New Publications!

Its been a pretty great week for the Neural Instrumentation Lab in terms of publications. My former graduate student, Alessandro Napoli, and I have recently published two papers together about multielectrode array dynamics with rat and human neurons. We're pretty proud of these, if we do say so ourselves...

Article 1
Investigating brain functional evolution and plasticity using micro electrode array technology
Brain Research Bulletin
http://www.sciencedirect.com/science/article/pii/S0361923015300423

Article 2
Comparative Analysis of Human and Rodent Brain Primary Neuronal Culture Spontaneous Activity Using Micro-Electrode Array Technology
Journal of Cellular Biochemistry
http://onlinelibrary.wiley.com/doi/10.1002/jcb.25312/abstract

Multielectrode Arrays - Progress!

My graduate student extraordinare has been working on growing neurons in multielectrode array dishes. These are special petri dishes with electrodes built right in so that we can stimulate the neurons and record from them. We can use this preparation to learn about how neurons communicate with each other. Its pretty neat. The equipment necessary to run these studies is not cheap, but luckily we stumbled across an unused rig at the medical school which they graciously agreed to let us use. Even better, they gave us our own incubator, which is critical since the cells must be kept at a constant temperature and CO2 concentration otherwise they will rapidly perish.

We've recently presented an update on our research at the BMES 2013 conference in Seattle. We are also in the process of submitting our first peer-reviewed paper on this work. Our first innovation has been the implementation of a statistical technique known as the False Discovery Rate method to identify statistically significant connections between neurons. We then used the FDR method to track how those changes evolve over time. Our first experiment, which we performed as a retrospective study using data graciously donated us by Dr. Potter at Georgia Tech, studied changes that occur over the course of 40 days in vitro. We found that cell cultures go through three phases - an initial phase where the cells are unconnected and disorganized; a second phase in which synaptic connections increase in both number and distance; and and a third phase in which the cells either die or massively prune off synaptic connections.

We've also made a really interesting discovery that cell cultures derived from the same source neural tissue tend to evolve along very similar time courses, whereas cultures derived from different neural tissue tend to evolve with very different time courses. We have some hypotheses as to why this happens but we can't know for sure without running more experiments.

At present, we're working on two projects. The first is to assess to what extent electrical stimulation causes changes in neural cultures. We are doing this by selectively applying stimuli to different quadrants of each neural dish and studying how neural connectivity changes as a result. We expect to have results on this later this year or early next year. Our second project is especially interesting but we've decided to keep a lid on it for the time being! We'll talk about it publicly once the work has been peer reviewed and published.

Neural Engineering Data Consortium

I am proud to announce a new effort we are undertaking at Temple called the Neural Engineering Data Consortium. The NEDC is being founded to help bring the power of big data and competitive common evaluations to neural engineering. Efforts such as this have been very effective in organizing other data intensive research areas such as human language processing.

We have been planning this effort for a while, and we're now pleased to announce that we've raised an initial round of capital to support planning and development efforts. Specifically, we seek two main goals over the next year

1. Complete a planning exercise that will identify how the NEDC should be structured, administrated, resourced, and interfaced to the neural engineering community. A big part of this effort will be getting buy-in from different elements of the neural engineering community.
2. Develop a proof-of-concept big-data corpus comprising 20,000 clinical EEGs derived from archives at Temple University Hospital. This corpus will be released freely to the neural engineering community.

We have begun the process of explaining the NEDC to the community and soliciting their feedback and participation. Last week, I presented the NEDC to participants at the 5th International Brain Computer Interface Meeting in Monterey, California. We are also planning a presentation at the 6th International IEEE Neural Engineering Conference this fall in San Diego. Furthermore, we are hosting a one day symposium at the 2013 IEEE GlobalSIP Conference. That symposium will focus on big data and will have a mix of invited speakers and contributed papers.

Our paper from the BCI Meeting can be found here.

My slides from that same meeting can be found here.

For more information, feel free to email me directly: iobeid@temple.edu.

Fuzzy Logic-Based Spike Sorting, Ctd.

Our new manuscript is now available online!

Balasubramanian K, Obeid I (2011) "Fuzzy logic-based spike sorting system" J Neurosci Methods. [Epub ahead of print]

Common Spatial Patterns

Congratulations to lab member Alessandro Napoli who presented a poster titled, "Combined Spatial Pattern and Spectral Filtering for EEG-Based BCIs" at last week's Conference on Neural Engineering in Cancun.

Alessandro is studying methods of de-noising EEG signals for brain machine interface applications. We now have an IRB in place to study his proposed technique in quite a few people, so hopefully his summer should be quite productive and we'll have some good results to publish come fall.

The technique he proposes combines Common Spatial Patterns (CSP) filtering with spectral analysis to look for areas of high activity in the brain during BMI tasks. His preliminary data is encouraging.

We are learning that acquiring high-quality EEG signals is as much art as it is science. We'll put together a blog post later this summer with some of our newly-acquired wisdom.

You can download the full-sized poster here.

Fuzzy Logic-Based Spike Sorting

We've just received word that a manuscript authored by my graduate student Karthikeyan Balasubramanian will be published in the Journal of Neuroscience Methods. The paper, titled, "Fuzzy Logic-based Spike Sorting System" looks at how fuzzy logic can be used as an autonomous feature extraction algorithm for spike sorting. Its a pretty neat concept: spike features are measured and fuzzified, and then fuzzy logic is used to calculate a "fuzziness" index for each spike that identifies how similar that spike is to an ideal spike waveform. The fuzziness indicies can be clustered directly for a complete spike sorting solution. There are several advantages of our system. The first is that the fuzzy rules don't ever need to be modified, meaning that the system doesn't need any channel-by-channel calibration every day. Secondly, the sorter does not require that spikes be spatially aligned, as with principal component analysis. Spike alignment is computationally expensive. Finally, our system is computationally negligible to implement and can be built in an FPGA with hundreds of channels in parallel for a nice clean low-power solution.