Open AccessSub‐band classification of decomposed single event‐related potential co‐variants for multi‐class brain–computer interface: a qualitative and quantitative approach
Author(s) -
Ahirwal Mitul Kumar,
Kumar Anil,
Singh Girish K.,
Suri Jasjit S.
Publication year - 2016
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2015.0200
Subject(s) - class (philosophy) , brain–computer interface , event (particle physics) , computer science , interface (matter) , artificial intelligence , pattern recognition (psychology) , data mining , speech recognition , physics , psychology , electroencephalography , parallel computing , neuroscience , bubble , quantum mechanics , maximum bubble pressure method
Subject dependent and co‐variant nature of electroencephalography (EEG)/event‐related potential (ERP) are still hurdles in the development of generalised EEG system such as clinical diagnoses and brain–computer interface (BCI) systems. Presently, classification of BCI classes is limited due to consideration of several trials of ERP as average case for generalisation or some time single‐trial ERP detection is preferred for subject specific schemes. Present study tries to develop a methodology for utilisation of co‐variant nature of ERP, based on the sub‐band decomposed ERPs. Proposed idea also explores a new dimension in BCI system design, as multi‐class classification for execution of multiple commands through single ERP. Sub‐band ERPs and their power spectrum‐based features are extracted and classified successfully with 70.64% accuracy using artificial neural network.