Selection of relevant features for classification of movements from single movement-related potentials using a genetic algorithm

E. Yom-Tov; G. F. Inbar

Selection of relevant features for classification of movements from single movement-related potentials using a genetic algorithm

E. Yom-Tov
, G. F. Inbar

Technion-Israel Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

12 Scopus citations

Abstract

Classification of movement-related potentials recorded from the scalp to their corresponding limb is a crucial task in brain-computer interfaces based on such potentials. This paper demonstrates how the features for such a task can be selected from a large bank of features using a genetic algorithm. We show that it is possible to differentiate between the movements of contralateral fingers with a classification accuracy of 77% using a small number of features (10-20) selected from a bank containing roughly 1000 features.

Original language	English
Pages (from-to)	1364-1366
Number of pages	3
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	2
State	Published - 2001
Externally published	Yes
Event	23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Istanbul, Turkey Duration: 25 Oct 2001 → 28 Oct 2001

Cite this

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title = "Selection of relevant features for classification of movements from single movement-related potentials using a genetic algorithm",

abstract = "Classification of movement-related potentials recorded from the scalp to their corresponding limb is a crucial task in brain-computer interfaces based on such potentials. This paper demonstrates how the features for such a task can be selected from a large bank of features using a genetic algorithm. We show that it is possible to differentiate between the movements of contralateral fingers with a classification accuracy of 77\% using a small number of features (10-20) selected from a bank containing roughly 1000 features.",

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AB - Classification of movement-related potentials recorded from the scalp to their corresponding limb is a crucial task in brain-computer interfaces based on such potentials. This paper demonstrates how the features for such a task can be selected from a large bank of features using a genetic algorithm. We show that it is possible to differentiate between the movements of contralateral fingers with a classification accuracy of 77% using a small number of features (10-20) selected from a bank containing roughly 1000 features.

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JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

T2 - 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 25 October 2001 through 28 October 2001

ER -

Selection of relevant features for classification of movements from single movement-related potentials using a genetic algorithm

Abstract

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