On the analysis of single versus multiple channels of electromagnetic brain signals

When extracting information from electromagnetic (EM) brain function through recordings such as the electroencephalogram (EEG) it is often assumed that signal processing techniques must be applied to multiple simultaneous recordings in order to obtain useful results. However, sometimes only a single channel of EEG recording is available or desirable. In this paper we objectively assess a novel methodology which exploits only a single measurement channel to extract information of interest relatively independent of channel location (relative to the source of interest).

The method relies on a combination of a matrix of delay vectors constructed from the single channel measurement, along with constrained independent component analysis, which incorporates prior information into the process.

Here, we use synthetically generated seizure EEG, composed of real, normal multi-channel EEG onto which is superimposed synthetic epileptic "seizure-like" activity, at different signal-to-noise (SNR) levels, through an equivalent current dipole model.

We show that the method can extract desired information from single channels with a reasonable accuracy even at very small SNR and from channels distant from the focus of the activity. This provides a powerful technique capable of extracting multiple sources underlying single channel recordings and will be useful in situations where only single channel EM recordings of brain function are desirable, such as would be the case in wearable or implantable recording devices.