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Appl. Sci. 2016, 6(5), 142; doi:10.3390/app6050142

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

1
Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
2
Department of Mechanical Engineering, Chung Yuan Christian University, Chungli 32023, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Chien-Hung Liu
Received: 31 December 2015 / Revised: 19 April 2016 / Accepted: 28 April 2016 / Published: 12 May 2016
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Abstract

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. View Full-Text
Keywords: brain-computer interface; EEG; healthcare; P300; steady-state visually evoked potential; support vector machine; kernel Fisher’s discriminant analysis brain-computer interface; EEG; healthcare; P300; steady-state visually evoked potential; support vector machine; kernel Fisher’s discriminant analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Liu, Y.-H.; Wang, S.-H.; Hu, M.-R. A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector. Appl. Sci. 2016, 6, 142.

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