Compressive Sensing system for recording of ECoG signals in-vivo

fact that in order to integrated the neural input signals in a range of frequencies till 10 kHz, thearea needed to implement the resistor and the capacitors is too large to be a design whichcould spread out all over all the paths are needed in all the channels of the chip array. Due tothis, switched capacitor architecture has been deployed. By considering an implementation thatmakes depend the magnitude of the components on a clock frequency, area constraints arerelaxed, so that has revealed as a practicable design to implement an analog design of the CSlikedacquisition system.7.2. Next stepsDuring the accomplishment of this project, Compressive Sensing-based systems has revealedas a promising compression method to be applied in biological signals which satisfy sparsityconditions.As it is shown along the previous chapters, Compressive Sensing systems are becoming moreand more relevant in order to efficiently implement high information rate acquisition circuits.However, it is a new methodology, and many design parameters have not been settled yet, sothe project has turned into the first approximation to an entire CS-platform based on analogimplementation for EPFL and IMEC.After the work which is circumscribed to the scope of the master thesis, a real SC-based mixingand integration path will be implemented in IMEC based on real components. In the same way,LNA and ADC will be integrated along with the mixer, integrator and random generator whichhave been individually designed in order to attain a complete analog path for futuremultichannel implementations.For the other hand, in order to be able to apply this array-based neural acquisition system torecord as many different neural signals as possible, it is recommended to study the bestsparsifying basis for each of the characteristic neural signals. Therefore, neural sciences couldbenefit of clearer recordings in order to better understand the illness which are related with thebrain. Similarly, other reconstruction methods, as [37], could be applied and compare with theones have been used in this work, in order to verify which one gives the best results.Finally, the new CS-application which has been presented [31] has to be deeper studied inorder to specify its better applicability in neural recording.72