Mining within-trial oscillatory brain dynamics to address the variability of optimized spatial filters

TL;DR

This paper introduces a clustering approach to analyze within-trial oscillatory brain dynamics, addressing variability issues in spatial filter solutions and enhancing neurotechnological applications like brain-computer interfaces.

Contribution

It proposes a method to cluster oscillatory components based on their temporal envelope dynamics, capturing subject-specific within-trial brain activity patterns.

Findings

Components' temporal dynamics are highly subject-specific.

Average of seven clusters per subject confined to specific frequency bands.

Method applicable to various spatial filtering algorithms.

Abstract

Data-driven spatial filtering algorithms optimize scores such as the contrast between two conditions to extract oscillatory brain signal components. Most machine learning approaches for filter estimation, however, disregard within-trial temporal dynamics and are extremely sensitive to changes in training data and involved hyperparameters. This leads to highly variable solutions and impedes the selection of a suitable candidate for, e.g.,~neurotechnological applications. Fostering component introspection, we propose to embrace this variability by condensing the functional signatures of a large set of oscillatory components into homogeneous clusters, each representing specific within-trial envelope dynamics. The proposed method is exemplified by and evaluated on a complex hand force task with a rich within-trial structure. Based on electroencephalography data of 18 healthy subjects, we found that the components' distinct temporal envelope dynamics are highly subject-specific. On average, we obtained seven clusters per subject, which were strictly confined regarding their underlying frequency bands. As the analysis method is not limited to a specific spatial filtering algorithm, it could be utilized for a wide range of neurotechnological applications, e.g., to select and monitor functionally relevant features for brain-computer interface protocols in stroke rehabilitation.