Interface System Research Articles

A high effectiveness impact‐optimized piezoelectric energy harvesting interface system

AbstractThis paper presents a novel high‐performance impact‐optimized interface system for an impact‐driven piezoelectric energy harvester (PEH) which utilizes two independent parallel harvesting plans, that is, low‐efficiency self‐powered passive path and high‐efficiency active maximum power point tracking (MPPT)‐based path, for different situations based on the characteristics of the input excitation and stored energy content which are evaluated by the mode detection unit. It uses a synchronous electrical charge extraction‐based self‐powered passive circuit as a primary energy extraction strategy. Thus, the proposed structure is self‐sustained with cold start capability. When the determined prerequisites are met, the system switches to the secondary energy extraction strategy, that is, high‐efficiency MPPT‐based path, in which during the maximum power point sensing phase, the PEH is sensed without disconnecting it from the interface and during the maximum power point setting phase, a bidirectional DC/DC converter performs a fully bidirectional energy transfer, increasing the extraction efficiency. The proposed system is designed and simulated using standard 180 nm complementary metal‐oxide semiconductor (CMOS) technology. Post‐layout simulation results show that when the input energy content is around 50 µJ, the FoMMOPIR, periodic harvesting efficiency, shock harvesting efficiency, MPPT efficiency, and effectiveness of the proposed system are 505%, 65%, 80%, 70%, and 56%, respectively.

Effect of depth ratio on frozen wave instability in immiscible liquids

In the present experimental work, a two-liquid system with comparable densities (density ratio = 0.543) undergoing lateral sinusoidal oscillation [along the length (L) of the container] has been considered. Under high forcing frequency condition (ω≫ν/L2), frozen waves appear at the interface of two-liquid system. Different depth ratios (hr) (or filling levels of liquid-1 and liquid-2) have been used to investigate the variations in the instability thresholds, wave amplitudes, wavelengths, and wave profiles of the frozen waves. Instability threshold of frozen waves shows unidirectional nature with varying depth ratios. A coefficient of relative velocity (c1) has been used to non-dimesionalize the variation of threshold amplitude (Aoc) and wave amplitude (Ar) with respect to depth ratios. Variation of normalized wave amplitude with modified Froude number shows existence of two regimes viz. gravity and surface tension dominated beyond which waves become three dimensional. A significant variation in wave profiles has been observed at high amplitude of forcing and reported in terms of radius of curvature. The three-dimensional frozen waves with temporally and spatially periodic doubling have been discussed.

Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks

Attention is one of many human cognitive functions that are essential in everyday life. Given our limited processing capacity, attention helps us focus only on what matters. Focusing attention on one speaker in an environment with many speakers is a critical ability of the human auditory system. This paper proposes a new end-to-end method based on the combined transformer and graph convolutional neural network (TraGCNN) that can effectively detect auditory attention from electroencephalograms (EEGs). This approach eliminates the need for manual feature extraction, which is often time-consuming and subjective. Here, the first EEG signals are converted to graphs. We then extract attention information from these graphs using spatial and temporal approaches. Finally, our models are trained with these data. Our model can detect auditory attention in both the spatial and temporal domains. Here, the EEG input is first processed by transformer layers to obtain a sequential representation of EEG based on attention onsets. Then, a family of graph convolutional layers is used to find the most active electrodes using the spatial position of electrodes. Finally, the corresponding EEG features of active electrodes are fed into the graph attention layers to detect auditory attention. The Fuglsang 2020 dataset is used in the experiments to train and test the proposed and baseline systems. The new TraGCNN approach, as compared with state-of-the-art attention classification methods from the literature, yields the highest performance in terms of accuracy (80.12%) as a classification metric. Additionally, the proposed model results in higher performance than our previously graph-based model for different lengths of EEG segments. The new TraGCNN approach is advantageous because attenuation detection is achieved from EEG signals of subjects without requiring speech stimuli, as is the case with conventional auditory attention detection methods. Furthermore, examining the proposed model for different lengths of EEG segments shows that the model is faster than our previous graph-based detection method in terms of computational complexity. The findings of this study have important implications for the understanding and assessment of auditory attention, which is crucial for many applications, such as brain–computer interface (BCI) systems, speech separation, and neuro-steered hearing aid development.

PENINGKATAN MUTU DAN EFISIENSI PELAYANAN KESEHATAN MELALUI IMPLEMENTASI REKAM MEDIS ELEKTRONIK DI RUMAH SAKIT HASTA HUSADA

Implementation of electronic medical records (RME) in hospitals has become an important step in improving the quality and efficiency of health services. Hasta Husada Hospital has adopted this system, but still faces various challenges. This study aims to analyze and evaluate improving the quality and efficiency of health services through the implementation of electronic medical records (RME) at Hasta Husada Hospital. This research uses a qualitative approach. The research results show that RME provides significant benefits, including fast and accurate access to patient data, increased work efficiency, and reduced medical errors. Although most users feel comfortable using RME after training, there are still complaints about the system interface needing to be simplified. User attitudes were generally positive towards RME after seeing real benefits, despite initial resistance. Implementation of the Hospital Management Information System (SIMRS) has shown mixed results, with some units having fully transitioned to RME and others still in the transition stage.

