Brain Activity Recognition with a Wearable fNIRS Using Neural Networks

被引：0

作者：

Huve, Gauvain ^{[1
]}

Takahashi, Kazuhiko ^{[1
]}

Hashimoto, Masafumi ^{[1
]}

机构：

[1] Doshisha Univ, Kyoto, Japan

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (ICMA) | 2017年

关键词：

Brain Computer Interface; fNIRS; Deep Neural Network; Convolutional Neural Network;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Functional Near-Infrared Spectroscopy (fNIRS) has taken the focus in the domain of Brain Computer Interfaces (BCI) in the recent years. However, there is not yet a defined standard for the treatment of data obtained through fNIRS. This study aims at evaluating the performance of deep neural networks and convolutional neural networks in the classification of different stimulus in the prefrontal cortex, caused by predefined activities: subtractions, word generation, and rest. After optimizing both types of networks, experimental results suggest deep neural networks to be more precise, but convolutional neural networks to be faster to train. It also shows that neither type of network is able to distinguish subtraction from word generation.

引用

页码：1573 / 1578

页数：6

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