Kernel Flow
Kernel Flow is a non-invasive brain-computer interface (BCI) technology developed by Kernel, a neurotechnology company founded in 2016. Flow uses functional near-infrared spectroscopy (fNIRS) to measure neural activity in the cerebral cortex[@kernel]. This represents a significant advancement in non-invasive brain sensing technology, offering capabilities that bridge the gap between research-grade fMRI and portable EEG systems.
Overview
Mermaid diagram (expand to render)
Kernel Flow represents a significant advancement in non-invasive brain sensing technology. Unlike electroencephalography (EEG), which measures electrical signals on the scalp, Flow uses infrared light to detect hemodynamic responses in the brain—similar to functional MRI but with a portable headset form factor["@cooper2012"]. This technology was pioneered by Franz Jobsis in 1977, who first demonstrated the feasibility of non-invasive infrared monitoring of cerebral oxygen sufficiency["@jobsis1977"].
The technology measures changes in oxygenated and deoxygenated hemoglobin concentrations in the cortical tissue, which serve as proxies for neural activity through the neurovascular coupling mechanism["@boas2004"]. This hemodynamic response provides information about brain function that complements the faster electrical signals measured by EEG.
Technology
How It Works
Kernel Flow employs diffuse optical tomography (DOT), an advanced form of fNIRS, to measure[@kernela]:
- Oxygenated hemoglobin (HbO): Primary indicator of neural activity through increased blood flow
- Deoxygenated hemoglobin (HbR): Complementary signal reflecting oxygen extraction
- Total hemoglobin: Combined measure of blood volume changes
The system uses arrays of infrared light sources and detectors arranged across the scalp to create a 3D tomographic reconstruction of brain activity. This approach provides better spatial resolution than traditional fNIRS by using multiple overlapping measurement paths[@egger2018].
Measurement Principles
The underlying physics involves the differential absorption of near-infrared light by hemoglobin:
Near-infrared light (650-900 nm) penetrates tissue effectively
Oxygenated and deoxygenated hemoglobin have distinct absorption spectra
Changes in light absorption reflect hemoglobin concentration changes
Multiple source-detector distances provide depth sensitivitySpecifications
| Feature | Specification | Clinical Significance |
|---------|---------------|---------------------|
| Channels | 48+ optical channels | Dense cortical coverage |
| Sampling Rate | Up to 100 Hz | Real-time monitoring |
| Spatial Resolution | ~1-2 cm (depth-dependent) | Cortical layer specificity |
| Wearability | Full-head headset | Extended monitoring sessions |
| Battery | Wireless, rechargeable | Mobile use |
| Connectivity | Bluetooth 5.0 | Wireless data transfer |
| Wavelengths | Multiple (NIR) | Multi-depth measurement |
Technical Advantages
vs. EEG
| Aspect | Kernel Flow | EEG |
|--------|------------|-----|
| Signal Type | Hemodynamic | Electrical |
| Spatial Resolution | 1-2 cm | 5-10 cm |
| Depth Sensitivity | Cortical only | Surface |
| Robustness | Motion-sensitive | More robust |
| Cognitive Load | Minimal | Minimal |
vs. fMRI
| Aspect | Kernel Flow | fMRI |
|--------|------------|------|
| Portability | High (headset) | Very low (scanner) |
| Cost | Low ($10K-30K) | High ($1M+) |
| Noise | Quiet | Loud |
| Motion Constraint | Minimal | Significant |
| Accessibility | Point-of-care | Centralized |
Applications
Cognitive Assessment
Kernel Flow is primarily marketed for cognitive neuroscience research and applications[@balconi2017]:
Memory Studies
- Working memory capacity assessment
- Episodic memory encoding and retrieval
- Memory training efficacy
Attention Research
- Sustained attention monitoring
- Selective attention paradigms
- Attentional control assessment
Executive Function
- Decision-making paradigms
- Problem-solving tasks
- Cognitive flexibility assessment
Neurodegeneration Research
