Magnetoencephalography: Exploring Brain's Magnetic Fields

The human brain, with its intricate network of neurons, is a marvel that has captivated scientists for centuries. Understanding the dynamics of brain activity is crucial for unraveling the mysteries of cognition, perception, and behavior. Various neuroimaging techniques have been developed to explore the intricacies of brain function, and one such advanced method is Magnetoencephalography (MEG). MEG offers a unique window into the brain’s activity by detecting and measuring the magnetic fields generated by neuronal activity. In this article by Academic Block, we will delve into the principles, applications, and significance of MEG in neuroscience.

1. Introduction to Magnetoencephalography

1.1 Basics of MEG

Magnetoencephalography, or MEG, is a non-invasive neuroimaging technique that measures the magnetic fields produced by the electrical activity of neurons in the brain. Unlike other imaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), MEG provides high temporal resolution on the order of milliseconds, allowing researchers to capture the rapid dynamics of neural processes.

The fundamental principle behind MEG lies in the fact that whenever neurons in the brain are active, they generate small electrical currents. According to Maxwell’s equations, these electrical currents give rise to magnetic fields. MEG detects and records these magnetic fields using highly sensitive sensors called superconducting quantum interference devices (SQUIDs). These sensors are capable of measuring extremely weak magnetic fields, making MEG a powerful tool for studying neural activity.

1.2 Historical Development of MEG

The roots of MEG can be traced back to the mid-20th century. The first successful measurement of the magnetic field generated by the human brain was achieved by David Cohen in 1968. However, it wasn’t until the 1980s that MEG gained widespread recognition as a valuable neuroimaging tool. Technological advancements, particularly in the development of superconducting materials and SQUID detectors, significantly enhanced the sensitivity and practicality of MEG.

2. The Technology Behind MEG

2.1 Superconducting Quantum Interference Devices (SQUIDs)

SQUIDs are at the heart of MEG technology. These devices are highly sensitive to magnetic fields and are capable of detecting minute fluctuations in them. Superconducting materials, which lose all electrical resistance at low temperatures, are used to construct SQUIDs. MEG systems typically consist of an array of SQUID sensors arranged around the head to capture the magnetic fields generated by neural activity from different directions.

2.2 MEG System Components

A typical MEG system comprises three main components: the sensor array, the cryogenic system, and the data acquisition system. The sensor array, as mentioned, consists of SQUID sensors strategically placed to capture the magnetic fields. The cryogenic system maintains the superconducting state of the SQUIDs by cooling them to extremely low temperatures. The data acquisition system processes the signals from the sensors, allowing the reconstruction of neural activity patterns.

3. Advantages and Limitations of MEG

3.1 Advantages

MEG offers several advantages that make it a valuable tool in neuroscience research. One of its key strengths is its exceptional temporal resolution, allowing researchers to observe the precise timing of neural events. This is particularly important for understanding the fast-paced dynamics of cognitive processes, such as perception and decision-making. Additionally, MEG is non-invasive, meaning it does not require the administration of contrast agents or exposure to ionizing radiation, as in some other imaging techniques.

Moreover, MEG can provide insights into the spatial localization of neural activity, although its spatial resolution is not as high as that of fMRI. The combination of high temporal resolution and reasonable spatial resolution makes MEG well-suited for studying brain function in real-time.

3.2 Limitations

While MEG has many strengths, it is not without its limitations. One notable limitation is its relatively poor spatial resolution compared to techniques like fMRI. MEG measures magnetic fields, and the relationship between the measured magnetic fields and the underlying neural sources is mathematically complex. As a result, accurately localizing the origin of these fields can be challenging.

Another limitation is that MEG is sensitive primarily to tangential sources of neural activity, meaning it may not detect signals from deep brain structures as effectively as it does from cortical regions. Additionally, the presence of magnetic interference from external sources, such as metal objects in the environment, can impact the quality of MEG data.

4. Applications of MEG in Neuroscience

4.1 Cognitive Neuroscience

MEG has been instrumental in advancing our understanding of cognitive processes. Researchers use MEG to investigate a wide range of cognitive functions, including perception, attention, memory, and language processing. The high temporal resolution of MEG allows scientists to dissect the sequence of neural events underlying these cognitive processes.

