Single-trial classification of evoked responses to auditory tones using OPM- and SQUID-MEG

Joonas Iivanainen*, Tony R. Carter, Michael C.S. Trumbo, Jim McKay, Samu Taulu, Jun Wang, Julia M. Stephen, Peter D.D. Schwindt, Amir Borna

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)

Abstract

Objective. Optically pumped magnetometers (OPMs) are emerging as a near-room-temperature alternative to superconducting quantum interference devices (SQUIDs) for magnetoencephalography (MEG). In contrast to SQUIDs, OPMs can be placed in a close proximity to subject’s scalp potentially increasing the signal-to-noise ratio and spatial resolution of MEG. However, experimental demonstrations of these suggested benefits are still scarce. Here, to compare a 24-channel OPM-MEG system to a commercial whole-head SQUID system in a data-driven way, we quantified their performance in classifying single-trial evoked responses. Approach. We measured evoked responses to three auditory tones in six participants using both OPM- and SQUID-MEG systems. We performed pairwise temporal classification of the single-trial responses with linear discriminant analysis as well as multiclass classification with both EEGNet convolutional neural network and xDAWN decoding. Main results. OPMs provided higher classification accuracies than SQUIDs having a similar coverage of the left hemisphere of the participant. However, the SQUID sensors covering the whole helmet had classification scores larger than those of OPMs for two of the tone pairs, demonstrating the benefits of a whole-head measurement. Significance. The results demonstrate that the current OPM-MEG system provides high-quality data about the brain with room for improvement for high bandwidth non-invasive brain-computer interfacing.

Original languageEnglish
Article number056032
Journal Journal of Neural Engineering
Volume20
Issue number5
DOIs
Publication statusPublished - 1 Oct 2023
MoE publication typeA1 Journal article-refereed

Keywords

  • auditory cortex
  • brain-computer interface
  • classification
  • decoding
  • magnetoencephalography
  • optically pumped magnetometer

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