Knowledge Vault 3/50 - G.TEC BCI & Neurotechnology Spring School 2024 - Day 4
Wireless EEG and fNIRS recordings
Maria Antonia Piedrahita, Patrick Reitner,
g.tec medical engineering Colombia (CO), g.tec medical engineering GmbH (AT)
<Resume Image >

Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Llama 3:

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and fNIRS recordings] --> B[gTech Colombia presents wireless
EEG/fNIRS recordings. 1] A --> C[fNIRS: non-invasive neuroimaging
with near-infrared light. 2] C --> D[Neurovascular coupling, chromophores, tissue
properties crucial for fNIRS. 3] D --> E[Neuronal activity increases blood
flow, HBO concentration. 4] D --> F[HBO, HBR have different
near-infrared absorption spectra. 5] C --> G[fNIRS uses scalp sources,
detectors forming channels. 6] G --> H[Short channels measure superficial
layers, long measure cortex. 7] C --> I[Continuous wave fNIRS measures
light intensity, uses Beer-Lambert. 8] C --> J[fNIRS signal contains hemodynamic
response, physiological noise. 9] C --> K[Average HBO response: initial
dip, peak, return to baseline. 10] A --> L[EEG/fNIRS: complementary information, improved
spatiotemporal resolution, enhances BCI. 11] A --> M[Three wireless EEG/fNIRS systems
presented with software. 12] A --> N[Experiment: motor execution, right-hand
grasping, EEG/fNIRS over motor cortex. 13] N --> O[Data recorded, synchronized via
LSL for real-time analysis. 14] N --> P[Signal quality checked before
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EEG/fNIRS signals, triggers, filtering. 17] A --> S[Offline analysis in MATLAB:
EEG filtering, ERD/ERS, fNIRS averaging. 18] S --> T[fNIRS: increased HBO, decreased
HBR in contralateral motor cortex. 19] S --> U[EEG: ERD in motor
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temporal resolution, spatial localization. 21] A --> W[fNIRS challenges: hair obstruction,
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Latin America, potential for growth. 23] A --> Y[Typical fNIRS paradigms: 10s
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respiration, Mayer waves, motion. 25] A --> AA[Live demo showcases real-world
EEG/fNIRS potential, challenges. 26] A --> AB[Future: improving signal quality,
robust analysis, expanding applications. 27] A --> AC[Open questions: optimal paradigms,
artifact removal, neurovascular coupling interpretation. 28] A --> AD[Collaboration advances field, translates
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1.- Maria Antonia Piedrahita from gTech Colombia presents on wireless EEG and fNIRS recordings.

2.- fNIRS is a non-invasive neuroimaging technique using near-infrared light to measure changes in oxygenated and deoxygenated hemoglobin concentrations.

3.- Neurovascular coupling, chromophores of interest (HBO and HBR), and biological tissue properties are important for understanding fNIRS.

4.- Neuronal activity increases blood flow and HBO concentration in active brain regions due to neurovascular coupling.

5.- HBO and HBR have different absorption spectra in the near-infrared range; biological tissue is relatively transparent to this light.

6.- fNIRS uses sources and detectors placed on the scalp; channels are created with one source and one detector.

7.- Short separation channels measure superficial layers; long separation channels measure deeper cortical activity.

8.- Continuous wave fNIRS illuminates tissue and measures transmitted light intensity, which is converted to concentration using the modified Beer-Lambert law.

9.- The measured fNIRS signal contains the hemodynamic response and other physiological noise components like cardiac pulsations and respiration.

10.- The average HBO response shows an initial dip, peak around 8 seconds, then returns to baseline; HBR decreases or stays constant.

11.- Combining EEG and fNIRS provides complementary information, improved spatiotemporal resolution, and can enhance brain-computer interfaces.

12.- Three wireless EEG/fNIRS systems are presented: g.Nautilus with g.HIamp/NIRx NIRSport2, g.Nautilus with Artinis Brite, integrated with respective software.

13.- Experiment setup: motor execution paradigm with right-hand grasping; 32 EEG electrodes and 8 fNIRS channels over the motor cortex.

14.- Data is recorded using each system's acquisition software, synchronized via Lab Streaming Layer (LSL) for real-time visualization and analysis.

15.- Signal quality is checked for EEG (impedance, noise, alpha waves) and fNIRS (channel quality, scalp coupling) before starting the experiment.

16.- The experimental paradigm alternates between rest and task periods, with visual cues indicating when to perform hand grasping.

17.- Real-time data visualization in MATLAB/Simulink allows monitoring of EEG and fNIRS signals, triggering, filtering, and averaging.

18.- Offline analysis in MATLAB with custom scripts or gHiSys Analyze processes EEG (filtering, triggering, ERD/ERS) and fNIRS (filtering, triggering, averaging) data.

19.- fNIRS results show increased HBO and decreased HBR in contralateral motor cortex channels during right-hand grasping.

20.- EEG time-frequency analysis reveals event-related desynchronization (ERD) in motor cortex electrodes during hand grasping.

21.- Combining EEG and fNIRS leverages their complementary strengths: high temporal resolution of EEG and better spatial localization of fNIRS.

22.- Challenges with fNIRS include hair obstruction and signal attenuation in darker skin; techniques like hair thinning and spring-loaded optodes can help.

23.- Neurotech is still emerging in Colombia and Latin America, with potential for growth and impact on neurological and mental health applications.

24.- Typical fNIRS paradigms use a 10-second baseline and 20-25 second post-stimulus window to capture the slower hemodynamic response.

25.- Common fNIRS artifacts include cardiac pulsation, respiration, Mayer waves, blood pressure changes, and motion; short separation channels can help remove them.

26.- This live demo, despite suboptimal conditions, showcases the potential and challenges of real-world EEG/fNIRS experiments.

27.- Future directions include improving signal quality, developing robust analysis methods, and expanding applications in basic and clinical neuroscience.

28.- Open questions remain about optimal paradigm design, artifact removal, and interpretation of neurovascular coupling in various populations and conditions.

29.- Collaborative efforts between researchers, clinicians, and industry partners can advance the field and translate findings into impactful neurotech solutions.

30.- Hands-on training opportunities like this demonstration are valuable for building practical skills and driving innovation in the global neurotech community.

Knowledge Vault built byDavid Vivancos 2024