How fast can f-VEP BCIs ever be?

Author

Sengelmann, Malte ; Engel, Andreas K. ; Maye, Alexander

Author_Institution

Dept. of Neurophysiol. & Pathophysiology, Univ. Med. Center Hamburg-Eppendorf, Hamburg, Germany

fYear

2013

fDate

6-8 Nov. 2013

Firstpage

1049

Lastpage

1053

Abstract

VEP-based BCIs offer a number of advantages that make them a promising candidate for applications in everyday environments: They do not require user training, the high signal-to-noise ratio of VEPs allows reliable classification, and high information transfer rates (ITRs) of up to ~100 bits/min have been achieved during recent years. In this article we estimate an upper bound of the ITR for VEP BCIs that use frequency and phase coding for classification (f-VEP BCIs). The estimate is based on an idealized classification process that operates on real EEG data of the steady-state (SSVEP) from naïve subjects. Our study yields subject-specific upper bounds in the range of approx. 200 to 500 bits/min. We identify causes for the significantly lower ITR of existing f-VEP BCIs and suggest solutions that can narrow the gap to the upper bound.

Keywords

brain-computer interfaces; electroencephalography; medical signal processing; neurophysiology; signal classification; visual evoked potentials; ITR upper bound estimation; electroencephalography; f-VEP BCI classification; frequency coding; information transfer rates; phase coding; real EEG data; signal-to-noise ratio; steady-state visual evoked potentials; subject-specific upper bounds; Accuracy; Feature extraction; Light emitting diodes; Spatial filters; Steady-state; Upper bound; Visualization;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on

Conference_Location

San Diego, CA

ISSN

1948-3546

Type

conf

DOI

10.1109/NER.2013.6696117

Filename

6696117