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High-Sensitivity rf Detection Using an Optically Pumped Comagnetometer Based on Natural-Abundance Rubidium with Active Ambient-Field Cancellation

Physical Review Applied

Bainbridge, Jonathan E.; Claussen, Neil C.; Iivanainen, Joonas; Schwindt, Peter S.

To detect a specific radio-frequency (rf) magnetic field, rf optically pumped magnetometers (OPMs) require a static magnetic field to set the Larmor frequency of the atoms equal to the frequency of interest. However, unshielded and variable magnetic field environments (e.g., an rf OPM on a moving platform) pose a problem for rf OPM operation. Here, we demonstrate the use of a natural-abundance rubidium vapor to make a comagnetometer to address this challenge. Our implementation builds upon the simultaneous application of several OPM techniques within the same vapor cell. First, we use a modified implementation of an OPM variometer based on 87Rb to detect and actively cancel unwanted external fields at frequencies ≲60 Hz using active feedback to a set of field control coils. In this experiment, we exploit this stabilized field environment to implement a high-sensitivity rf magnetometer using 85Rb. Using this approach, we demonstrate the ability to measure rf fields with a sensitivity of approximately 9 fT Hz-1/2 inside a magnetic shield in the presence of an applied field of approximately 20 μT along three mutually orthogonal directions. This demonstration opens up a path toward completely unshielded operation of a high-sensitivity rf OPM.

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Visual decoding of phrases from occipital neuromagnetic signals

International IEEE/EMBS Conference on Neural Engineering, NER

Dash, Debadatta; Ferrari, Paul; Borna, Amir B.; Iivanainen, Joonas; Schwindt, Peter S.; Wang, Jun

Orthographic visual perception (reading) is encoded via a widespread dynamic interaction between different language centers of the brain and visual cortex. In this study, we investigated orthographic visual perception decoding with Magnetoencephalography (MEG), where phrases were visually presented to participants. We compared the decoding performance obtained with sensors within the occipital lobe that obtained with sensors covering the whole head. Two naive machine learning classifiers namely support vector machines (SVM) and linear discriminant analysis (LDA) were used. Experimental results indicated that the decoding performance using only occipital sensors is similar to the performance obtained with all sensors within the task period, which were all above chance level. In addition, temporal analysis by taking short-time windows showed that the occipital sensors were more discriminative near onset compared to later time periods, while using the whole head sensor setup at later time periods performed slightly better than occipital sensors. This finding may indicate a sequential order (from visual cortex to other areas beyond occipital lobe) during visual speech perception.

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7 Results
7 Results