A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier.
High rate classification of imagery tasks is still one of the hot topics among the brain computer interface (BCI) groups. In order to improve this rate, a new approach based on fractal dimension as feature and Adaboost as classifier is presented for five subjects in this paper. To have a comparison, features such as band power, Hjorth parameters along with LDA classifier have been taken into account. Fractal dimension as a feature with Adaboost and LDA can be considered as alternative combinations for BCI applications.
Boostani
Reza
R
Amir Kabir University of Technology, Faculty of Biomedical Engineering, Tehran, Iran.
Moradi
Mohammad Hassan
MH
D000465:Algorithms; D001185:Artificial Intelligence; D003143:Communication Devices for People with Disabilities; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D017709:Fractals; D006801:Humans; D007092:Imagination / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface