Picture of wind turbine against blue sky

Open Access research with a real impact...

The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs.

The Energy Systems Research Unit (ESRU) within Strathclyde's Department of Mechanical and Aerospace Engineering is producing Open Access research that can help society deploy and optimise renewable energy systems, such as wind turbine technology.

Explore wind turbine research in Strathprints

Explore all of Strathclyde's Open Access research content

Identification of time-frequency EEG features modulated by force direction in arm isometric exertions

Nasseroleslami, B. and Lakany, H. and Conway, B. A. (2011) Identification of time-frequency EEG features modulated by force direction in arm isometric exertions. In: 2011 5th International IEEE/EMBS conference on neural engineering (NER). International IEEE EMBS Conference on Neural Engineering . IEEE, New York, pp. 422-425. ISBN 9781424441419

Full text not available in this repository. (Request a copy from the Strathclyde author)

Abstract

Electroencephalographic (EEG) activity associated with human motor tasks has been studied in time domain and time-frequency representations. Various classification and decoding techniques have been used to extract movement or motor task parameters from EEG such as direction of an isometrically exerted force. Identification of time and time-frequency regions that contain the highest directional information can considerably enhance the efficiency of decoding and classification algorithms. In this paper we have addressed this issue for directional arm isometric exertions to 4 different directions in horizontal plane. We have used the non-parametric Permutational ANOVA to identify time-frequency regions capturing the highest level of inter-group variance as a measure of directional information. There are information-rich regions in delta, theta, alpha, and beta bands after corresponding visual cues. Parietal regions show higher directional information during planning compared to execution. The results can be used for pattern classification and decoding of motor parameters in Brain-Computer-Interfacing (BCI) and BCI-rehabilitation.