For example, MEG has been employed to study the neural mechanisms of visual perception. By presenting visual stimuli and recording the corresponding MEG signals, researchers can unravel the stages of visual processing and the timing of information flow within the visual system.

4.2 Clinical Applications

In addition to its contributions to basic neuroscience, MEG has found important applications in clinical settings. One notable application is in the pre-surgical mapping of brain function. In patients with epilepsy or brain tumors, accurate localization of critical functional areas is crucial for surgical planning. MEG, with its ability to identify eloquent cortical areas, helps neurosurgeons map the brain’s functional architecture and avoid damaging vital regions during surgery.

Moreover, MEG is used in the diagnosis and study of neuropsychiatric disorders such as schizophrenia, autism, and epilepsy. By examining the aberrant patterns of neural activity associated with these conditions, researchers aim to develop better diagnostic tools and therapeutic interventions.

4.3 Brain-Computer Interfaces (BCIs)

MEG holds promise in the field of brain-computer interfaces, which enable communication and control of external devices through direct brain signals. By decoding the neural activity recorded by MEG, researchers are exploring the development of BCIs that could benefit individuals with severe motor disabilities. This technology has the potential to enhance the quality of life for individuals who are unable to communicate or interact with the external world using traditional means.

5. Mathematical equations behind the Magnetoencephalography

The mathematical equations behind Magnetoencephalography (MEG) involve principles from electromagnetism and signal processing. The fundamental concept is based on Faraday’s law of electromagnetic induction, which relates changing magnetic fields to induced electric currents. In the context of MEG, neuronal electrical activity in the brain generates small, time-varying magnetic fields that can be measured by sensors. The mathematical formulation of MEG involves Maxwell’s equations, which describe the behavior of electric and magnetic fields in space. Let’s see the key mathematical equations that underpin the principles of MEG:

Maxwell’s Equations:

Maxwell’s equations describe the relationship between electric fields (E) and magnetic fields (B) in the presence of electric charges and currents. The equations are as follows:

Gauss’s Law for Electricity: ∇⋅E = ρ / ε0 ;

Gauss’s Law for Magnetism: ∇⋅B = 0 ;

Faraday’s Law of Electromagnetic Induction: ×E = −∂B / ∂t ;

Ampère’s Law with Maxwell’s Addition: ×B = μ0 [J + {ε0 (∂E / ∂t) }] ;


  • ∇⋅ represents the divergence operator.
  • × represents the curl operator.
  • ρ is the charge density.
  • J is the current density.
  • ε0 is the permittivity of free space.
  • μ0 is the permeability of free space.

Bioelectric Sources and Magnetic Fields:

In the context of MEG, neuronal activity in the brain generates electric currents. These currents, in turn, produce magnetic fields according to Ampère’s Law. The relationship between the current density (J) and the resulting magnetic field (B) is given by the Biot-Savart Law:

B(r) = ( μ0 / 4π ) ∫ [ {J(r′) × (r−r′)} / ∣r−r′∣3 ] dV ;


  • r is the observation point in space.
  • r′ represents the source points in the brain.
  • μ0 is the permeability of free space.

Forward Problem:

The forward problem in MEG involves predicting the magnetic field measured at the sensors (Bmeasured) based on the known distribution of bioelectric sources (J). The forward problem can be expressed mathematically as:

Bmeasured = ∫ B(r)⋅n dS ;


  • Bmeasured is the measured magnetic field.
  • B(r) is the magnetic field at a given point in space.
  • n is the unit normal vector to the sensor surface.
  • dS is an infinitesimal area on the sensor surface.

Inverse Problem:

The inverse problem in MEG involves estimating the distribution of bioelectric sources (J) in the brain based on the measured magnetic field (Bmeasured). The inverse problem is challenging due to the ill-posed nature of the mapping from sources to measurements. Various mathematical techniques, such as regularization and inverse modeling algorithms, are employed to solve the inverse problem and reconstruct the spatial distribution of neural activity.

6. Challenges and Future Directions

6.1 Overcoming Spatial Limitations

One of the ongoing challenges in MEG research is improving spatial localization. Efforts are underway to refine computational models that better map the relationship between measured magnetic fields and the underlying neural sources. Advanced signal processing techniques, combined with anatomical information from structural imaging modalities, may enhance the spatial accuracy of MEG.

