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Introduction

  • Gürkan YılmazEmail author
  • Catherine Dehollain
Chapter
  • 946 Downloads
Part of the Analog Circuits and Signal Processing book series (ACSP)

Abstract

“How does the brain work?” is one of the Yilmaz questions that has been asked the most throughout the history of medical sciences. The question is addressed from different perspectives by all branches of science, in particular by the life sciences. Although all seek a different answer, there is something common which triggers all these researches: observation. With curiosity, thats what makes the beginning of a scientific study. From electrical engineering perspective, observation regarding this question is performed via recording the electrical signals generated by the neurons and interpreting those results. Recording neural activities plays an important role in numerous applications ranging from brain mapping to implementation of brain-machine interfaces (BMI) to recover lost functions or to understand the mechanisms behind the neurological disorders such as essential tremor, Parkinsons disease [1] and epilepsy [2, 3]. It also constitutes the first step of a closed-loop therapy system which additionally employs a stimulator and a decision mechanism. Such systems are envisaged to record neural anomalies and then stimulate corresponding tissues to cease such activities. Methods for recording the neural signals have evolved to its current state since decades, and the evolution still goes on. This chapter introduces the fundamentals of the new generation neural recording systems: implantable wireless neural recording systems with a case study on in-vivo epilepsy monitoring. Throughout this chapter, firstly, we have defined the problem and then, the motivation to solve this problem, introducing the systems anticipated benefits. Next, challenges to be encountered while realizing such a system have been explained and our approach has been briefly introduced.

Keywords

Neural recording Epilepsy ECoG iEEG Resective surgery Micro electrode array Ripple Fast ripple Multi-unit activity 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.EPFL RFIC Research GroupLausanneSwitzerland

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