Abstract
Spike trains and local field potentials (LFPs) are two different manifestations of neural activity recorded simultaneously from the same electrode array and contain complementary information of stimuli or behaviors. This paper proposes a tensor product kernel based decoder, which allows modeling the sample from different sources individually and mapping them onto the same reproducing kernel Hilbert space (RKHS) defined by the tensor product of the individual kernels for each source, where linear regression is conducted to identify the nonlinear mapping from the multi-type neural responses to the stimuli. The decoding results of the rat sensory stimulation experiment show that the tensor-product-kernel-based decoder outperforms the decoders with either single-type neural activities.
Original language | English |
---|---|
Title of host publication | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS |
Pages | 1331-1334 |
Number of pages | 4 |
DOIs | |
State | Published - Dec 14 2012 |
Event | 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States Duration: Aug 28 2012 → Sep 1 2012 |
Other
Other | 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 |
---|---|
Country | United States |
City | San Diego, CA |
Period | 8/28/12 → 9/1/12 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing
- Biomedical Engineering
- Health Informatics