
다채널 센서 배열을 사용하여 수행되는 현대 신경생리학적 기록에는 종종 더 낮은 차원의 활동 패턴이 관찰되는 동적 구조가 내재되어 있지만, 기존 방법으로는 이를 신뢰성 있게 식별하기 어렵다. 이 연구에서는 생물리학적으로 영감을 받은 상태 공간 표현을 사용하여 다채널 데이터에서 진동 성분을 데이터 주도적으로 추출하는 새로운 성분 분석 방법을 제안한다.


coremsg

Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100’s to 1000’s. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post-hoc manner from univariate analyses, or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgement in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as Oscillation Component Analysis (OCA). These parameters – the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations – all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.

Significance Statement Neuroscience studies often involve simultaneous recordings in a large number of sensors in which a smaller number of dynamic components generate the complex spatio-temporal patterns observed in the data. Current blind source separation techniques produce sub-optimal results and are difficult to interpret because these methods lack an appropriate generative model that can guide both statistical inference and interpretation. Here we describe a novel component analysis method employing a dynamic generative model that can decompose high-dimensional multivariate data into a smaller set of oscillatory components are learned in a data-driven way, with parameters that are immediately interpretable. We show how this method can be applied to neurophysiological recordings with millisecond precision that exhibit oscillatory activity such as electroencephalography and magnetoencephalography.

### Competing Interest Statement

The authors have declared no competing interest.

A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings

다채널-신경생리학적-기록에서-해석-가능한-진동-성분을-추출할-수-있는-동적-생성-모델


다채널 신경생리학적 기록에서 해석 가능한 진동 성분을 추출할 수 있는 동적 생성 모델
