Detection of neuroelectric signals from multiple data channels by optimum linear filter methods.

A general mathematical formulation for predicting achievable levels of detection of neuroelectric signals in associated background noise is provided for the case where such data are obtainable from multiple recording loci. The formulation depends upon the signal-to-noise ratios and bandwidths of the incorporated data channels and on the degree of noise dependency between channels. The signals are assumed to relate to the same event such as the production of an incipient movement but need not have the same waveshape. The detection technique is based upon passage of the data through a series of optimum linear filters. The outputs of the filters can either be summated in analog fashion prior to making the detection decision, or their separate outputs and separate detection decisions can be treated combinatorially to determine a detection decision for the aggregate. The former method is superior to the latter for small numbers of data channels. The latter method may be preferable where variation in signal latency exists between channels. Incorporation of information from multiple channels with independent noise can result in significant improvement over detection signals from a single channel provided that the signal-to-noise level of each additional channel exceeds that of the aggregate divided by square rootK, K being the total number of added channels. However, the presence of noise dependency between channels may severly restrict the degree of imporvement realizable through the multiple channel detection operation, irrespective of the number of added cha-nels. The implication of this result on the possibility of using EEG signals predicting incipient movement to control the operation of a motor prosthesis is profound. Inter-channel noise dependency with correlation coefficienr filter method of detection levels required for prosthesis operation. Zero lag correlation coefficients between electrical recordings from separate cortical loci both in man a