Compressive Sensing system for recording of ECoG signals in-vivo

time, which has been approximated as 33 μs, which is not exactly the inverse of the samplingfrequency. In any case, below reconstruction is shown, and it is probed that both of thecompressed signals give rise a good approximation to the same recovered signal.The reconstruction methods that have been considered are the Basic Pursuit Denoising (BPD)method withand the Least Absolute Shrinkage and Selection Operator (LASSO)method with , provided by SPGL1 [30]. They are well-defined in 5.3. The result of therecovered signal can be observed in Fig.5.1.4 and Fig.5.1.5.Figure 5.1.5. BPDN method reconstruction comparison.5.2. Multi-Channel Implementation of Compressive SensingDuring the study of the CS applications directly related with the initial purpose of the project, thescope of CS systems which are involved with sparse signals in time or frequency has spread toa new applicability which can be defined as Spatial Compressive Sensing, SCS. The resultsderiving from this approach has been submitted as publication in August 2012. Keeping in mindthe huge amount of data which are recorded in neural arrays and the need to compress it, thesmall area and high integration of the electrodes let a high resolution of the brain zone undersignal acquisition, what implies that electrical impulses can be recorded in almost a singleneuron.Due to the sparse nature of the spikes which are registered, when a group of neurons is active,the surrounding groups will be inactive till the stimulus propagates with certain latency. Thisusual scenery gives rise to some electrodes which are catching spikes and many others whichare inactive, so, it is clear to see, that it does exist a spatial sparsity, because at sampling50