Yatabe-Guilford personality inventory
Kretschmer type personality inventory
k - nearest neighbour method
In this paper we introduce a method to classify matching patterns between music and human mood using an electroencephalogram (EEG) analysis technique and considering personality. We analyse the EEG of the left prefrontal cortex by single-point sensing. The EEG recording device uses dry-type sensors. The feature vector is created by connecting the personality quantification results and the EEG features. Egograms—the Yatabe-Guilford personality inventory and a Kretschmer-type personality inventory are used to quantify personality. The EEG features are extracted using fast Fourier transform. Then, the matching patterns are classified using the k -nearest neighbour method. To show the effectiveness of the proposed method, we conduct experiments using real EEG data.
The Online Journal on Computer Science and Information Technology
International Foundation for Modern Education and Scientific Research
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