English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  New Methods for the P300 Visual Speller

Biessmann, F.(2006). New Methods for the P300 Visual Speller (1). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CF97-E Version Permalink: http://hdl.handle.net/21.11116/0000-0002-87E5-3
Genre: Report

Files

show Files
hide Files
:
lab_rotation_[0].pdf (Publisher version), 513KB
Name:
lab_rotation_[0].pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Biessmann, F1, 2, Author              
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: Brain-Computer Interfaces (BCI’s) enable us to infer intentional control signals from brain activity. The Visual Speller is a BCI based on event related potentials (ERP’s) in the electroencephalogram, such as the P300 (a positive deflection in the EEG about 300 ms after a rarely occuring stimulus). In the classical paradigm one trial (i.e. prediction of one symbol) consists of successive highlightings of one or more symbol(s) on a visual grid presented to the subject. The stimulus events in which the symbol of interest was highlighted will result in an enhanced ERP. This ERP, being stronger than the ERP’s elicited by non-target stimulus events, can be used for prediction of the letter the subject was focussing on using some machine learning algorithm, for example the support vector machine. The more symbols are highlighted simultaneously the faster the speller could potentially work. A stimulus code that uses few events per trial (and thus shows many symbols at once) is called dense. The tradeoff against code density is that the signal to noise ratio becomes worse with increasing stimulus frequency: the P300 signal is reported to be strongest when the target symbol frequency is lowest. The stimulus code in which only one symbol per stimulus event is presented, is a maximally sparse code. It has been proposed that high bitrates of information transfer in a visual speller can best be achieved with sparse stimulus codes. However sparse codes have long trial durations. In order to improve the information transfer rate, we tried to use denser stimulus codes that present fewer stimulus events per trial. To investigate the effect of stimulus type on classification accuracy and the interdependence of stimulus code and type, we explored new stimulus types including ones exploiting recent findings in neuropsychology, such as change blindness and isoluminant color motion. We show that, using appropriate stimuli, denser codes, and hence fewer stimulus events, yield sufficient classification accuracy to achieve competitive bitrates.

Details

show
hide
Language(s):
 Dates: 2006-11
 Publication Status: Published in print
 Pages: 21
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: 1
BibTex Citekey: 4477
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Lab Report of the Max Planck Institute for Biological Cybernetics
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 1 Sequence Number: - Start / End Page: - Identifier: -