A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector  
[PERSON]  
1 Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan 1*  
emotion recognition [16; 17], and steady-state visually evoked potential (SSVEP)-based BCIs [18; 19]. In this paper, EEG is adopted as the input of the developed BCI. SSVEP and P300 are visual evoked potential and event related potential (ERP) in EEG, respectively [20; 21]. SSVEP is a response to visual stimuli at specific flicker frequencies, where the response is the increase of EEG spectral power at the same frequency or near the flickering frequency [22]. SSVEP has a good signal-to-noise ratio (SNR) [23]. To induce SSVEP, a subject needs to keep gazing at a device which generates continuous visual stimuli at a specific flickering frequency, such as a flashing LED. However, a long-term continuous flickering stimulus may result in visual fatigue. Therefore, SSVEP is useful in designing a brain-controlled switch [18]. In contrast with SSVEP, P300 is relatively suitable for the design of a function BCI with multiple options. P300 is a response to an anticipated but infrequent stimulus, and typically appears in EEG signals around 300 ms after the stimulus onset. In P300 BCIs, a visual stimulus matrix is often designed and utilized to elicit P300 potentials in EEG signals, and the visual stimuli provided by this matrix follow the so-called oddball paradigm: rows and the columns of the matrix are intensified in a random order, and one at a time. Examples include the P300 word speller BCI [24], where a 6 \(\times\) 6 visual stimulus matrix is designed to provide users with 36 different options (characters and numbers), and the BCI system for upper-limb robotic rehabilitation [25]. Therefore, P300 BCIs can provide users with more options than SSVEP ones. However, EEG typically exhibits a very low SNR [26]. Therefore, P300-based BCIs rely on multiple repetitions of a stimulation sequence in order to increase the SNR of the P300 ERP [20], which results in a long EEG recording procedure. In this paper, we develop a healthcare BCI system by integrating the advantage of each of the two visually evoked EEG potentials. The system is composed of two modes, one is the switching mode and the other is the function selection mode. The switching mode allows a user to turn on the function selection mode whenever he/she wants by detecting the SSVEP in ongoing EEG. In the function selection mode, we design a healthcare function selection panel consisting of a 5 \(\times\) 5 visual stimulus matrix, and the 25 elements of the matrix represent 25 different daily activities that are frequently performed. The function selection mode allows the user to choose the element that he/she wants (called target element) by detecting P300 in ongoing EEG signals. The function represented by the target element is activated as soon as the target element is detected by the BCI system. Users can turn on the system and control multiple devices in a self-paced manner by means of the proposed healthcare BCI system, achieving the objective of autonomous healthcare. In addition to the development of the BCI system, in this paper we also address one challenging issue related to the accuracy of the target element detection in the element selection mode. The accuracy of target element detection accuracy is directly related to the one of P300 detection. Previously, P300 detection was treated as a binary classification problem of P300 and non-P300, where P300 refers to an EEG epoch containing a P300, and non-P300 refers to an EEG epoch that does not carry a P300 potential. Accordingly, any binary classifiers can serve as the P300 detector for which the input is an EEG epoch. However, it is difficult to detect P300 in single-trial EEG due to low SNR. The input to a P300 BCI is, in general, the average EEG over a number of trials, resulting in a time-consuming EEG data collection stage.