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The reaction of (Z)-5-phenyl-1,3-di-p-tolylpent-2-en-4-yn-1-ol (1) with trimethylsilyl chloride in dichloromethane at ambient temperature gave a dimeric ether compound 2 in 30% yield. Subsequently, heating 2 in toluene under refluxing temperature rendered the title compound quantitatively. The structure of this tricyclic-fused compound was characterized using NMR, mass spectroscopy, and X-ray crystallography. This unique linear tricyclic fused furan framework is reported for the first time.

Abstract Shikoku island, southwestern Japan lies in the western Nankai Trough and showcases along-strike segmentation of slow earthquake behavior. Whether the spatial variation of tremor behavior reflects the regional differences in structure/source properties and how much such differences can be recognized by the seismic signals themselves are two questions addressed in this paper. Taking advantage of advanced methods in recognizing and classifying signals using machine learning approaches, we attempt to answer them by conducting signal classification experiments in Shikoku. Based on the tremor catalog from 1 June 2014 to 31 March 2015, the tremors recorded in four different areas were treated as different classes and segmented into 60-s-long signals. The number of tremors in four different areas (A to D, from west to east) reached 15,000, 31,000, 10,000, and 16,000, respectively. To efficiently distinguish between tremors from different areas, we applied a k-nearest neighbor (k-NN) classifier with Fisher’s class separability criteria to select the optimal feature subset. The resulting classification performance reached more than 90% at all 12 stations. We further designed a triangle test to select the features that can better represent the differences in source properties between areas. We found that the most efficient features were associated with (1) the number of peaks in the temporal evolution of discrete Fourier transforms and (2) the energy distribution in the autocorrelation function (ACF). To match the difference in behavior revealed by the ACF, the size of the tremor zone, which mainly controls how long the seismic energy lasts in a tremor episode, was determined to be largest in Area B and smallest in Area C. The heterogeneity of the asperities in a tremor zone, which may control how spiky the tremor signals developed over time, was determined to be strong in Areas B and C. Together with previously documented variations in slow earthquake behavior in the same area, we finally propose a conceptual model that provides a better understanding of the regional differences in the tremor source properties in Shikoku, Japan. Graphical Abstract

A series of 3-hydroxy-fluorene-2-carboxylate compounds were prepared from Michael addition of acetoacetate to 2-benzylideneindan-l-one followed by Robinson annulation and aromatization. In this reaction, we were able to isolate two Robinson annulation products and characterize them. This sequential reaction could proceed without the isolation of intermediates to give the desired products directly in reasonable yields.

The stabilization of low-coordinate boron radicals is challenging, as they tend to form strong chemical bonds. Here the authors report the isolation and structural characterization of a dicoordinate boron radical featuring a π-donating amino and a π-accepting carbene ligand.

Background/Purpose: The number of psychiatrists working in community clinics in Taiwan has increased dramatically in the recent decade. This study aimed to investigate the trend of prevalence and incidence of depressive disorders and assess the quality of depression care between 2007 and 2016 in Taiwan. Methods: We used the claims database derived from Taiwan's National Health Insurance (NHI) program, in which approximately 23.0 million individuals were enrolled, translating to a coverage rate of 99%. Patients with depressive disorders were identified based on International Classification of Diseases codes. The process indicators of depression care quality included visit, duration, and dose adequacy. The outcome indicators included the rate of psychiatric hospitalisation, emergency visit, self-harm hospitalisation, and suicide. Results: The prevalence of treated depressive disorders increased from 1.61% in 2007 to 1.92% in 2016, i.e., a 25% increase, whereas the incidence of first-ever or recurrent depressive disorder did not change significantly. The number of patients treated by psychiatrists and in community clinics also increased. The quality of depression care improved, the proportion of patients receiving minimum psychiatric clinic follow-up and adequate medication increased, and the rate of emergency visits, psychiatric hospitalisation, and self-harm hospitalisation declined. Conclusion: The community-based psychiatric services increased and the quality indicators of depression care in Taiwan improved during 2007–2016. The causality warrants further investigations.

