| Start Date: | 6/16/2017 | Start Time: | 10:00 AM |
| End Date: | 6/16/2017 | End Time: | 12:00 PM |
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Event Description
BIOMED Master's Thesis Defense
Title:
NeuroHub Network Integration: Time Synchronization Device for Multimodal Brain Imaging and Hyperscanning Research
Speaker:
Neha Thomas, MS Candidate, School of Biomedical Engineering, Science and Health Systems
Advisor:
Hasan Ayaz, PhD, Associate Research Professor, School of Biomedical Engineering, Science and Health Systems
Abstract:
Significant progress has been made over the last decades in understanding the physiological and neural bases of cognitive processes and behavior. The advent of new and improved sensors enables monitoring the human body and brain activity in natural environments, with cost-effective, mobile and wearable form factor systems. As neuroimaging and brain sensing technologies are further developed, there is an expanding interest for using multiple systems concurrently on i) the same brain: multimodal/hybrid measurements for better identification of neurophysiological markers, and ii) multiple brains: hyperscanning for novel investigations of brain functions during social interactions. Particularly for functional neuroimaging, such as Functional Near Infrared Spectroscopy (fNIRS) and Electroencephelography (EEG), precise time synchronization of experimental events with acquired datasets is necessary for proper analysis and interpretation of results. However, there are currently no standards for interoperability and neuroimaging systems have many different designs and interfaces. Furthermore, it is often cumbersome to come up with a custom solution to each new research setup based on the devices involved.
The original NeuroHub, a plug- and-play time synchronization device developed at Drexel University, attempted to alleviate some of the complications associated with custom setups and time synchronization. The original NeuroHub, now renamed the NeuroHub core module, relayed any incoming signal to one of its four serial ports, TTL port, and parallel port, to all other ports on the device and can be connected to multiple sending/listening devices or computers. Although one or more of these legacy ports are present in various neuroimaging systems, modern computing systems require more sophisticated alternatives.
This thesis proposes a solution and improvement to the original NeuroHub, by incorporating time synchronization over a network as an information transfer layer. The network solution enables more flexible experimental configurations and expands the compatible plug-and-play system range. Moreover, this new approach eliminates the need for multiple wires, while still being able to service multiple clients. The new NeuroHub, called the NeuroHub network module, is also able to directly interface with a typical RS-232 serial port and offers the best of both worlds – ability to interface with network and legacy hardware ports for complete customizability, flexibility and backward compatibility.
The NeuroHub network module consists of a Raspberry Pi fitted with a serial port add-on board. The device transmits any event markers received from either networked or serial ports and relays them to the other opened ports. Verification testing confirmed that the device transmits with 100% accuracy and the latency to send a byte from one computer to the other via the network module was minimal, ranging from sub-millisecond speeds to 7 ms depending on the use of serial ports, baud-rate, and configuration order.
The NeuroHub network module was tested in Brain Compute Interface (BCI) setups using OpenViBE as a stimulus presenter and EEG data recording, with COBI Studio as the fNIRS data recording software to receive markers all through the NeuroHub network module. This simple use case demonstrates the utility of the network module for simplification of complex functional neuroimaging, neuroergonomics and BCI research experimental setups. |
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Location:
CONQUER Collaborative, Monell Chemical Senses Center, Room 114, located at 3508 Market Street. |
Audience:
Undergraduate StudentsGraduate StudentsFacultyStaff |
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