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NeuroHub networking integration: time synchronization device for multimodal brain imaging and hyperscanning research
Thesis   Open access

NeuroHub networking integration: time synchronization device for multimodal brain imaging and hyperscanning research

Neha Thomas
Master of Science (M.S.), Drexel University
Jun 2017
DOI:
https://doi.org/10.17918/etd-7458
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Abstract

Brain--Imaging Brain-computer interfaces Biomedical Engineering
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's 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 Electroencephalography (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 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 large number of clients. The new NeuroHub is also able to directly interface with typical RS-232 serial ports and offers the best of both worlds - ability to interface with network and legacy hardware ports for complete customizability, flexibility and backward compatibility. The new NeuroHub network module consists of a Raspberry Pi Model 1B 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 new 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 NeuroHub. This simple use case demonstrates the utility of the new NeuroHub for simplification of complex functional neuroimaging, neuroergonomics and BCI research experimental setups.

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