Neural decoding using Kernel-based functional representation of ECoG recordings

Abstract

A Kernel-based Feature Representation (KFR) approach is proposed to extract patterns from multi-channel time-series of measured brain activity. To search for Inter-Channel Similarity (ICS), we introduce a kernel function to embed input data through a sliding window. We use the ICS-based data representation to obtain relevant channel dependencies along time. Hence, the introduced KFR that seeks for spatio-temporal relationships among channels facilitates brain activity analysis relating to neural decoding tasks.We test the KFR on two neural decoding collections of macaque Electrocorticographic signals. Obtained results show that proposed KFR improves both data visual interpretability and stimulus prediction.