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EEG-Based Mental Task Classification: Linear and Nonlinear Classification of Movement Imagery

2005

Conference Paper

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Abstract—Use of EEG signals as a channel of communication between men and machines represents one of the current challenges in signal theory research. The principal element of such a communication system, known as a “Brain-Computer Interface,” is the interpretation of the EEG signals related to the characteristic parameters of brain electrical activity. Our goal in this work was extracting quantitative changes in the EEG due to movement imagination. Subject‘s EEG was recorded while he performed left or right hand movement imagination. Different feature sets extracted from EEG were used as inputs into linear, Neural Network and HMM classifiers for the purpose of imagery movement mental task classification. The results indicate that applying linear classifier to 5 frequency features of asymmetry signal produced from channel C3 and C4 can provide a very high classification accuracy percentage as a simple classifier with small number of features comparing to other feature sets.

Author(s): Athena Akrami, AMN.
Book Title: EMBS
Journal: Engineering in Medicine and Biology Society, Annual International Conference of the IEEE
Year: 2005
Month: September
Day: 0

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), September 1-4, 2005, Shanghai, China (Accepted)

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
State: Accepted

BibTex

@inproceedings{3519,
  title = {EEG-Based Mental Task Classification: Linear and Nonlinear
  Classification of Movement Imagery},
  author = {Athena Akrami, AMN.},
  journal = {Engineering in Medicine and Biology Society, Annual International Conference of the IEEE},
  booktitle = {EMBS},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = sep,
  year = {2005},
  month_numeric = {9}
}