The Advantages of Combining fNIRS and EEG for Brain Monitoring
Author: Mohammad Shahbakhti
Recent studies have highlighted the advantages of combining functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for a deeper understanding of brain activity [1]. While it is widely recognized that simultaneous analysis of EEG and fNIRS improves our understanding of brain activity by providing better temporal (when) and spatial (where) resolutions [2], their combined benefits extend well beyond this fundamental advantage. In this blog post, we delve into two additional practical benefits of this combination for studying brain activity in cognitive states.
1. Dual insights on electrical activity of brain and its oxygen consumption
Figure 1: Combining fNIRS and EEG can give increased insight into brain activity
One major advantage of combining EEG and fNIRS is the ability to measure both the brain's electrical activity and its oxygen use. EEG tracks the electrical signals from neurons, while fNIRS looks at changes in oxygen levels, which reflect how much energy the brain consumes. By combining these two, we get a fuller picture of brain activity, showing both how the brain is working and the energy it needs to do so.
Let’s explain this with an example.
Imagine a scenario where an air traffic controller is responsible for managing dozens of aircraft simultaneously. Their ability to make quick, accurate decisions is critical for maintaining safety. Significant shifts in alpha and theta waves in EEG can indicate high mental workload or fatigue [3]. At the same time, fNIRS can detect an increase in oxygenated hemoglobin, which reflects higher metabolic demand and cognitive effort [4].
Combining EEG and fNIRS provides two key pieces of information: EEG shows the brain’s electrical activity, helping us identify when the controller is approaching their mental limit, while fNIRS indicates whether the brain has enough oxygen to manage the task. For example, if EEG shows decreased activity but fNIRS still detects high oxygen levels, it could suggest the controller is mentally exhausted, even though they are still engaged in the task. This may signal the need for a break or reassignment to prevent mistakes.
By offering both the "what" (neural activity) and the "how" (oxygen consumption), EEG and fNIRS together provide a more comprehensive understanding of brain function.
2. Access to additional physiological parameters
Beyond the dual insights, combining EEG and fNIRS enables simultaneous monitoring of physiological parameters alongside brain activity. The sensitivity of EEG and fNIRS to physiological noise offers opportunities to better understand interactions between the brain and other organs. For instance, EEG signals, especially in the frontal lobe, often include eye blinks, which are effective indicators for monitoring cognitive states [5]. Similarly, fNIRS is influenced by cardiac pulsation, respiratory fluctuations, and blood pressure changes [6]. This makes the combination particularly useful for studying the interplay between brain activity and systemic physiological responses.
Let’s illustrate this with an example of stress monitoring.
Imagine a surgeon performing a critical operation under intense time pressure. Monitoring their stress levels in real-time is crucial, as high stress can impair decision-making and motor skills. The combined use of EEG and fNIRS offers a comprehensive way to achieve this by analyzing both brain activity and related physiological responses.
EEG provides valuable insights into brainwave patterns associated with stress. Signals from the frontal lobe often may show increased beta activity and decreased alpha waves during heightened stress or cognitive overload. Additionally, blinks in EEG can indirectly indicate stress or fatigue, as frequent blinking is linked to emotional strain or mental exertion. Meanwhile, fNIRS complements this by measuring oxygenation changes in the prefrontal cortex, which reflect the brain's metabolic demand. Under stress, oxygen consumption often rises, and fNIRS can capture this alongside systemic responses like increased heart rate, respiratory changes, and fluctuations in blood pressure - key physiological responses to stress.
Figure 2: Extracting further physiological parameters when combining fNIRS and EEG
By combining EEG and fNIRS, a richer understanding of stress may emerge. For instance, reduced alpha waves and increased beta waves, and frequent blink appearance in EEG, paired with heightened oxygenation and pronounced heart and respiratory measures in fNIRS, may indicate that both the brain and body are under stress. This integrated perspective enables timely interventions, like promoting relaxation or adjusting workload, to enhance well-being and performance.
Conclusion
The fusion of fNIRS and EEG brings together the best of two worlds: the speed of electrical brain monitoring and the depth of hemodynamic analysis. With their combined capabilities, researchers and clinicians gain a richer, more nuanced understanding of the brain and its connection to the rest of the body. Whether to advance neuroscience research, enhance medical diagnostics, or optimize human performance, the synergy between fNIRS and EEG is paving the way for exciting innovations in brain monitoring and beyond.
Literature[1] Li, R.; Yang, D.; Fang, F.; Hong, K.-S.; Reiss, A.L.; Zhang, Y. Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review. Sensors 2022, 22, 5865. https://doi.org/10.3390/s22155865.
[2] Liu, Z.; Shore, J.; Wang, M.; Yuan, F.; Buss, A.; Zhao, X. A systematic review on hybrid EEG/fNIRS in brain-computer interface. Biomedical Signal Processing and Control 2021, 68, 102595. https://doi.org/10.1016/j.bspc.2021.102595.
[3] Kingphai, K.; Moshfeghi, Y. Mental Workload Assessment Using Deep Learning Models from EEG Signals: A Systematic Review. IEEE Transactions on Cognitive and Developmental Systems 2024. https://doi.org/10.1109/TCDS.2024.3460750.
[4] Ji, X.; Dong, Q.; Liu, Z.; Pu, J.; Li, T. Association between low-frequency oscillation and cognitive compensation in high-performance group: An fNIRS mapping study. NeuroImage 2024, 304, 120944. https://doi.org/10.1016/j.neuroimage.2024.120944.
[5] Shahbakhti, M.; et al. Fusion of EEG and Eye Blink Analysis for Detection of Driver Fatigue. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2023, 31. https://doi.org/10.1109/TNSRE.2023.3267114.
[6] Hakimi, N.; Shahbakhti, M.; Sappia, S.; Horschig, J.M.; Bronkhorst, M.; Floor-Westerdijk, M.; Valenza, G.; Dudink, J.; Colier, W.N.J.M. Estimation of Respiratory Rate from Functional Near-Infrared Spectroscopy (fNIRS): A New Perspective on Respiratory Interference. Biosensors 2022, 12, 1170. https://doi.org/10.3390/bios12121170.
