Research

Optical Brain Imaging Using Functional Near Infrared Spectroscopy (fNIRS) and Diffuse Correlation Spectroscopy (DCS)

Optical brain imaging techniques using near-infrared light have recently become the most popular non-invasive neuroimaging techniques. These approaches directly depend on the absorption and scattering of near-infrared light sent by a source. After absorption and scattering of photons, they are captured by detectors. While concentration changes of oxy-hemoglobin (∆HbO2) and deoxyhemoglobin (∆Hb) are being measured by using fNIRS, DCS is used to measure relative cerebral blood flow (rCBF).

Both techniques have the advantages of [1]:

  • Non-invasiveness

  • Deep tissue penetration (up to 1 cm)

  • High spatial resolution


Working principle of fNIRS (top) and DCS (bottom) [2].

Electroencephalography (EEG): Principle and Applications

EEG working principle [4].

Principle

There are billions of cells in the brain, half of which are neurons and the other half of which support and aid the activity of neurons. Synapses, which serve as gateway for inhibitory or excitatory activity, are used to connect these neurons in a dense network. Every activity in this synapses an electrical pulse known as postsynaptic potential [3].

It is difficult to detect pulses from a single neuron but it is possible to detect the magnetic field created by a large group of neurons generating pulses. Common EEG systems use electrodes placed on top of the scalp to measure the electrical activity generated by thousands of neurons. Less common systems use invasive electrodes to measure electrical activity of a small group of neurons or a single neuron.

EEG systems have higher temporal resolutions compared to fNIRS systems, but they lack the spatial resolutions that can be achieved by fNIRS.

Comparison of common neuroimaging techniques [5].

Applications

EEG and other neuroimaging systems can be used together with Machine Learning to develop a large range of projects. Such as:

  • Finding biomarkers that can help early detection of neurological diseases.

  • Investigating how the brain behaves during certain tasks to get more insight into cognitive function.

  • Creating Brain Machine Interfaces (BCI) to help disabled people in their daily lives.

  • Detecting sleep disorders & classifying sleep stages.

Brain Machine Interface example [6].

Resources

[1] Durduran, T., & Yodh, A. G. (2014). Diffuse correlation spectroscopy for non-invasive, micro-vascular cerebral blood flow measurement. Neuroimage, 85, 51-63.

[2] Imaging Technologies. Optics Martinos. (n.d.). Retrieved December 7, 2022, from https://optics.martinos.org/research/imaging-modalities/

[3] Farnsworth, B., Heslinga, O., & Krosschell, K. (2022, November 15). What is EEG (electroencephalography) and how does it work? iMotions. Retrieved December 7, 2022, from https://imotions.com/blog/learning/research-fundamentals/what-is-eeg/

[4] Uldry, Laurent & Millan, Jose del R.. (2007). Feature Selection Methods on Distributed Linear Inverse Solutions for a Non-Invasive Brain-Machine Interface.

[5] Farnsworth, B. et al. (2022) Top 3 devices for monitoring and measuring brain activity, iMotions. Available at: http://websitebuild.imotions.com/blog/learning/best-practice/top-3-devices-measuring-brain-activity/ (Accessed: December 7, 2022).

[6] Brain-Computer Interface User Types 90 Characters Per Minute with Mind (no date) The Scientist Magazine®. Available at: https://www.the-scientist.com/news-opinion/brain-computer-interface-user-types-90-characters-per-minute-with-mind-68762 (Accessed: 7 December 2022).