Flow has emerging applications in neurodegenerative disease research[@curtin2019]:
Alzheimer's Disease
- Prefrontal cortex oxygenation monitoring[@herrmann2015]
- Cognitive decline tracking
- Treatment response assessment
- Early detection of MCI[@khan2014]
Parkinson's Disease
- Motor planning deficit assessment
- Bradykinesia characterization
- Gait and balance monitoring[@holper2013]
Stroke Rehabilitation
- Motor cortex mapping
- Neurofeedback training
- Rehabilitation progress monitoring[@mihara2013]
Clinical Evidence
Research Applications
Kernel Flow has been used in various research studies:
| Study Type | Applications | Key Findings |
|------------|-------------|--------------|
| Working Memory | Training studies, capacity assessment | Improved understanding of WM mechanisms |
| Attention | ADHD research, vigilance tasks | Attention network connectivity |
| BCI Development | Motor imagery, task paradigms | Feasibility of fNIRS-BCI[@hauk2019] |
| Rehabilitation | Stroke recovery, neurofeedback | Functional improvement tracking |
Clinical Trials
While primarily a research tool, fNIRS is being integrated into clinical trials:
- Stroke rehabilitation studies
- Cognitive training interventions
- Drug efficacy monitoring
- Biomarker validation studies
Comparison with Other Brain Sensing Technologies
Non-Invasive BCI Technologies
| Technology | Spatial Res. | Temporal Res. | Portability | Cost |
|------------|-------------|---------------|-------------|------|
| Kernel Flow (fNIRS) | 1-2 cm | Seconds | High | $ |
| EEG | 5-10 cm | Milliseconds | High | $ |
| fMRI | 1-3 mm | Seconds | Very Low | $$$$ |
| MEG | 1-3 cm | Milliseconds | Very Low | $$$$ |
| PET | 4-5 mm | Minutes | Very Low | $$$$ |
Commercial Competitors
Kernel is one of several companies developing non-invasive brain sensing technologies:
| Company | Product | Technology | Primary Use |
|---------|---------|------------|-------------|
| Kernel | Flow | fNIRS/DOT | Research, cognitive assessment |
| OpenBCI | Ultracortex | EEG | Research, BCI development |
| g.tec | g.Nautilus | EEG | Research, clinical |
| Emotiv | EPOC | EEG | Consumer, research |
| Wearable Sensing | DSI | EEG | Research |
Regulatory Status
Kernel Flow is primarily sold as a research tool and has not received FDA clearance for clinical diagnostic use. It is available for:
- Research institutions
- Academic laboratories
- Cognitive neuroscience studies
- Brain-computer interface development
Potential Regulatory Pathways
Future clinical applications may pursue:
- 510(k) pathway for diagnostic devices
- De novo classification for novel devices
- Breakthrough Device designation for neurological conditions
Clinical Applications in Neurodegeneration
Alzheimer's Disease
Kernel Flow applications in Alzheimer's disease[@khan2014][@herrmann2015]:
Cognitive Assessment
- Objective measurement of executive function
- Working memory capacity evaluation
- Attention and processing speed
Treatment Monitoring
- Response to disease-modifying therapies
- Cholinesterase inhibitor effects
- Non-pharmacological intervention tracking
Research Applications
- Neural correlates of cognitive decline
- Biomarker development
- Disease progression monitoring
Parkinson's Disease
PD-specific applications:
Motor Planning Studies
- Cortical activity during movement
- Supplementary motor area involvement
- Premotor cortex function
Non-Motor Symptoms
- Cognitive impairment detection
- Executive dysfunction assessment
- Depression and anxiety monitoring
Treatment Optimization
- Dopaminergic therapy response
- Deep brain stimulation effects
- Levodopa-induced changes
Stroke Recovery
Stroke rehabilitation applications:
Motor Cortex Mapping
- Cortical representation mapping
- Reorganization assessment
- Plasticity monitoring
Neurofeedback Training
- Motor imagery enhancement
- Voluntary movement facilitation
- Cortical activation optimization
Progress Monitoring
- Objective functional assessment
- Recovery trajectory prediction
- Treatment efficacy evaluation
Future Directions
Technical Improvements
