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Development and testing of a multimodal neuroimaging protocol

What are you making?

The fNIRS group at the University of Calgary is developing a simultaneous multimodal imaging protocol which will enable the integration of neuroelectric (EEG), hemodynamic (fNIRS) and cardiovascular (ECG) metrics. Given the novelty of recording with this many devices, substantive engineering and testing was required in order to ascertain proper time-synchronization between devices. Our study will evaluate individual differences between subjects over the course of an entire week of recordings.

How are OpenBCI tools being applied?

OpenBCI tools are being incorporated into our multimodal setup as both EEG and ECG measurements will be gathered using the Cython+Daisy. In upcoming work, we will also evaluate the use of embedded accelerometers in the assessment of sway metrics.

Why is this important?

Several clinical and non-clinical applications rely heavily on the ability to properly classify neurological states. Among the advances proposed, the use of multiple modalities has been proposed. However, inter-subject variability (the difference from person to person) is known to degrade the accuracy of classification algorithms in BCI applications.

Our study will integrate all of the aforementioned modalities in addition to a series of other daily factors which may help increase the accuracy of fNIRS, EEG and ECG. Each signals reliability will be evaluated in a series of task which includes rest, working memory, standing and a motor movement task.

Members of the research group (from left to right). First row: Andrew Lapointe, Joel Burma, Ibukun Oni, Ateyeh Soroush; Second row: Jessica Ritchie, Stephen Jiang, Jamey Loewen and Jeff Dunn.

What is the timeline for the next few months?
We are currently preparing to double our sample size in order to strengthen our preliminary findings. We will also be testing a new waist-clip design allowing the use of OpenBCIs waist-mounted accelerometers in sway assessments.

We are currently developing adaptive algorithms. The next step will be to collect simulated data with variability and perform tests to determine the validity of the hypothesis proposed. Once it has been proven, it will be tested on healthy people before testing on patients. Based on the results, we will design studies to improve the model’s adaptability.

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