**Mental Task Recognition by EEG Signals: A Novel Approach with ROC Analysis Approach with ROC Analysis**

**Mental Task Recognition by EEG Signals: A Novel** 

DOI: 10.5772/intechopen.71743

Takashi Kuremoto, Masanao Obayashi, Shingo Mabu and Kunikazu Kobayashi and Kunikazu Kobayashi Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

Takashi Kuremoto, Masanao Obayashi, Shingo Mabu

http://dx.doi.org/10.5772/intechopen.71743

#### **Abstract**

Electroencephalogram or electroencephalography (EEG) has been widely used in medical fields and recently in cognitive science and brain-computer interface (BCI) research. To distinguish metal tasks such as reading, calculation, motor imagery, etc., it is generally to extract features of EEG signals by dimensionality reduction methods such as principle component analysis (PCA), linear determinant analysis (LDA), common spatial pattern (CSP), and so on for classifiers, for example, k-nearest neighbor method (kNN), kernel support vector machine (SVM), and artificial neural networks (ANN). In this chapter, a novel approach of feature extraction of EEG signals with receiver operating characteristic (ROC) analysis is introduced.

**Keywords:** brain-computer interface (BCI), electroencephalogram or electroencephalography (EEG), artificial neural networks (ANN), support vector machine (SVM), receiver operating characteristic (ROC), Fourier transformation (FT)
