Detection of Highly Motivated Time Segments in Brain Computer Interface Signals

ABSTRACT

Motivation of a subject, who is associated with the data acquisition of brain computer interface (BCI) experiment, is a very crucial parameter for executing a successful BCI application. This paper proposes a novel method to present the distribution of motivation of a subject during a BCI experiment. The proposed method was successfully applied to the BCI Competition 2003 Data Set III and the BCI Competition 2005 Data Set I using fast Fourier transform-based band power features with a linear discriminant analysis classifier. The results show that not only the motivation of the subject dramatically changes during the trial but also using highly motivated time segments provides 7.86% and 2.00% improvement in the classification accuracy of the BCI Competition 2003 Data Set III and the BCI Competition 2005 Data Set I, respectively.

KEYWORDS:

• Brain computer interface
• Classification accuracy
• Distribution of motivation
• EEG

Additional information

Notes on contributors

Onder Aydemir

Onder Aydemir was born in Trabzon, Turkey. He received his BSc, MSc and PhD degrees from Karadeniz Technical University (KTU) in 2005, 2008 and 2013, respectively. He studied at the Medical University of Vienna for his post-doctorate research in 2014–2015. He is an Assistant Professor at the Department of Electrical and Electronics Engineering, KTU. His research interests include biomedical signal and image processing, pattern recognition, computational neuroscience and telecommunication applications.