The chemical screening of a cultured soft coral, Briareum violaceum, led to the isolation of eight natural, briarane-related diterpenoids, including three unreported metabolites, briavioids E–G (1–3), and five known briaranes, briacavatolides B (4) and C (5), briaexcavatin L (6), briaexcavatolide U (7) and briarenol K (8). The structures of briaranes 1–8 were established using spectroscopic methods. The absolute configuration of briavioid A (9), obtained in a previous study, was reported for the first time in this study by a single-crystal X-ray diffraction analysis using a copper radiation source. The anti-inflammatory activity of briaranes 1 and 2 and briaranes 4–8 was evaluated by screening their inhibitory ability against the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) proteins in lipopolysaccharide (LPS)-induced RAW 264.7 macrophage cells.

Mobile and IoT devices are blooming, and their applications are prevailing worldwide. In the meantime, the Industry 4.0 trend converges industrial control systems with the internet environment, which makes it vulnerable. Mobile and IoT applications provide seamless connectivity to the emergent network environment for daily work. As mobile phones contain privacy information, malicious mobile applications could compromise mobile devices and cause financial losses. Moreover, attackers could launch DDoS attacks to exhaust a resource of mobile or IoT devices in the emergent network environment. Static malware analysis consumes less computing resources and power than dynamic analysis. This study proposes a power-efficient solution to identify mobile threats, which applies taint analysis to protect the emergent network environment. The experimental results show that the proposed approach could detect mobile malware efficiently.

Individuals with mild cognitive impairment (MCI) are at high risk of developing into dementia (e. g., Alzheimer's disease, AD). A reliable and effective approach for early detection of MCI has become a critical challenge. Although compared with other costly or risky lab tests, electroencephalogram (EEG) seems to be an ideal alternative measure for early detection of MCI, searching for valid EEG features for classification between healthy controls (HCs) and individuals with MCI remains to be largely unexplored. Here, we design a novel feature extraction framework and propose that the spectral-power-based task-induced intra-subject variability extracted by this framework can be an encouraging candidate EEG feature for the early detection of MCI. In this framework, we extracted the task-induced intra-subject spectral power variability of resting-state EEGs (as measured by a between-run similarity) before and after participants performing cognitively exhausted working memory tasks as the candidate feature. The results from 74 participants (23 individuals with AD, 24 individuals with MCI, 27 HC) showed that the between-run similarity over the frontal and central scalp regions in the HC group is higher than that in the AD or MCI group. Furthermore, using a feature selection scheme and a support vector machine (SVM) classifier, the between-run similarity showed encouraging leave-one-participant-out cross-validation (LOPO-CV) classification performance for the classification between the MCI and HC (80.39%) groups and between the AD vs. HC groups (78%), and its classification performance is superior to other widely-used features such as spectral powers, coherence, and the complexity estimated by Katz's method extracted from single-run resting-state EEGs (a common approach in previous studies). The results based on LOPO-CV, therefore, suggest that the spectral-power-based task-induced intra-subject EEG variability extracted by the proposed feature extraction framework has the potential to serve as a neurophysiological feature for the early detection of MCI in individuals.

Most people with motor disabilities use a joystick to control an electric wheelchair. However, those who suffer from multiple sclerosis or amyotrophic lateral sclerosis may require other methods to control an electric wheelchair. This study implements an electroencephalography (EEG)-based brain–computer interface (BCI) system and a steady-state visual evoked potential (SSVEP) to manipulate an electric wheelchair. While operating the human–machine interface, three types of SSVEP scenarios involving a real-time virtual stimulus are displayed on a monitor or mixed reality (MR) goggles to produce the EEG signals. Canonical correlation analysis (CCA) is used to classify the EEG signals into the corresponding class of command and the information transfer rate (ITR) is used to determine the effect. The experimental results show that the proposed SSVEP stimulus generates the EEG signals because of the high classification accuracy of CCA. This is used to control an electric wheelchair along a specific path. Simultaneous localization and mapping (SLAM) is the mapping method that is available in the robotic operating software (ROS) platform that is used for the wheelchair system for this study.

Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi’s fractal dimension, and Katz’s fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.

This study aims to develop and validate the use of machine learning-based prediction models to select individualized pharmacological treatment for patients with depressive disorder. This study used data from Taiwan’s National Health Insurance Research Database. Patients with incident depressive disorders were included in this study. The study outcome was treatment failure, which was defined as psychiatric hospitalization, self-harm hospitalization, emergency visits, or treatment change. Prediction models based on the Super Learner ensemble were trained separately for the initial and the next-step treatments if the previous treatments failed. An individualized treatment strategy was developed for selecting the drug with the lowest probability of treatment failure for each patient as the model-selected regimen. We emulated clinical trials to estimate the effectiveness of individualized treatments. The area under the curve of the prediction model using Super Learner was 0.627 and 0.751 for the initial treatment and the next-step treatment, respectively. Model-selected regimens were associated with reduced treatment failure rates, with a 0.84-fold (95% confidence interval (CI) 0.82–0.86) decrease for the initial treatment and a 0.82-fold (95% CI 0.80–0.83) decrease for the next-step. In emulation of clinical trials, the model-selected regimen was associated with a reduced treatment failure rate.

Classification between individuals with mild cognitive impairment (MCI) and healthy controls (HC) based on electroencephalography (EEG) has been considered a challenging task to be addressed for the purpose of its early detection. In this study, we proposed a novel EEG feature, the kernel eigen-relative-power (KERP) feature, for achieving high classification accuracy of MCI versus HC. First, we introduced the relative powers (RPs) between pairs of electrodes across 21 different subbands of 2-Hz width as the features, which have not yet been used in previous MCI-HC classification studies. Next, the Fisher’s class separability criterion was applied to determine the best electrode pairs (five electrodes) as well as the frequency subbands for extracting the most sensitive RP features. The kernel principal component analysis (kernel PCA) algorithm was further performed to extract a few more discriminating nonlinear principal components from the optimal RPs, and these components form a KERP feature vector. Results carried out on 51 participants (24 MCI and 27 HC) show that the newly introduced subband RP feature showed superior classification performance to commonly used spectral power features, including the band power, single-electrode relative power, and also the RP based on the conventional frequency bands. A high leave-one-participant-out cross-validation (LOPO-CV) classification accuracy 86.27% was achieved by the RP feature, using a simple linear discriminant analysis (LDA) classifier. Moreover, with the same classifier, the proposed KERP further improved the accuracy to 88.24%. Finally, cascading the KERP feature to a nonlinear classifier, the support vector machine (SVM), yields a high MCI-HC classification accuracy of 90.20% (sensitivity = 87.50% and specificity = 92.59%). The proposed method demonstrated a high accuracy and a high usability (only five electrodes are required), and therefore, has great potential to further develop an EEG-based computer-aided diagnosis system that can be applied for the early detection of MCI.

This paper focuses on the development of a P300 speller and the design of a rehabilitation robot using a brain-machine interface. The combined feature set provides a norm that can be used to assess trends of the user’s increased or decreased independence. The combined feature set is found to maintain a 90% sorting rate; it can also reduce the relationship of individual independence for each subject. Among the results, the highest P300 classification accuracy can be increased by 36.04%. A novel adaptive coupled elastic actuator (ACEA) is proposed that uses adjustable characteristics to adapt to the applied output and input forces, thus ensuring safe human-machine interaction without the use of complex control strategies. The proposed robotic system uses variable impedance to achieve adaptability and safety in dynamic unstructured environments. This paper discusses the design, model, control, and performance of the ACEA.

