Neurotransmitter vesicles released at the presynaptic axon terminal diffuse across the synaptic cleft and open specific ion channels on the membrane of the postsynaptic neuron. Ionic currents through these channels lead to a change in the membrane potential; excitatory synapses depolarize the cell (increase the membrane potential) while inhibitory synapses hyperpolarize the cell (decrease its membrane potential) or shunt the membrane potential to its resting value of about −70 mV (measured as the potential of the intracellular space with respect to the extracellular space). These postsynaptic potentials (PSPs) give rise to intracellular currents; see Fig. 17.1d. It is important to note that most inhibitory synapses are close to or even on the soma of the neuron where they can strongly influence the likelihood of the postsynaptic neuron to fire an action potential. In contrast, excitatory synapses are most abundant further away from the soma, on the branches of the dendritic tree. Therefore, both excitatory and inhibitory postsynaptic currents are typically towards the soma of the neuron. If the net potential change at the soma, taking into account the number and type of simultaneously active synapses and their distances from the soma, exceeds a threshold value of about −35 mV, the neuron fires an action potential (AP) that actively propagates along the axon towards presynaptic terminals of other synapses. Since the AP involves transient opening of local ion channels and thus a local change in the membrane potential, the intracellular current associated with an AP flows both forward and backward with respect to the propagation of the AP.

Although both postsynaptic currents and currents due to action potentials generate magnetic fields, the antiparallel arrangement of intracellular AP current components constitutes a quadrupole whose field diminishes rapidly with distance. When measuring the fields at a distance much larger than the length of significant AP current flow, the fields due to an AP are substantially weaker than those due to the largely unidirectional postsynaptic currents, which can be modeled as current dipoles.

The detectability of neural currents is also affected by their time course; the slower postsynaptic currents (tens of ms in duration) are more likely to co-occur than the faster APs (1–2 ms). Since the fields of single neural events are by far too weak to be measured by MEG or EEG, concerted action of tens or hundreds of thousands of neurons is needed for extracranial detection [2, 9]. Moreover, these simultaneously active neurons need to have their dendrites oriented roughly in parallel for the fields to sum up constructively. Fortunately, apical dendrites of pyramidal neurons are arranged in such a manner, and indeed the postsynaptic currents in these neurons produce the bulk of MEG and EEG signals.

In rare cases also axonal activity, i.e., APs, can be recorded with MEG. Bends in axons destroy the symmetry of the forward and backward currents and give rise to a dipolar component. However, the short duration of the AP still hampers its detection. Yet, sometimes, external stimulation can phase-lock the APs so well that massive averaging can make APs detectable; for example, responses from human auditory brain stem have been recorded with MEG [10]. These responses partly reflect axonal activity.

Neuromagnetic fields within centimeters from the scalp are on the order of 100 femtoteslas (fT). For example, the Earth’s magnetic field (50–90 μT) is thus 9 orders of magnitude (billion times) stronger. Similarly, moving vehicles and many electric devices produce magnetic fields that are many orders of magnitude stronger than any neuromagnetic signal. Therefore, measuring these weak fields is extremely challenging, and a combination of ultrasensitive detectors and magnetic shielding is needed.

Currently all MEG systems in routine use are based on superconducting quantum interference devices (SQUIDs) which are magnetic field sensors that provide femtotesla-range sensitivity. As the name implies, these sensors exploit superconductivity and thus require cryogenic temperatures to operate. SQUIDs are typically made of low-critical-temperature superconductors that need to be cooled down to temperatures of liquid helium (boiling point 4.2 K or −269 °C). Despite the sufficient sensitivity of SQUIDs for MEG, alternative sensors are actively being sought since liquid helium is expensive and its supply is limited. For example, high-critical-temperature SQUIDs that operate at liquid-nitrogen (T = 77 K) temperatures [11] and atomic (optical) magnetometers [12] have been demonstrated to be capable of recording MEG, but further development is needed for these sensors to outperform helium-cooled SQUIDs in practical MEG devices.

To capture the neuromagnetic fields above the whole scalp and to enable localizing the neural sources generating the measured fields, large helmet-shaped arrays of SQUID sensors are used. State-of-the-art systems comprise over 300 sensors distributed across the scalp. Due to the low temperatures required, the sensor helmet is placed inside a cryogenic vessel, Dewar, which is designed to minimize the transfer of heat to the liquid-helium bath inside the vessel while allowing the neuromagnetic fields to pass undistorted to the sensors.

As mentioned earlier, magnetic shielding is typically needed to protect the neuromagnetic measurement from interfering ambient magnetic fields such as those from traffic, power lines, elevators, and laboratory equipment. The most common shielding method is a magnetically shielded room, which is made of mu-metal (an alloy with very high permeability) and aluminum. Such a passive shield can be augmented with active systems that measure the interfering field and apply currents to coils that produce fields that counteract the interference. The magnetically shielded room requires space and is a substantial expense. Attempts to measure MEG without such a room have been made, but unfortunately robust operation in urban environments is yet to be demonstrated.

As is evident from the above, compared to EEG, MEG is considerably more expensive and not portable. However, MEG offers superior spatial resolution and to some extent complementary sensitivity with respect to EEG. These features outweigh the higher cost of instrumentation when the goal is to localize brain activity with high accuracy. MEG may also facilitate the development of brain–computer interfaces even if the eventual application does not use MEG.

While the sensor signals are already indicative of brain activity, the full potential of MEG can only be exploited by subjecting the measured multichannel data to mathematical operations that (a) suppress external interference, (b) improve the signal-to-noise ratio (SNR) of the brain signals of interest, and (c) estimate the locations of the neural sources underlying the measured signals. These steps are discussed in the following. Special attention is paid to their real-time application.

First, let us define the concept of signal space as it becomes handy when discussing the following methods. A measurement with N channels can be described as a signal vector in an N-dimensional virtual space where each measurement channel spans one axis. The spatial distribution, or pattern on the sensors, corresponds to the direction of that vector whereas the strength of the signal is reflected as the length of this vector. Thus, a neural source with a fixed location and orientation corresponds to a signal vector to a fixed direction; variation of the strength of that source only affects the length of the vector. Conversely, a source whose location or orientation varies over time corresponds to a signal vector whose direction changes over time.

Often the signal of interest is confined to a specific part of the signal space, i.e., only a limited set of signal patterns is generated by the neural processes underlying the measured signal. This set is referred to as a signal subspace. For example, when measuring with a whole-scalp MEG system or a high-density EEG system, the sequence of responses evoked by a visual stimulus fall within a certain subspace which is likely to be separate from the subspace spanned by, e.g., auditory responses. Likewise, external magnetic interference is confined to a specific subspace which is separate from that of brain signals as will be discussed in the following.

For a more detailed and mathematical treatment of signal space in the context of MEG and EEG, see [13].