Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification

Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt's estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively

Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification

Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt’s estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively

Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification

Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain-computer interface (BCI) systems. Recent research has shown that nonlinear classification algorithms perform better than their linear counterparts, but most of them cannot extract sufficient significant information which leads to a less efficient classification. In this paper, we propose a novel approach called FDDL-ELM, which combines the discriminative power of extreme learning machine (ELM) with the reconstruction capability of sparse representation. Firstly, the common spatial pattern (CSP) algorithm is adopted to perform spatial filtering on raw EEG data to enhance the task-related neural activity. Secondly, the Fisher discrimination criterion is employed to learn a structured dictionary and obtain sparse coding coefficients from the filtered data, and these discriminative coefficients are then used to acquire the reconstructed feature representations. Finally, a nonlinear classifier ELM is used to identify these features in different MI tasks. The proposed method is evaluated on 2-class Datasets IVa and IIIa of BCI Competition III and 4-class Dataset IIa of BCI Competition IV. Experimental results show that our method achieved superior performance than the other existing algorithms and yielded the accuracies of 80.68%, 87.54%, and 63.76% across all subjects in the above-mentioned three datasets, respectively

Quantized H-infinity filtering for different communication channels

In this paper, we consider the H∞ filtering problem for linear systems using quantized measurements. The communication channel we consider consists of two cases: the ideal one and the unreliable one. For the ideal channel, we designed a filter to mitigate the quantization effects, which ensured not only the asymptotical stability but also a prescribed H∞ filtering performance. For the unreliable channel, we introduced the stochastic variable satisfying Bernoulli random binary distribution to model the lossy measurements. We also designed a filter to cope with the losses and mitigate quantization effects simultaneously which ensured not only stochastic stability, but also a prescribed H∞ filtering performance. Furthermore, we derive sufficient conditions for the existence of the above filters. Finally, a numerical example is given to illustrate that the proposed approach is effective and feasible

Efficacy and Safety Outcomes of Intravitreal Dexamethasone Implant Therapy for the Treatment of Adult Coats’ Disease

Purpose. To evaluate the efficacy and safety outcomes of dexamethasone intravitreal implant in patients with Stage 3A Coats’ disease. Methods. A consecutive case series of adult Coats’ disease managed with or without intravitreal dexamethasone implant (Ozurdex®, Allergan Inc., Irvine, California, USA) injection was retrospectively evaluated. The medical records of all included patients with a minimum follow-up of 6 months were reviewed. The patients were divided into two groups according to the application of dexamethasone implant as a DEX (+) group and DEX (−) group. Laser photocoagulation, anti-VEGF agents, and vitrectomy were performed if necessary. The primary outcomes included best-corrected visual acuity (BCVA), central retinal thickness (CRT), and intraocular pressure (IOP) at month 6. Resolution of the exudative retinal detachment (ERD), subretinal fluid (SRF), and vitreous hemorrhage (VH) was also collected. Results. Ten eyes (10 patients) with Stage 3A Coats’ disease were included, and the mean follow-up time was 9.70 ± 4.42 months. The mean age was 44.20 ± 7.42 years, and 80% were male. Six eyes (6 patients) received intravitreal injection of Ozurdex were included in the DEX (+) group, while the other 4 eyes in the DEX (−) group. No significant difference of baseline characteristics including BCVA, CRT, IOP, and follow-up time can be defined between DEX (+) and DEX (−) groups. For the patients in the DEX (+) group, a significant improvement of BCVA was observed from the baseline of 1.28 ± 0.58 to 0.84 ± 0.66 logMAR at month 6 (P=0.03), while the CRT decreased from 970.33 ± 696.49 to 421.00 ± 275.76 μm (P=0.067). For the DEX(−) group, BCVA changed from 0.76 ± 0.74 to 0.96 ± 0.60 logMAR at month 6 (P=0.066), while the CRT from 382.75 ± 17.68 to 412.75 ± 195.53 μm (P=0.525) with no significant difference. IOP was elevated from 13.15 ± 1.74 mmHg at baseline to 18.05 ± 3.57 mmHg at month 6 with a P value of 0.02 for the DEX(+) group and from 14.48 ± 1.70 to 18.83 ± 4.06 mmHg (P=0.076) for the DEX (−) group. After a mean follow-up of 9.70 months, 5/6 (83.3%) eyes in the DEX (+) group and ¼ (25%) eye in the DEX (−) group achieved reattachment of ERD. Conclusion. Intravitreal dexamethasone implant therapy is effective for adult Stage 3A Coats’ disease, which provides a new treatment option for ophthalmologists

Double-Criteria Active Learning for Multiclass Brain-Computer Interfaces

Recent technological advances have enabled researchers to collect large amounts of electroencephalography (EEG) signals in labeled and unlabeled datasets. It is expensive and time consuming to collect labeled EEG data for use in brain-computer interface (BCI) systems, however. In this paper, a novel active learning method is proposed to minimize the amount of labeled, subject-specific EEG data required for effective classifier training, by combining measures of uncertainty and representativeness within an extreme learning machine (ELM). Following this approach, an ELM classifier was first used to select a relatively large batch of unlabeled examples, whose uncertainty was measured through the best-versus-second-best (BvSB) strategy. The diversity of each sample was then measured between the limited labeled training data and previously selected unlabeled samples, and similarity is measured among the previously selected samples. Finally, a tradeoff parameter is introduced to control the balance between informative and representative samples, and these samples are then used to construct a powerful ELM classifier. Extensive experiments were conducted using benchmark and multiclass motor imagery EEG datasets to evaluate the efficacy of the proposed method. Experimental results show that the performance of the new algorithm exceeds or matches those of several state-of-the-art active learning algorithms. It is thereby shown that the proposed method improves classifier performance and reduces the need for training samples in BCI applications

Double boron–oxygen-fused polycyclic aromatic hydrocarbons: skeletal editing and applications as organic optoelectronic materials

Abstract An efficient one-pot strategy for the facile synthesis of double boron–oxygen-fused polycyclic aromatic hydrocarbons (dBO-PAHs) with high regioselectivity and efficient skeletal editing is developed. The boron–oxygen-fused rings exhibit low aromaticity, endowing the polycyclic aromatic hydrocarbons with high chemical and thermal stabilities. The incorporation of the boron–oxygen units enables the polycyclic aromatic hydrocarbons to show single-component, low-temperature ultralong afterglow of up to 20 s. Moreover, the boron–oxygen-fused polycyclic aromatic hydrocarbons can also serve as ideal n-type host materials for high-brightness and high-efficiency deep-blue OLEDs; compared to single host, devices using boron–oxygen-fused polycyclic aromatic hydrocarbons-based co-hosts exhibit dramatically brightness and efficiency enhancements with significantly reduced efficiency roll-offs; device 9 demonstrates a high color-purity (Commission International de l’Eclairage CIEy = 0.104), and also achieves a record-high external quantum efficiency (28.0%) among Pt(II)-based deep-blue OLEDs with Commission International de l’Eclairage CIEy < 0.20; device 10 achieves a maximum brightnessof 27219 cd/m2 with a peak external quantum efficiency of 27.8%, which representes the record-high maximum brightness among Pt(II)-based deep-blue OLEDs. This work demonstrates the great potential of the double boron–oxygen-fused polycyclic aromatic hydrocarbons as ultralong afterglow and n-type host materials in optoelectronic applications