In this article, a neural network corrector is proposed to correct the image shift, yielding the degradation of three-dimensional image reconstruction, for each slice captured by cone-beam computed tomography simulator. There are 3 degrees of freedom in tube module of simulator; the central point of tube module should be aligned with the central point of detector module to guarantee the accurate image projection. However, the mechanism manufacturing and assembling tolerance will let the above aim cannot be met. Here, a standard kit is made to measure the image shift by 1° step from −10° to 10°. The measure data will be the input training data of proposed neural network corrector, and the corrected translation position will be the output of neural network corrector. The Levenberg–Marquardt learning algorithm adjusts the connected weights and biases of the neural network using a supervised gradient descent method, such that the defined error function can be minimized. To avoid the problem of overfitting and improve the generalized ability of the neural network, Bayesian regularization is added to the Levenberg–Marquardt learning algorithm. After the training of neural network corrector, the different target position commands are fed into the neural network corrector. Then, the corrected data from neural network corrector are fed to be the new position command to verify the image correction performance. Moreover, a phantom kit is made to check the corrected performance of the neural network corrector. Finally, the experimental results verify that the image shift can be reduced by the neural network corrector.

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher’s discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher’s emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods.

Patients suffering from severe motor disabilities usually require assistance from other people when doing rehabilitation exercises, which causes the rehabilitation process to be time-consuming and inconvenient. Therefore, we propose an automatic feature extraction method for a brain-machine interface that allows patients to control a robot using their own brain waves. A brain–machine interface (BMI) based on the P300 event-related potential (ERP), called Brain Controlled Rehabilitation System (BCRS), was developed to detect the intentions of patients. Using the BCRS, patients can communicate with the robot through their brain waves. However, deciding how to obtain an automatically extracted, useful EEG signal is a difficult and important problem for BMI research. In this paper, Independent Component Analysis – Multiple Kernel Learning (ICA-MKL) is used to directly extract a useful signal and build the classification mode for BCRS. The results reveal that this method is useful for automatically extracting the P300 signal and the accuracy is better than MKL. In additional, the same method can be extended into any motor imaginary area and the accuracy of ICA-MKL for brain imaginary data is also good to removing eye-blink artifacts and the accuracy performance is also good.

Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD) manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD) has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD) to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms.

Electroencephalography (EEG) can assist with the detection of major depressive disorder (MDD). However, the ability to distinguish adults with MDD from healthy individuals using resting-state EEG features has reached a bottleneck. To address this limitation, we collected EEG data as participants engaged with positive pictures from the International Affective Picture System. Because MDD is associated with blunted positive emotions, we reasoned that this approach would yield highly dissimilar EEG features in healthy versus depressed adults. We extracted three types of relative EEG power features from different frequency bands (delta, theta, alpha, beta, and gamma) during the emotion task and resting state. We also applied a novel classifier, called a conformal kernel support vector machine (CK-SVM), to try to improve the generalization performance of conventional SVMs. We then compared CK-SVM performance with three machine learning classifiers: linear discriminant analysis (LDA), conventional SVM, and quadratic discriminant analysis. The results from the initial analyses using the LDA classifier on 55 participants (24 MDD, 31 healthy controls) showed that the participant-independent classification accuracy obtained by leave-one-participant-out cross-validation (LOPO-CV) was higher for the EEG recorded during the positive emotion induction versus the resting state for all types of relative EEG power. Furthermore, the CK-SVM classifier achieved higher LOPO-CV accuracy than the other classifiers. The best accuracy (83.64%; sensitivity = 87.50%, specificity = 80.65%) was achieved by the CK-SVM, using seven relative power features extracted from seven electrodes. Overall, combining positive emotion induction with the CK-SVM classifier proved useful for detecting MDD on the basis of EEG signals. In the future, this approach might be used to develop a brain–computer interface system to assist with the detection of MDD in the clinic. Importantly, such a system could be implemented with a low-density electrode montage (seven electrodes), highlighting its practical utility.

