first issues
The following issues generally follow our envisioned workflow. Depending on the participant's interest and/or expertise, they may also choose to address individual components.
Skills
Onboarding documentation
Please see README.md
What will participants learn?
Participants will gain hands-on experience with:
Public data to use
Raw data
Note that data collection is ongoing and the folder will be populated by 9/4/2026
Number of collaborators
2
Credit to collaborators
All contributors will be prominently listed on the project’s primary repository documentation. Outstanding contributions that alter or improve the underlying methodology will be offered formal co-authorship on future academic abstracts or manuscript updates, as appropriate and in accordance with standard scientific authorship and contribution practices.
Image
Project Summary
How does the brain respond to emotion in sound? Together, we will build an open-source pipeline for analyzing functional near-infrared spectroscopy (fNIRS) data and investigate how pleasant and unpleasant sounds shape activity in auditory and prefrontal brain regions.
Type
pipeline_development
Development status
1_basic structure
Topic
other
Tools
MNE, FieldTrip
Programming language
Python, Matlab
Modalities
fNIRS
Git skills
1_commit_push
Anything else?
Things to do after the project is submitted and ready to review.
Hi @brainhack-vandy/project-monitors my project is ready!