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Neural Recording Interface and Signal Processing

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MEA and Cell.png


Over the last decades, an increasing burden that neurological disorders applied on patients and societies attracts more attentions. A better understanding of the brain and neuro system is needed. Since the number of electrophysiological signals generated by neurons are normally massive, a large scale, high accuracy, intense density and robust artificial device is needed. To record electrical signal within the brain, modulate its activity and even stimulate certain event, direct interfacing with the brain is the main approach in neuro science research. In the mid-1990s, the first neuro-prosthetic device implanted in human is appeared, led by a research group from University of Michigan. Recently, cell signal recording using penetrating electrodes grows steadily and its outcomes has overcome a series of fundamental challenges like difficult of stimulation, nonideal overall performance , low quality data and lack of data analysis methods.

To study the network activity of neurons and tackle these challenges, researchers are using various methods, including imaging techniques, such as calcium imaging, intracellular recording methods, such as patch clamp, and extracellular recording techniques, such as electrical imaging. Through the recording of optical or electrical signals generated by neurons, such as action potentials (APs) and local-field potentials (LFPs), single-neuron behavior and neural signaling in neuronal networks can be studied. Among all these approached, Microelectrode Array(MEA) has been one of the most efficient ways of acquiring neural signals from a large number of neurons in terms of number of recording sites, temporal resolution, spatial resolution, and signal-to-noise ratio.

In ETHz we invented a new neural signal detecting scheme that can detect neurons firing action potential (AP) spikes ( Currently we are designing the next generation Ultra Low Power Neural Recording Interface and we are inviting students to join us to design the low power on chip spike sorting algorithm.


In this project, the student will

1. Get to know more about Neural Recording state of the art work

2. Get familiar and implement the Neural spike sorting algorithm.

3. Explore new digital circuit structure for Neuro Recoridng processing

Status: Unavailable

Looking for master student who is interseted in doing semester project. If you are interested in this challenging position on an exciting and challenging topic, please send your most recent curriculum vitae including a transcript of grades by email to:

Yiyang Chen <>


The ideal candidate should have a multi-disciplinary background, strong mathematical aptitude and programming skills (matlab or python).


  • 20% Literature review
  • 20% Theory
  • 60% Programming


Prof. Taekwang Jang <>

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