We recorded whole scalp magnetoencephalographic (MEG) signals simultaneously with the surface electromyogram from upper and lower limb muscles of six healthy right-handed adults during voluntary isometric contraction. The 15- to 33-Hz MEG signals, originating from the anterior bank of the central sulcus, i.e., the primary motor cortex, were coherent with motor unit firing in all subjects and for all muscles. The coherent cortical rhythms originated in the hand motor area for upper limb muscles (1st dorsal interosseus, extensor indicis proprius, and biceps brachii) and close to the foot area for lower limb muscles (flexor hallucis brevis). The sites of origin corresponding to different upper limb muscles did not differ significantly. The cortical signals preceded motor unit firing by 12-53 ms. The lags were shortest for the biceps brachii and increased systematically with increasing corticomuscular distance. We suggest that the motor cortex drives the spinal motoneuronal pool during sustained contractions, with the observed cortical rhythmic activity influencing the timing of efferent commands. The cortical rhythms could be related to motor binding, but the rhythmic output may also serve to optimize motor cortex output during isometric contractions.

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Multichannel measurement with hundreds of channels oversamples a curl-free vector field, like the magnetic field in a volume free of sources. This is based on the constraint caused by the Laplace's equation for the magnetic scalar potential; outside of the source volume the signals are spatially band limited. A functional solution of Laplace's equation enables one to separate the signals arising from the sphere enclosing the interesting sources, e.g. the currents in the brain, from the magnetic interference. Signal space separation (SSS) is accomplished by calculating individual basis vectors for each term of the functional expansion to create a signal basis covering all measurable signal vectors. Because the SSS basis is linearly independent for all practical sensor arrangements, any signal vector has a unique SSS decomposition with separate coefficients for the interesting signals and signals coming from outside the interesting volume. Thus, SSS basis provides an elegant method to remove external disturbances. The device-independent SSS coefficients can be used in transforming the interesting signals to virtual sensor configurations. This can also be used in compensating for distortions caused by movement of the object by modeling it as movement of the sensor array around a static object. The device-independence of the decomposition also enables physiological DC phenomena to be recorded using voluntary head movements. When used with properly designed sensor array, SSS does not affect the morphology or the signal-to-noise ratio of the interesting signals.

Measurement of external magnetic fields provides information on electric current distribution inside an object. For example, in magnetoencephalography modern measurement devices sample the magnetic field produced by the brain in several hundred distinct locations around the head. The signal space separation ͑SSS͒ method creates a fundamental linear basis for all measurable multichannel signal vectors of magnetic origin. The SSS basis is based on the fact that the magnetic field can be expressed as a combination of two separate and rapidly converging expansions of harmonic functions with one expansion for signals arising from inside of the measurement volume of the sensor array and another for signals arising from outside of this volume. The separation is based on the different convergence volumes of the two expansions and on the fact that the sensors are located in a source current-free volume between the interesting and interfering sources. Individual terms of the expansions are shown to contain uncorrelated information of the underlying source distribution. SSS provides a stable decomposition of the measurement into a fundamental device-independent form when used with an accurately calibrated multichannel device. The external interference signals are elegantly suppressed by leaving the interference components out from the reconstruction based on the decomposition. Representation of multichannel data with the SSS basis is shown to provide a large variety of applications for improved analysis of multichannel data.

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We recorded somatosensory evoked magnetic fields from ten healthy, right-handed subjects with a 122-channel whole-scalp SQUID magnetometer. The stimuli, exceeding the motor threshold, were delivered alternately to the left and right median nerves at the wrists, with interstimulus intervals of 1, 3, and 5 s. The first responses, peaking around 20 and 35 ms, were explained by activation of the contralateral primary somatosensory cortex (SI) hand area. All subjects showed additional deflections which peaked after 85 ms; the source locations agreed with the sites of the secondary somatosensory cortices (SII) in both hemispheres. The SII responses were typically stronger in the left than the right hemisphere. All subjects had an additional source, not previously reported in human evoked response data, in the contralateral parietal cortex. This source was posterior and medial to the SI hand area, and evidently in the wall of the postcentral sulcus. It was most active at 70-110 ms.

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