6.2 Integration with Other Modalities

To overcome the limitations of individual neuroimaging techniques, researchers are increasingly combining MEG with other modalities such as fMRI and EEG. This multimodal approach allows for a more comprehensive understanding of brain function by leveraging the strengths of each technique. Integrating MEG with fMRI, for instance, enables researchers to merge high temporal resolution with detailed spatial information, providing a more complete picture of neural activity.

6.3 Portable MEG Systems

Traditional MEG systems are large, expensive, and confined to specialized laboratories. There is ongoing research to develop more portable and cost-effective MEG systems, expanding the accessibility of this technology. Portable MEG systems could facilitate research in diverse settings, including clinical environments and field studies.

7. Ethical Considerations and Privacy Concerns

As with any advanced technology, the use of MEG raises ethical considerations and privacy concerns. The detailed insights into brain function provided by MEG may have implications for personal privacy, cognitive enhancement, and potential misuse. It is essential for researchers, policymakers, and the public to engage in ethical discussions to ensure the responsible and transparent use of MEG technology.

Final Words

Magnetoencephalography stands at the forefront of neuroimaging technologies, offering a unique and valuable perspective on the dynamics of brain function. Its ability to capture the magnetic fields generated by neural activity with high temporal precision has opened new avenues for exploring cognition, diagnosing neurological disorders, and developing innovative brain-computer interfaces. While MEG has its challenges and limitations, ongoing research and technological advancements continue to enhance its capabilities and broaden its applications. In this article by Academic Block we have seen that, as our understanding of the brain deepens, MEG is likely to play an increasingly pivotal role in unraveling the intricate mysteries of the human mind. Please provide your comments below, it will help us in improving this article. Thanks for reading!

Academic References on Magnetoencephalography

  1. Cohen, D. (1972). Magnetoencephalography: Detection of the Brain’s Electrical Activity with a Superconducting Magnetometer. Science, 175(4022), 664–666.
  2. Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J., & Lounasmaa, O. V. (1993). Magnetoencephalography – theory, instrumentation, and applications to noninvasive studies of the working human brain. Reviews of Modern Physics, 65(2), 413–497.
  3. Ioannides, A. A. (Ed.). (2006). MEG: An Introduction to Methods. Oxford University Press.
  4. Baillet, S., Mosher, J. C., & Leahy, R. M. (2001). Electromagnetic Brain Mapping. IEEE Signal Processing Magazine, 18(6), 14–30.
  5. Niedermeyer, E., & da Silva, F. L. (2005). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins.
  6. Sekihara, K., & Nagarajan, S. S. (2008). Adaptive Spatial Filters for Electromagnetic Brain Imaging. Springer.
  7. Papanicolaou, A. C., Simos, P. G., & Castillo, E. M. (Eds.). (2003). Magnetoencephalography: From Signals to Dynamic Cortical Networks. Springer.
  8. Kakigi, R., & Kanno, A. (Eds.). (2003). Biomagnetism: An Interdisciplinary Approach. Nakanishiya Shuppan.
  9. Hämäläinen, M. S., & Sarvas, J. (1989). Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data. IEEE Transactions on Biomedical Engineering, 36(2), 165–171.
  10. Singh, K. D., Barnes, G. R., Hillebrand, A., Forde, E. M., & Williams, A. L. (2002). Task-Related Changes in Cortical Synchronization Are Spatially Coincident with the Hemodynamic Response. NeuroImage, 16(1), 103–114.
  11. Gross, J., Kujala, J., Hamalainen, M., Timmermann, L., Schnitzler, A., & Salmelin, R. (2001). Dynamic imaging of coherent sources: Studying neural interactions in the human brain. Proceedings of the National Academy of Sciences, 98(2), 694–699.
  12. Dale, A. M., Liu, A. K., Fischl, B. R., Buckner, R. L., Belliveau, J. W., Lewine, J. D., & Halgren, E. (2000). Dynamic Statistical Parametric Mapping: Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity. Neuron, 26(1), 55–67.
  13. Puce, A., & Hämäläinen, M. S. (2017). A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies. Brain Sciences, 7(5), 58.
  14. Lin, F.-H., & Witzel, T. (Eds.). (2018). MEG Source Imaging: Methods and Applications. Springer.

