EEG Electrode Cap for Combining EEG and fNIRS

Category: Brain science research

Time：2024-12-21

Introduction to fNIRS

In recent years, brain imaging technology has made significant progress. Brain imaging techniques can be divided into two main categories: 1) Structural imaging, such as Computed Tomography (CT); and 2) Functional imaging, such as Electroencephalography (EEG). Apart from invasive methods (such as cortical EEG), non-invasive functional imaging methods can mainly be classified into electrophysiological methods and metabolic methods. EEG is a popular electrophysiological method widely used in neuroscience, neurorehabilitation, brain-computer interfaces (BCI), and many other research fields. EEG offers excellent temporal resolution (on the millisecond scale) for measuring neural activity, but its spatial resolution is limited due to volume conduction, which restricts source localization accuracy. On the other hand, Functional Near-Infrared Spectroscopy (fNIRS) is a promising tool for monitoring brain metabolic changes. It provides good spatial resolution due to its inherent hemodynamic delay, but its temporal resolution is lower. fNIRS is more robust than EEG in the presence of electrical noise and motion-based muscle activity artifacts, making it a viable alternative to EEG in noisy environments.

Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive, real-time, long-term monitoring optical brain imaging technique that uses near-infrared light to detect tissue blood oxygen changes. Near-infrared light (wavelength range of 550 nm to 950 nm) can penetrate a certain thickness of biological tissue and be used to detect brain oxygenation levels. fNIRS works by emitting light into the brain using optical fibers and then measuring the reflected light with detectors. By observing specific wavelengths of light, researchers can measure hemoglobin absorption and blood flow in specific brain regions. As brain areas become more active, the oxygenation levels change. Researchers can identify brain activity in real-time based on blood oxygen and other factors.

fNIRS has been widely used to study normal brain functions, such as in psychology, education, management, and sports science, as well as in brain mechanisms related to diseases like schizophrenia, depression, epilepsy, and consciousness disorders. Additionally, this technology has unique advantages in the study of cognitive development and disease diagnosis in children and infants.

Introduction to EEG-fNIRS

EEG has fine temporal resolution (millisecond precision), but its spatial resolution is limited. fNIRS has good spatial resolution (<1 cm) but limited temporal resolution (approximately 3 to 6 seconds). The multimodal fusion technology based on EEG and fNIRS integrates the advantages of both techniques spatiotemporal resolution. By combining EEG and fNIRS, it is possible to monitor the brain across various time scales, spatial resolutions, and different potential physiological processes. The fusion of these two techniques allows for the simultaneous acquisition of neural electrical activity and blood oxygen metabolic changes, providing a more comprehensive and accurate reflection of brain functional activity.

The simultaneous EEG and fNIRS system has garnered increasing attention. fNIRS is suitable for simultaneous recording with EEG because its near-infrared light does not interfere with EEG measurements. Over the past decade, more research methods have focused on the simultaneous use of EEG and fNIRS. The EEG-fNIRS combined technology has proven its advantages in several research areas. For example, the hybrid EEG-NIRS BCI system shows better classification accuracy than each individual modality BCI system, and EEG-NIRS hybrid systems have also been used to better understand language functions in neonates and infants. EEG-fNIRS correlation analysis helps further reveal the complex relationship between electrophysiological and hemodynamic changes in neuroscience. Using EEG and fNIRS combined recording and fusion analysis, high-spatial-resolution and high-temporal-resolution attention activation response patterns have been plotted, revealing interesting neurovascular coupling mechanisms behind attention.

Both of these technologies have the advantages of being safe, non-invasive, cost-effective, portable, easy to obtain signals from, usable in natural environments, and capable of long-duration continuous monitoring. Compared to single-modality systems, EEG-fNIRS fusion technology improves classification accuracy by integrating different types of features (neural electrical activity signals and hemoglobin concentration changes), increasing the number of control commands, and enhancing the functionality of the system.

Challenges in EEG-fNIRS Multimodal Fusion Technology

On one hand, due to mismatched temporal resolutions and the inherent delay in hemodynamic responses, there can be a degree of synchronization difficulty in the features. Morioka et al. proposed a cortical current decoding method based on fNIRS information, using fNIRS features as prior information to estimate cortical EEG signals in EEG, demonstrating that this method outperforms EEG-only decoding methods in spatial attention tasks. Sangtae et al. proposed a feature combination method based on feature normalization, normalizing EEG and fNIRS features within the range of 0–1, and applying the total of the features to achieve better BCI performance in detecting fatigue driving tasks than using single-modality systems. Although further optimization is needed, these methods may serve as future solutions to overcome the current limitations in the fusion of EEG-fNIRS features.

On the other hand, mismatched recording locations of EEG and fNIRS may affect the interpretation of neurovascular coupling mechanisms in the brain. To address this, it is crucial to match the spatial locations of EEG electrodes and fNIRS optodes as much as possible during the experimental preparation phase. The hemodynamic response recorded by fNIRS lies between the light source and detector, so EEG electrodes should be placed between the emitter and detector to ensure spatial matching. Additionally, the signal-to-noise ratio of fNIRS may be affected by the participant’s hair, so it is essential to ensure good contact between the optodes and the scalp during preparation.

EEG Electrode Cap for Combining EEG and fNIRS

To simultaneously measure EEG and fNIRS, EEG electrodes and fNIRS optodes must be perfectly integrated into a single electrode cap. Therefore, a key factor is the EEG-fNIRS combined electrode cap. Greentek provides solutions for EEG-fNIRS combined electrode caps. Based on users’ research needs, EEG electrodes and fNIRS optodes are seamlessly integrated into the same electrode cap.

Advantages of Greentek’s EEG-fNIRS Combined Electrode Cap:

A stable cap structure that keeps fNIRS optodes and EEG electrodes in fixed positions to obtain artifact-free data.

Black cap material ensures that ambient light does not cause distortion.

Lightweight EEG electrodes and fNIRS optodes reduce motion artifacts during recordings.

Multiple, freely configurable, wearable, multi-channel EEG-fNIRS combined electrode caps, with flexible electrode placement according to the areas of interest in the research.

Available in different sizes to suit research for various age groups.

If you have any needs for EEG-fNIRS combined electrode caps, please contact Greentek’s technical team in the research field.

References

Fazli, S. et al. Enhanced performance by a hybrid NIRS-EEG brain-computer interface. Neuroimage 59, 519–529 (2012).
Fazli, S. & Lee, S.-W. Brain-computer interfacing: a multi-modal perspective. J. Comput. Sci. Eng. 7, 132–138 (2013).
Wallois, F., Mahmoudzadeh, M., Patil, A. & Grebe, R. The usefulness of simultaneous EEG-NIRS recording in language studies. Brain Lang. 121, 110–123 (2012).
Schneider, S. et al. Beyond the N400: Complementary access to early neural correlates of novel metaphor comprehension using combined electrophysiological and haemodynamic measurements. Cortex 53, 45–59 (2014).
Nasi, T. et al. Correlation of visual-evoked hemodynamic responses and potentials in human brain. Exp. Brain Res. 202, 561–570 (2010).
Zhao C, Guo J, Li D, Tao Y, Ding Y, Liu H, Song Y. 2019. Anticipatory alpha oscillation predicts attentional selection and hemodynamic response. Human Brain Mapping. 40:3606–3619.
Huang J, Wang F, Ding Y, Niu H, Tian F, Liu H, Song Y. 2015. Predicting N2pc from anticipatory HbO activity during sustained visuospatial attention: a concurrent fNIRS-ERP study. NeuroImage. 113:225–234.

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