Multi Channel Brain FEG Signals Based Emotional Arousal Classification with Unsupervised Feature Learning using Autoencoders

被引：0

作者：

Ayata, Deger ^{[1
]}

Yaslan, Yusuf ^{[1
]}

Kamasak, Mustafa ^{[1
]}

机构：

[1] Istanbul Tech Univ, Comp Engn Dept, Fac Comp & Informat, Istanbul, Turkey

来源：

2017 25TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2017年

关键词：

Brain EEG Analysis; Arousal Recognition; Multi-channel sensor processing; Signal Processing; kNN; Random Forests; Autoencoders; Feature Learning; Brain Computer Interface;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

The importance of learning important features in an automatic manner is growing exponentially, as the volume of data and number of systems using pattern recognition techniques continue to increase. In this paper, arousal recognition from multi channels EEC signals was conducted using human crafted statistical features and learned features from 32 different EEG source channels. We have obtained 98.99% accuracy rate with unsupervised feature learning approach for Arousal classification. Unsupervised feature learning worked better compared to handcrafted feature approach.

引用

页数：4

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