Open AccessInvestigation of multiple frequency recognition from single‐channel steady‐state visual evoked potential for efficient brain–computer interfaces application
Author(s) -
Purushothaman Geethanjali,
Prakash Prashanth R.,
Kothari Saurabh
Publication year - 2018
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2017.0220
Subject(s) - canonical correlation , computer science , harmonics , pattern recognition (psychology) , speech recognition , channel (broadcasting) , identification (biology) , artificial intelligence , fundamental frequency , spectral density , brain–computer interface , electroencephalography , acoustics , telecommunications , physics , botany , quantum mechanics , voltage , biology , psychology , psychiatry
In this study, the authors have examined a single‐channel electroencephalogram from O z for identification of seven visual stimuli frequencies with multivariate synchronisation index (MSI) and canonical correlation analysis (CCA). Authors investigated the feasibility in three case studies with varying overlapped as well as non‐overlapped window lengths. The visual stimuli frequencies ≤10 Hz are considered in case study I and >10 Hz in case study II. Case study III contains frequencies of both case studies I and II. All the case studies revealed that CCA outperforms MSI for reference signals constituting fundamental, one subharmonics, and three super‐harmonics. The results revealed that the accuracy of identification improves with 50% overlap in both the algorithms. Further, recognition accuracy is studied with varying combination sub‐ and super‐harmonics for case study III with 50% overlap. The results revealed that CCA and MSI perform better with reference signals constituting fundamental and twice fundamental frequency compared with traditional power spectral density analysis (PSDA). In addition to recognition accuracy, the information bit transfer rate is also higher in CCA relative to MSI and PSDA.