Using random forests for classifying motor imagery EEG

David Steyrl; Reinhold Scherer; Gernot Müller-Putz

Using random forests for classifying motor imagery EEG

David Steyrl, Reinhold Scherer, Gernot Müller-Putz

Institute of Neural Engineering (7090)

Research output: Contribution to conference › Poster

Abstract

One crucial issue for accurate sensorimotor rhythm (SMR) Brain- Computer Interfaces (BCIs) control is the selection of the most discriminative oscillatory EEG components. The Random Forests (RF) ensemble classifier is particularly interesting in this context.

Original language	English
Publication status	Published - Jan 2013
Event	TOBI Workshop IV Practical Brain-Computer Interfaces for End-Users: Progress and Challenges - Sion, Switzerland Duration: 23 Jan 2013 → 25 Jan 2013

Conference

Conference	TOBI Workshop IV Practical Brain-Computer Interfaces for End-Users: Progress and Challenges
City	Sion, Switzerland
Period	23/01/13 → 25/01/13

Fields of Expertise

Human- & Biotechnology

Access to Document

Steyrl 2013 TOBI Workshop IV SionFinal published version, 935 KB

Cite this

@conference{35f54312862c429c9e136fbfa56d4855,

title = "Using random forests for classifying motor imagery EEG",

abstract = "One crucial issue for accurate sensorimotor rhythm (SMR) Brain- Computer Interfaces (BCIs) control is the selection of the most discriminative oscillatory EEG components. The Random Forests (RF) ensemble classifier is particularly interesting in this context.",

author = "David Steyrl and Reinhold Scherer and Gernot M{\"u}ller-Putz",

year = "2013",

month = jan,

language = "English",

note = "TOBI Workshop IV Practical Brain-Computer Interfaces for End-Users: Progress and Challenges ; Conference date: 23-01-2013 Through 25-01-2013",

}

TY - CONF

T1 - Using random forests for classifying motor imagery EEG

AU - Steyrl, David

AU - Scherer, Reinhold

AU - Müller-Putz, Gernot

PY - 2013/1

Y1 - 2013/1

N2 - One crucial issue for accurate sensorimotor rhythm (SMR) Brain- Computer Interfaces (BCIs) control is the selection of the most discriminative oscillatory EEG components. The Random Forests (RF) ensemble classifier is particularly interesting in this context.

AB - One crucial issue for accurate sensorimotor rhythm (SMR) Brain- Computer Interfaces (BCIs) control is the selection of the most discriminative oscillatory EEG components. The Random Forests (RF) ensemble classifier is particularly interesting in this context.

M3 - Poster

T2 - TOBI Workshop IV Practical Brain-Computer Interfaces for End-Users: Progress and Challenges

Y2 - 23 January 2013 through 25 January 2013

ER -

Using random forests for classifying motor imagery EEG

Abstract

Conference

Fields of Expertise

Access to Document

Fingerprint

Cite this