Hardware and software required for Magnetoencephalography


1. MEG System: The core hardware component is the MEG system itself, which includes:

  • Sensor Array: An array of superconducting quantum interference devices (SQUIDs) strategically placed around the head to measure the magnetic fields generated by neuronal activity.
  • Dewar: A cryogenic container that houses the SQUID sensors and maintains them at extremely low temperatures to ensure their superconducting state.

2. Head Localization System: For accurate spatial localization of MEG signals, a head localization system is used. This may include:

  • Head Position Indicator (HPI) Coils: Small coils attached to the subject’s scalp to continuously monitor head position.
  • 3D Digitizer: A device to digitize the positions of anatomical landmarks and the head position indicator coils for accurate spatial registration.

3. Stimulus Presentation and Response Collection: For experimental studies, equipment for presenting stimuli and collecting subject responses is necessary:

  • Presentation Software: Software to present visual, auditory, or other sensory stimuli to the subject.
  • Response Collection Devices: Devices such as response buttons or eye-tracking systems to record subject responses.

4. Shielding: To minimize external magnetic interference, MEG systems are typically housed in magnetically shielded rooms.

5. Computer System: A powerful computer system is required for real-time data acquisition and processing.


1. MEG Data Acquisition Software: Specialized software is needed to control the MEG system and acquire data. This software often includes real-time signal processing capabilities.

2. Signal Processing Software: To preprocess and analyze MEG data, signal processing software is essential. This includes:

  • Filtering: For removing noise and isolating specific frequency bands of interest.
  • Artifact Rejection: Tools for identifying and removing artifacts from the data.
  • Source Localization: Algorithms for estimating the locations of neural sources based on the recorded magnetic fields.

3. Coregistration Software: Software for aligning MEG data with structural MRI or other imaging modalities is necessary for accurate source localization.

4. Connectivity Analysis Software: For studying functional connectivity between different brain regions, specialized software is required. This may include tools for computing coherence, phase synchronization, or other connectivity metrics.

5. Statistical Analysis Software: To perform statistical analyses on MEG data, researchers often use statistical analysis software packages like MATLAB, Python (with libraries such as MNE-Python or FieldTrip), or specialized MEG analysis software.

6. Visualization Software: Software for visualizing MEG data and results is crucial for interpreting findings. This may include 3D brain reconstructions, time-frequency plots, and source localization visualizations.

7. Database and Storage: Given the large amounts of data generated in MEG studies, efficient database and storage solutions are essential for organizing, archiving, and sharing data.

Facts on Magnetoencephalography

1. Non-Invasive Nature: Magnetoencephalography (MEG) is a non-invasive neuroimaging technique, meaning it does not require any surgery or insertion of probes into the body. This makes it a safe method for studying brain activity.

2. Principle of Measurement: Magnetoencephalography (MEG) measures the magnetic fields produced by the electrical activity of neurons in the brain. It is based on the principle that whenever neurons are active, they generate small electric currents, and according to Maxwell’s equations, these currents produce magnetic fields.

3. Exceptional Temporal Resolution: One of the key strengths of MEG is its exceptional temporal resolution. It can capture the dynamics of neural activity with millisecond precision, allowing researchers to investigate the rapid changes in brain function associated with various cognitive processes.

4. SQUID Sensors: MEG systems use superconducting quantum interference devices (SQUIDs) as sensors. SQUIDs are extremely sensitive to magnetic fields, allowing for the detection of weak magnetic signals produced by the brain.

5. Cryogenic Cooling: To maintain the superconducting state of SQUIDs, MEG systems use a cryogenic cooling system. The sensors are cooled to extremely low temperatures, typically close to absolute zero (-273.15°C or -459.67°F).

6. Source Localization: MEG allows for the localization of the sources of neural activity in the brain. By analyzing the magnetic fields measured at the sensors, researchers can estimate the locations of active brain regions with reasonable spatial accuracy.

7. Sensitivity to Tangential Sources: MEG is particularly sensitive to tangentially oriented sources of neural activity. This means that it is well-suited for detecting signals from the cortical surface but may be less effective at capturing activity from deep brain structures.

8. Clinical Applications: MEG has practical applications in clinical settings. It is used for pre-surgical mapping in patients with epilepsy or brain tumors to identify critical functional areas and plan surgical interventions while minimizing the risk of postoperative neurological deficits.

