A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector

This paper presents a novel brain-computer interface (BCI)-based healthcare control system, which is based on steady-state visually evoked potential (SSVEP) and P300 of electroencephalography (EEG) signals. The proposed system is composed of two modes, a brain switching mode and a healthcare functio...

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Main Authors: Yi-Hung Liu, Shih-Hao Wang, Ming-Ren Hu
Format: Article
Language:English
Published: MDPI AG 2016-05-01
Series:Applied Sciences
Subjects:
EEG
Online Access:http://www.mdpi.com/2076-3417/6/5/142
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spelling doaj-cc1d8a6519b441a68d37743cfce428d22020-11-24T21:35:38ZengMDPI AGApplied Sciences2076-34172016-05-016514210.3390/app6050142app6050142A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based DetectorYi-Hung Liu0Shih-Hao Wang1Ming-Ren Hu2Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei 10608, TaiwanDepartment of Mechanical Engineering, Chung Yuan Christian University, Chungli 32023, TaiwanDepartment of Mechanical Engineering, Chung Yuan Christian University, Chungli 32023, TaiwanThis paper presents a novel brain-computer interface (BCI)-based healthcare control system, which is based on steady-state visually evoked potential (SSVEP) and P300 of electroencephalography (EEG) signals. The proposed system is composed of two modes, a brain switching mode and a healthcare function selection mode. The switching mode can detect whether a user has the intent to activate the function selection mode by detecting SSVEP in an ongoing EEG. During the function selection mode, the user is able to select any functions that he/she wants to activate through a healthcare control panel, and the function selection is done by detecting P300 in the user’s EEG signals. The panel provides 25 functions representing 25 frequently performed activities of daily life. Therefore, users with severe motor disabilities can activate the system and any functions in a self-paced manner, achieving the goal of autonomous healthcare. To achieve high P300 detection accuracy, a novel P300 detector based on kernel Fisher’s discriminant analysis (kernel FDA) and support vector machine (SVM) is also proposed. Experimental results, carried out on five subjects, show that the proposed BCI system achieves high SSVEP detection (93%) and high P300 detection (95.5%) accuracies, meaning that the switching mode has a high sensitivity, and the function selection mode has the ability to accurately detect the functions that the users want to trigger. More important, only three electrodes (Oz, Cz, and Pz) are required to measure EEG signals, enabling the system to have good usability in practical use.http://www.mdpi.com/2076-3417/6/5/142brain-computer interfaceEEGhealthcareP300steady-state visually evoked potentialsupport vector machinekernel Fisher’s discriminant analysis
collection DOAJ
language English
format Article
sources DOAJ
author Yi-Hung Liu
Shih-Hao Wang
Ming-Ren Hu
spellingShingle Yi-Hung Liu
Shih-Hao Wang
Ming-Ren Hu
A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector
Applied Sciences
brain-computer interface
EEG
healthcare
P300
steady-state visually evoked potential
support vector machine
kernel Fisher’s discriminant analysis
author_facet Yi-Hung Liu
Shih-Hao Wang
Ming-Ren Hu
author_sort Yi-Hung Liu
title A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector
title_short A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector
title_full A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector
title_fullStr A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector
title_full_unstemmed A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector
title_sort self-paced p300 healthcare brain-computer interface system with ssvep-based switching control and kernel fda + svm-based detector
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2016-05-01
description This paper presents a novel brain-computer interface (BCI)-based healthcare control system, which is based on steady-state visually evoked potential (SSVEP) and P300 of electroencephalography (EEG) signals. The proposed system is composed of two modes, a brain switching mode and a healthcare function selection mode. The switching mode can detect whether a user has the intent to activate the function selection mode by detecting SSVEP in an ongoing EEG. During the function selection mode, the user is able to select any functions that he/she wants to activate through a healthcare control panel, and the function selection is done by detecting P300 in the user’s EEG signals. The panel provides 25 functions representing 25 frequently performed activities of daily life. Therefore, users with severe motor disabilities can activate the system and any functions in a self-paced manner, achieving the goal of autonomous healthcare. To achieve high P300 detection accuracy, a novel P300 detector based on kernel Fisher’s discriminant analysis (kernel FDA) and support vector machine (SVM) is also proposed. Experimental results, carried out on five subjects, show that the proposed BCI system achieves high SSVEP detection (93%) and high P300 detection (95.5%) accuracies, meaning that the switching mode has a high sensitivity, and the function selection mode has the ability to accurately detect the functions that the users want to trigger. More important, only three electrodes (Oz, Cz, and Pz) are required to measure EEG signals, enabling the system to have good usability in practical use.
topic brain-computer interface
EEG
healthcare
P300
steady-state visually evoked potential
support vector machine
kernel Fisher’s discriminant analysis
url http://www.mdpi.com/2076-3417/6/5/142
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