- Higher channel counts for improved spatial resolution
- Integration with other modalities (EEG, tDCS, TMS)
- Advanced signal processing algorithms
- Improved motion artifact rejection
Clinical Translation
- Clinical trials for specific applications
- FDA clearance for neurological diagnostics
- Integration with electronic health records
Consumer Applications
- Brain fitness monitoring
- Meditation and mindfulness tracking
- Cognitive enhancement training
Limitations and Considerations
Technical Limitations
Depth Sensitivity: Only measures cortical (surface) activity
Motion Artifacts: Sensitive to head movement[@ouille2019]
Variable Coupling: Hair can affect optode contact
Lower Temporal Resolution: Seconds vs. milliseconds for EEGPractical Considerations
- Cost: Higher than EEG but lower than fMRI
- Training: Requires trained operators
- Analysis Complexity: Specialized expertise needed
See Also
- [Brain-Computer Interface Technologies](/technologies/bci)
- [Non-Invasive Brain Stimulation](/technologies)
- [Functional Near-Infrared Spectroscopy](/technologies/fnirs-bci)
- [Alzheimer's Disease Research](/institutions/alzheimers-disease-research-centers)
- [Parkinson's Disease Research](/diseases/parkinsons-disease)
- [Stroke Rehabilitation](/therapeutics/rehabilitation)
- [EEG Brain-Computer Interfaces](/technologies/eeg-bci)
Relevant Mechanisms
Kernel Flow's fNIRS technology interfaces with several key neurodegenerative disease mechanisms:
- [Neurovascular Unit** — fNIRS measures hemodynamic responses through neurovascular coupling](/brain-regions/pons)
- [Cerebral Blood Flow** — Blood oxygenation changes reflect neural activity](/proteins/ANG)
- [Cortical Oscillations** — Hemodynamic responses correlate with neural oscillations](/brain-regions/pons)
- [BDNF Signaling** — Neurovascular health supports neurotrophic factor function](/genes/ar)
References
[Kernel Official Website](https://kernel.co/)
[Kernel Flow Technical Specifications](https://kernel.co/flow)
[Cooper et al., Functional near-infrared spectroscopy (2012)](https://doi.org/10.1016/j.neuroimage.2012.01.134)
[Jobsis, Noninvasive infrared monitoring (1977)](https://doi.org/10.1126/science.867576)
[Boas et al., Diffuse optical imaging of brain activation (2004)](https://doi.org/10.1016/j.neuroimage.2004.07.013)
[Egger et al., fNIRS in cognitive neuroscience (2018)](https://doi.org/10.1016/j.neuroimage.2018.01.038)
[Curtin et al., Near-infrared spectroscopy in neurodegenerative disease (2019)](https://doi.org/10.1177/0271678X19839594)
[Fallgatter et al., Near-infrared spectroscopy in psychiatry (1997)](https://doi.org/10.1016/S0361-9230(96)00344-1)
[Schroeter et al., Neuronal activity affects cerebral hemodynamics (2004)](https://doi.org/10.1016/j.neuroimage.2004.01.029)
[Hauk et al., fNIRS in brain-computer interface research (2019)](https://doi.org/10.3390/s19122799)
[Mihara et al., Near-infrared spectroscopy for stroke rehabilitation (2013)](https://doi.org/10.2176/nmc.oa.2012-0198)
[Khan et al., fNIRS in Alzheimer's disease (2014)](https://doi.org/10.3233/JAD-132135)
[Herrmann et al., Prefrontal cortex oxygenation in neurodegeneration (2015)](https://doi.org/10.1016/j.nicl.2015.05.008)
[Holper et al., Motor learning in Parkinson's disease (2013)](https://doi.org/10.1016/j.neuroimage.2013.03.019)
[Meyer et al., Neurofeedback with fNIRS (2016)](https://doi.org/10.1117/1.NPh.3.3.031407)
[Balconi et al., fNIRS applications in neuroscience (2017)](https://doi.org/10.3389/fpsyg.2017.01345)
[Hock et al., fNIRS in dementia research (2018)](https://doi.org/10.1007/s00702-017-1820-8)
[Ouille et al., fNIRS signal quality assessment (2019)](https://doi.org/10.3390/s19020459)
[Virtanen et al., fNIRS for brain-computer interface (2019)](https://doi.org/10.3390/s19112590)
[Cutini et al., Resting-state fNIRS (2014)](https://doi.org/10.1016/j.neuroimage.2014.01.056)
[Pfeifer et al., Optical imaging of cortical activity (2018)](https://doi.org/10.1038/s41583-018-0043-9)
[Yeh et al., Wearable fNIRS technology (2020)](https://doi.org/10.1007/978-3-030-22462-4_18)