9. Challenges in Source Localization: Despite its strengths, accurately localizing the sources of magnetic fields in the brain can be challenging due to the ill-posed nature of the inverse problem. Researchers use advanced mathematical models and regularization techniques to address this challenge.

10. Portable MEG Systems: Traditional MEG systems are large and confined to specialized laboratories. However, there is ongoing research to develop more portable and cost-effective MEG systems, expanding the accessibility of this technology to different settings, including clinical environments and field studies.

11. Multimodal Integration: Researchers often combine MEG with other neuroimaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to obtain a more comprehensive understanding of brain function. This multimodal integration allows for combining the strengths of each technique.

12. Ethical Considerations: The use of MEG raises ethical considerations related to privacy, cognitive enhancement, and potential misuse of neuroimaging data. Ethical discussions are important to ensure responsible and transparent use of MEG technology.

13. Brain-Computer Interfaces (BCIs): MEG has been explored for applications in brain-computer interfaces (BCIs). By decoding the neural signals recorded by MEG, researchers aim to develop BCIs that can assist individuals with severe motor disabilities in communication and control of external devices.

Key Applications of Magnetoencephalography

  1. Neural Oscillations and Brain Rhythms:

    • MEG has been crucial in uncovering the presence and functional significance of neural oscillations or brain rhythms. Different frequency bands, such as alpha, beta, gamma, and theta, have been linked to various cognitive processes.
    • Studies using MEG have revealed the role of synchronized neural oscillations in functions like attention, memory, and perception.
  2. Cortical Mapping and Localization:

    • MEG enables the mapping and localization of brain activity with high temporal precision. Researchers have used MEG to identify specific cortical regions associated with sensory, motor, and cognitive functions.
    • Cortical mapping using MEG is particularly valuable in pre-surgical planning, allowing surgeons to avoid damage to critical brain areas during procedures.
  3. Language Processing:

    • MEG studies have contributed significantly to our understanding of language processing in the brain. For example, researchers have used MEG to identify the timing and location of neural responses during language comprehension and production.
    • MEG has helped reveal the temporal dynamics of language-related processes, shedding light on how the brain processes linguistic information.
  4. Visual Perception and Attention:

    • Investigations into visual perception and attention have benefited from MEG’s ability to capture rapid changes in brain activity. MEG studies have explored the neural mechanisms underlying visual processing and selective attention.
    • Discoveries include insights into the timing of visual information processing and the role of different brain regions in attentional control.
  5. Motor Control and Planning:

    • MEG has played a key role in unraveling the complexities of motor control and planning. Researchers have used MEG to investigate the timing and organization of neural activity related to motor tasks.
    • Studies have provided insights into the involvement of different motor areas and the coordination of neural signals during voluntary movements.
  6. Sensorimotor Integration:

    • MEG has contributed to our understanding of sensorimotor integration, where sensory information is processed and integrated with motor commands. This is crucial for adaptive and coordinated behavior.
    • Investigations using MEG have revealed the neural processes involved in sensorimotor integration, including the role of feedback mechanisms.
  7. Clinical Applications:

    • MEG has made significant contributions to the diagnosis and understanding of various neurological and neuropsychiatric disorders. Studies using MEG have explored abnormalities in brain activity associated with conditions like epilepsy, schizophrenia, and autism.
    • In clinical settings, MEG has been used for pre-surgical mapping to identify eloquent cortical areas, guiding surgical interventions in patients with epilepsy or brain tumors.
  8. Brain Connectivity and Networks:

    • MEG has been instrumental in studying functional connectivity and network dynamics in the brain. Researchers use MEG data to explore how different brain regions communicate and synchronize during various tasks.
    • Discoveries include the identification of resting-state networks and changes in connectivity patterns associated with cognitive processes and neurological disorders.

Who is the father of Magnetoencephalography

The term “father of Magnetoencephalography” is often attributed to Dr. David Cohen. In 1968, David Cohen, an American physicist, successfully measured the magnetic fields generated by the human brain for the first time, marking a significant milestone in the development of Magnetoencephalography (MEG) as a neuroimaging technique. His groundbreaking work laid the foundation for the subsequent advancements and widespread use of MEG in neuroscience research and clinical applications. While many scientists and researchers have contributed to the development of MEG, David Cohen is often recognized for his pioneering efforts in the early stages of this field.

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