Compressive Sensing system for recording of ECoG signals in-vivo

frequency when all the electrodes are scanned, solely a few of them will detect a spike. If atsampling frequency it is created a vector containing what each of the electrodes captures in thatclock cycle, that vector most probably will be sparse as well, and so it can be compressed byapplying CS. By repeating this operation during all the acquisition time (N samples), N vectorswill be composed, each of them with M measurements. In Fig.5.2.1 it is sketched the SCSconception for the first clock time multielectrode acquisition, which leads to a spatially collectedsparse signal. When acquisition time is completed, original signals can be reconstructed byreversing the operation during the off-line signal processing.Figure 5.2.1. Spatial CS example. The composition of the first sample of all of the electrodes gives rise toa sparse signal.If spatial sparsity is guaranteed, several benefits arise from SCS, without applying thresholdingor signal-dependent pre-processing neural signals can be recovered by achieving a largerCompression Ratio, because signals of length N can be recovered by M measurements, thelatter one depending on the number of electrodes of the array. Similarly, the new compactrandom generator which is included in Chapter 4 can be used without eventual drawbacks ofcorrelation between paths, because each of the samples of a signal is involved in a different CSspatial operation.As in 5.1., the reconstruction methods which have been considered in order to test this new CSapproach have been the ones based on SPGL1 [30], (see 5.3). In Fig.5.2.2 it can be observedthe original and reconstructed signals in 10 ms for two sample channels using MATLAB andcircuit simulations, which has been included in [31].51