Optical Co-registration of MRI and On-scalp MEG

Zetter, Rasmus; Iivanainen, Joonas; Parkkonen, Lauri

doi:10.1038/s41598-019-41763-4

Cited by 67 publications

(48 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another barrier for the development of a speech-BCI is that MEG is currently not suitable for this application due to its high cost, size, and immobility. Encouraging recent work on wearable, OPMbased MEG systems (Boto et al, 2018;Roberts et al, 2019;Zetter et al, 2019) has shown that it is possible to build portable MEG machines with a significantly reduced cost and size (equivalent to the size of a helmet). This technological advance opens the possibility for utilizing OPM-based MEG as a speech-BCI to potentially restore communication for locked-in patients.…”

Section: Toward a Next-generation Speech-bcimentioning

confidence: 99%

Decoding Imagined and Spoken Phrases From Non-invasive Neural (MEG) Signals

2020

View full text Add to dashboard Cite

Speech production is a hierarchical mechanism involving the synchronization of the brain and the oral articulators, where the intention of linguistic concepts is transformed into meaningful sounds. Individuals with locked-in syndrome (fully paralyzed but aware) lose their motor ability completely including articulation and even eyeball movement. The neural pathway may be the only option to resume a certain level of communication for these patients. Current brain-computer interfaces (BCIs) use patients' visual and attentional correlates to build communication, resulting in a slow communication rate (a few words per minute). Direct decoding of imagined speech from the neural signals (and then driving a speech synthesizer) has the potential for a higher communication rate. In this study, we investigated the decoding of five imagined and spoken phrases from single-trial, non-invasive magnetoencephalography (MEG) signals collected from eight adult subjects. Two machine learning algorithms were used. One was an artificial neural network (ANN) with statistical features as the baseline approach. The other was convolutional neural networks (CNNs) applied on the spatial, spectral and temporal features extracted from the MEG signals. Experimental results indicated the possibility to decode imagined and spoken phrases directly from neuromagnetic signals. CNNs were found to be highly effective with an average decoding accuracy of up to 93% for the imagined and 96% for the spoken phrases.

show abstract

Section: Toward a Next-generation Speech-bcimentioning

confidence: 99%

Decoding Imagined and Spoken Phrases From Non-invasive Neural (MEG) Signals

2020

View full text Add to dashboard Cite

show abstract

“…In order to enable source localisation, accurate knowledge of the sensor locations and orientations relative to the brain is required. This was provided by a 3-dimensional optical imaging system (structure IO camera (Occipital Inc., San Francisco, CA, USA) coupled to an Apple iPad, operating with Skanect Pro software) and an anatomical MRI scan Homölle and Oostenveld, 2019;Zetter et al, 2019). The anatomical MRI was recorded using a 3 T Philips Ingenia MRI system, running an MPRAGE sequence, at an isotropic spatial resolution of 1 mm.…”

Section: Opm Co-registrationmentioning

confidence: 99%

Measuring functional connectivity with wearable MEG

Boto

Hill

Rea

et al. 2020

Preprint

View full text Add to dashboard Cite

Optically-pumped magnetometers (OPMs) offer the potential for a step change in magnetoencephalography (MEG) enabling wearable systems that: provide improved data quality; accommodate any subject group; allow data capture during movement and offer a reduction in costs. However, OPM-MEG is still a nascent technology and, to realise its potential, it must be shown to facilitate key neuroscientific measurements, such as the characterisation of human brain networks. Networks, and the connectivities that underlie them, have become a core area of neuroscientific investigation, and their importance is underscored by many demonstrations of their perturbation in brain disorders. Consequently, a demonstration of network measurements via OPM-MEG would be a significant step forward. Here, we aimed to show that a wearable 50-channel OPM-MEG system enables characterisation of the electrophysiological connectome. To this end, we characterise connectivity in the resting state and during a simple visuo-motor task, using both OPM-MEG and a state-of-the-art 275-channel cryogenic MEG device. Our results show that connectome matrices from OPM and cryogenic systems exhibit an extremely high degree of similarity, with correlation values >70 %. This value is not measurably different to the correlation observed between connectomes measured in different subject groups, on a single scanner. In addition, similar differences in connectivity between individuals (scanned multiple times) were observed in cryogenic and OPM-MEG data, again demonstrating the fidelity of OPM-MEG data. This demonstration shows that a nascent OPM-MEG system offers results similar to a cryogenic device, even despite having ~5 times fewer sensors. This adds weight to the argument that OPMs will ultimately supersede cryogenic sensors for MEG measurement.

show abstract

“…To fix the position of the subject's head inside the helmet, dummy sensors were inserted into sockets on the sides of the helmet so that they gently pressed on the head on each side. MEG-MRI coregistration was performed using an optical scanner as described by Zetter, Iivanainen, and Parkkonen (2019); an example of coregistered OPM positions with respect to the subject's head is shown in Figure 1.…”

Section: Meg Acquisitionmentioning

confidence: 99%

Potential of on‐scalp MEG: Robust detection of human visual gamma‐band responses

Iivanainen

Zetter

Parkkonen

2019

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

Electrophysiological signals recorded intracranially show rich frequency content spanning from near‐DC to hundreds of hertz. Noninvasive electromagnetic signals measured with electroencephalography (EEG) or magnetoencephalography (MEG) typically contain less signal power in high frequencies than invasive recordings. Particularly, noninvasive detection of gamma‐band activity (>30 Hz) is challenging since coherently active source areas are small at such frequencies and the available imaging methods have limited spatial resolution. Compared to EEG and conventional SQUID‐based MEG, on‐scalp MEG should provide substantially improved spatial resolution, making it an attractive method for detecting gamma‐band activity. Using an on‐scalp array comprised of eight optically pumped magnetometers (OPMs) and a conventional whole‐head SQUID array, we measured responses to a dynamic visual stimulus known to elicit strong gamma‐band responses. OPMs had substantially higher signal power than SQUIDs, and had a slightly larger relative gamma‐power increase over the baseline. With only eight OPMs, we could obtain gamma‐activity source estimates comparable to those of SQUIDs at the group level. Our results show the feasibility of OPMs to measure gamma‐band activity. To further facilitate the noninvasive detection of gamma‐band activity, the on‐scalp OPM arrays should be optimized with respect to sensor noise, the number of sensors and intersensor spacing.

show abstract

Optical Co-registration of MRI and On-scalp MEG

Cited by 67 publications

References 32 publications

Decoding Imagined and Spoken Phrases From Non-invasive Neural (MEG) Signals

Decoding Imagined and Spoken Phrases From Non-invasive Neural (MEG) Signals

Measuring functional connectivity with wearable MEG

Potential of on‐scalp MEG: Robust detection of human visual gamma‐band responses

Contact Info

Product

Resources

About