Linked Life Data

9342887

Source:http://linkedlifedata.com/resource/pubmed/id/9342887

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
9
pubmed:dateCreated
1997-12-22
pubmed:abstractText
Individuals can learn to control the amplitude of EEG activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. For one-dimensional (i.e., vertical) cursor movement, a linear equation translates the EEG activity into cursor movement. To translate an individual's EEG control into cursor control as effectively as possible, the intercept in this equation, which determines whether upward or downward movement occurs, should be set so that top and bottom targets are equally accessible. The present study compares alternative methods for using an individual's previous performance to select the intercept for subsequent trials. In offline analyses, five different intercept selection methods were applied to EEG data collected while trained subjects were moving the cursor to targets at the top or bottom edge of the screen. In the first two methods-moving average, and weighted sum-a single intercept was selected for the entire 1-2 sec period of each trial. In the other three methods-blocked moving average, blocked weighted sum, and blocked recursive sum (a variation of the weighted sum)-an intercept was selected for each 200-ms segment of the trial. The results from these methods were compared in regard to their balance between upward and downward movements and their consistency of performance across trials. For all subjects combined, the five methods performed similarly. However, performance across subjects was more consistent for the moving average, blocked moving average, and blocked recursive sum methods than for the weighted sum and blocked weighted sum methods. Due to its consistent performance and its computational simplicity, the moving average method, using the five most recent pairs of top and bottom trials, appears to be the method of choice.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
0013-5585
pubmed:author
pubmed:issnType
Print
pubmed:volume
42
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
226-33
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
1997
pubmed:articleTitle
EEG-based communication: evaluation of alternative signal prediction methods.
pubmed:affiliation
Department of Medical Informatics, Graz University of Technology, Austria.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't