Extracting Motion-Related Subspaces from EEG in Mobile Brain/Body Imaging Studies using Source Power Comodulation
- Publication Type:
- Conference Proceeding
- International IEEE/EMBS Conference on Neural Engineering, NER, 2019, 2019-March pp. 344 - 347
- Issue Date:
|Gehrke-etal-2019_IEEE_MotionSubspaceReoncstruction.pdf||Published version||869.9 kB|
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© 2019 IEEE. Mobile Brain/Body Imaging (MoBI) is an emerging non-invasive approach to investigate human brain activity and motor behavior associated with cognitive processes in natural conditions. MoBI studies and analyses pipelines combine brain measurements, e.g. Electroencephalography (EEG), with motion data as participants conduct tasks with near-natural behavior. Within the field however, standard source decomposition and reconstruction pipelines largely rely on unsupervised blind source separation (BSS) approaches and do not consider movement information to guide the decomposition of oscillatory brain sources. We propose the use of a supervised spatial filtering method, Source Power Co-modulation (SPoC), for extracting source components that co-modulate with body motion. Further, we introduce a method to validate the quality of oscillatory sources in MoBI studies. We illustrate the approach to investigate the link between hand and head movement kinematics and power dynamics of EEG sources while participants explore an invisible maze in virtual reality. Stable oscillatory source envelopes correlating with hand and head motion were isolated in all subjects, with median ρ =.13 for all sources and median ρ =.16 for sources passing the selection criteria. The results indicate that it is possible to improve movement related source separation to further guide our understanding of how movement and brain dynamics interact.
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