On Robust Detection Of Brain Stimuli With Ramanujan Periodicity Transforms

Keywords

Brain computer interface; Nested periodic matrices; Ramanujan periodicity transform; Ramanujan subspace; Steady-state visual evoked potentials

Abstract

Visual Evoked Potentials (VEPs) are the brain responses to repetitive visual stimuli. The ability to detect the underlying frequencies of VEPs is crucial to advancing Brain Computer Interfaces (BCIs). This paper considers the detection of such frequencies using a Ramanujan Periodicity Transform based model. We analyze the performance of a generalized likelihood ratio detector and derive the exact distributions of the sufficient statistics under hypotheses corresponding to different stimulus frequencies using confluent hyper-geometric functions, along with flexible approximate distributions. Choosing stimulation periods with non-overlapping divisors is shown to enhance the detection performance. Our analysis provides guidelines for efficient design of stimulus waveforms for BCIs.

Publication Date

4-10-2018

Publication Title

Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017

Volume

2017-October

Number of Pages

729-733

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ACSSC.2017.8335440

Socpus ID

85050979961 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85050979961

This document is currently not available here.

Share

COinS