Interpretable Visualization and Higher-Order Dimension Reduction for ECoG Data

TL;DR

This paper introduces an interpretable dimension reduction method for ECoG data, enabling neuroscientists to visualize and explore complex brain activity recordings with enhanced clarity and relevance.

Contribution

We develop a sparse, smooth, higher-order PCA extension tailored for large, multi-dimensional ECoG datasets, improving interpretability and analysis capabilities.

Findings

Effective visualization of ECoG data during speech processing

Enhanced electrode and frequency band selection

Improved interpretability of complex neural signals

Abstract

ElectroCOrticoGraphy (ECoG) technology measures electrical activity in the human brain via electrodes placed directly on the cortical surface during neurosurgery. Through its capability to record activity at a fast temporal resolution, ECoG experiments have allowed scientists to better understand how the human brain processes speech. By its nature, ECoG data is difficult for neuroscientists to directly interpret for two major reasons. Firstly, ECoG data tends to be large in size, as each individual experiment yields data up to several gigabytes. Secondly, ECoG data has a complex, higher-order nature. After signal processing, this type of data may be organized as a 4-way tensor with dimensions representing trials, electrodes, frequency, and time. In this paper, we develop an interpretable dimension reduction approach called Regularized Higher Order Principal Components Analysis, as well as an extension to Regularized Higher Order Partial Least Squares, that allows neuroscientists to explore and visualize ECoG data. Our approach employs a sparse and functional Candecomp-Parafac (CP) decomposition that incorporates sparsity to select relevant electrodes and frequency bands, as well as smoothness over time and frequency, yielding directly interpretable factors. We demonstrate the performance and interpretability of our method with an ECoG case study on audio and visual processing of human speech.