Defect inspection plays a critical role in thin film transistor liquid crystal display (TFT-LCD) manufacture, and has received much attention in the field of automatic optical inspection (AOI). Previously, most focus was put on the problems of macro-scale Mura-defect detection in cell process, but it has recently been found that the defects which substantially influence the yield rate of LCD panels are actually those in the TFT array process, which is the first process in TFT-LCD manufacturing. Defect inspection in TFT array process is therefore considered a difficult task. This paper presents a novel inspection scheme based on kernel principal component analysis (KPCA) algorithm, which is a nonlinear version of the well-known PCA algorithm. The inspection scheme can not only detect the defects from the images captured from the surface of LCD panels, but also recognize the types of the detected defects automatically. Results, based on real images provided by a LCD manufacturer in Taiwan, indicate that the KPCA-based defect inspection scheme is able to achieve a defect detection rate of over 99% and a high defect classification rate of over 96% when the imbalanced support vector machine (ISVM) with 2-norm soft margin is employed as the classifier. More importantly, the inspection time is less than 1 s per input image.

Motor imagery is based on the volitional modulation of sensorimotor rhythms (SMRs); however, the sensorimotor processes in patients with amyotrophic lateral sclerosis (ALS) are impaired, leading to degenerated motor imagery ability. Thus, motor imagery classification in ALS patients has been considered challenging in the brain–computer interface (BCI) community. In this study, we address this critical issue by introducing the Grassberger–Procaccia and Higuchi’s methods to estimate the fractal dimensions (GPFD and HFD, respectively) of the electroencephalography (EEG) signals from ALS patients. Moreover, a Fisher’s criterion-based channel selection strategy is proposed to automatically determine the best patient-dependent channel configuration from 30 EEG recording sites. An EEG data collection paradigm is designed to collect the EEG signal of resting state and the imagination of three movements, including right hand grasping (RH), left hand grasping (LH), and left foot stepping (LF). Five late-stage ALS patients without receiving any SMR training participated in this study. Experimental results show that the proposed GPFD feature is not only superior to the previously-used SMR features (mu and beta band powers of EEG from sensorimotor cortex) but also better than HFD. The accuracies achieved by the SMR features are not satisfactory (all lower than 80%) in all binary classification tasks, including RH imagery vs. resting, LH imagery vs. resting, and LF imagery vs. resting. For the discrimination between RH imagery and resting, the average accuracies of GPFD in 30-channel (without channel selection) and top-five-channel configurations are 95.25% and 93.50%, respectively. When using only one channel (the best channel among the 30), a high accuracy of 91.00% can still be achieved by the GPFD feature and a linear discriminant analysis (LDA) classifier. The results also demonstrate that the proposed Fisher’s criterion-based channel selection is capable of removing a large amount of redundant and noisy EEG channels. The proposed GPFD feature extraction combined with the channel selection strategy can be used as the basis for further developing high-accuracy and high-usability motor imagery BCI systems from which the patients with ALS can really benefit.

Cationic methylpalladium complexes bearing hemilabile bidentate α-amino–pyridines can serve as effective precursors for catalytic alternating copolymerization of norbornene (N) and ethylene (E), under mild conditions. The norbornyl palladium complexes in the formula of {[RHNCH2(o-C6H4N)]Pd(C7H10Me)(NCMe)}(BF4) (R = iPr (2a), tBu (2b), Ph (2c), 2,6-Me2C6H3 (2d), 2,6-iPr2C6H3 (2e)) were synthesized via single insertion of norbornene into the corresponding methylpalladium complexes 1a–1e, respectively. Both square planar methyl and norbornyl palladium complexes exhibit facile equilibria of geometrical isomerization, via sterically-controlled amino decoordination–recoordination of amino–pyridine. Kinetic studies of E-insertion, N-insertion of complexes 1 and 2, and the geometric isomerization reactions have been examined by means of VT-NMR, and found in excellent agreement with the results estimated by DFT calculations. The more facile N-insertion in the cis-isomers, and ready geometric isomerization, cooperatively lead to a new mechanism that accounts for the novel catalytic formation of alternating COC.

Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP-CSP feature and the SVM classifier with only several trials, and this level of accuracy seems to become stable as more trials (i.e.,

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.