The application of integrating electroencephalograph-based emotion recognition technology into brain–computer interface systems for the treatment of depression: a narrative review

Dysregulation of the prefrontal cortex, amygdala, and hippocampus, along with alterations in P300 amplitude and abnormalities in the theta and beta bands, has been closely linked to the onset and pathophysiology of depression. Consequently, integrating electroencephalograph-based emotion recognition technology into brain‒computer interface systems offers the potential for real-time identification and modulation of emotional states through continuous interaction between the brain‒computer interface system and brain activity. This closed-loop system could precisely control neural stimulation in brain regions associated with emotional disorders, potentially alleviating the distressing memories of traumatic events. Although the efficacy of the brain‒computer interface in treating depression still requires validation through extensive clinical trials, its inherent real-time feedback and adaptive capabilities present a promising avenue for depression therapy. This review aims to explore the neuroanatomical mechanisms and neural activity patterns associated with depression and evaluate the potential of brain‒computer interface technology as a treatment modality. The objectives include summarizing key brain regions and neural networks involved in depression, analyzing their activity patterns, and assessing the impact of brain‒computer interface technology on these regions to provide theoretical support for future clinical trials. Significant functional abnormalities have been identified in the prefrontal cortex, amygdala, and hippocampus of patients with depression. The gray matter density, functional connectivity, and neural activity in these regions are closely associated with the severity of depressive symptoms. Common features in patients with depression include a reduced P300 amplitude and increased θ and α current density. Brain‒computer interface technology has demonstrated potential in modulating these abnormal neural activities, particularly in emotion recognition and regulation. When combined with techniques such as repetitive transcranial magnetic stimulation and deep brain stimulation, brain‒computer interface may provide effective interventions for managing emotional states in patients with depression. This review confirms the association between depression and functional abnormalities in specific brain regions and suggests that brain‒computer interface technology offers promising therapeutic potential by modulating abnormal neural activity. Brain‒computer interface could represent a novel treatment approach for depression. Future research should focus on validating the practical applications, efficacy, and safety of brain‒computer interface in treating depression.

Miniaturized, portable gustation interfaces for VR/AR/MR

Gustation is one of the five innate sensations for humans, distinguishing from vision, auditory, tactile, and olfaction, as which is a close and chemically induced sense. Despite the fact that a handful of gustation display technologies have been developed, the new technologies still pose significant challenges in miniaturization of the overall size for portability, enriching taste options within a limited working area, supporting natural human–device interaction, and achieving precisely controlled taste feedback. To address these issues, here, we report a set of intelligent and portable lollipop-shaped taste interfacing systems covering from 2 to 9 different taste options for establishing an adjustable taste platform in virtual reality (VR), augmented reality (AR), and mixed reality (MR) environments. Tasteful and food-grade chemicals embedded agarose hydrogels serve as taste sources based on iontophoresis operation principle, with an adjustable feedback intensity and independent operation time by tuning the voltage input. To achieve portability and user-friendly operation, the devices are miniaturized into a gustation interface with 9-channel taste generators in the dimension of 8 cm × 3 cm × 1 cm. To realize both gustation and olfaction feedbacks in Metaverse, an olfaction interface based on 7-channel odor generators is also introduced into the gustation interface system. As a result, the demonstrations of our gustation interface systems in intelligent medical gustation assessment, remote shopping, and mixed reality have proven their advances and great progress in various potential application areas, ranging from human–machine interfaces, to biomedical science, and to entertainment.

Classification of motor imagery EEG with ensemble RNCA model

Motor Imagery (MI) based brain-computer interface (BCI) systems are used for regaining the motor functions of neurophysiologically affected persons. But the performance of MI tasks is degraded due to the presence of redundant EEG channels. Hence, a novel ensemble regulated neighborhood component analysis (ERNCA) method provides a perfect identification of neural region that stimulate motor movements. Domains of statistical, frequency, spatial and transform-based features narrowed down the misclassification rate. The gradient boosting method selects the relevant features thereby reduces the computational complexity. Finally, Bayesian optimized ensemble classifier finetuned the classification accuracies of 97.22 % and 91.62 % for Datasets IIIa and IVa respectively. This approach is further strengthened by analyzing real-time data with the accuracy of 93.75 %. This method qualifies out of four benchmark methods with significant percent of improvement in accuracy for these three datasets. As per the spatial distribution of refined EEG channels, majority of the brain's motor functions concentrates on frontal and central cortex regions of brain.

Population-based evolutionary search for joint hyperparameter and architecture optimization in brain-computer interface

In recent years, deep learning (DL)-based models have become the de facto standard for motor imagery brain-computer interface (MI-BCI) systems due to their notable performance. However, these models often require extensive hyperparameter optimization process to achieve optimal results. To tackle this challenge, recent studies have proposed various methods to automate this process. Despite promising results, these methods overlook the architecture elements, which are crucial factors for MI-BCI system performance and are highly intertwined with hyperparameter settings. To overcome this limitation, we propose a joint optimization framework that uses a population-based evolutionary search to optimize both hyperparameters and architectures. Our framework adopts a two-stage optimization approach that alternates between hyperparameter and architecture optimization to effectively manage the complexity of the joint search process. Furthermore, we introduce a novel ensemble method that leverages diverse promising configurations to enhance generalization and robustness. Evaluations on two public MI-BCI datasets show that our framework consistently outperforms competing methods across a range of backbone models, demonstrating its effectiveness and versatility.

Automatic discrimination between neuroendocrine carcinomas and grade 3 neuroendocrine tumors by deep learning of H&E images

Neuroendocrine neoplasms (NENs) arise from diffuse neuroendocrine cells and are categorized as either well-differentiated and less proliferative Neuroendocrine Tumors (NETs), divided into low (G1), middle (G2), and high grades (G3), or poorly differentiated, and more proliferative Neuroendocrine Carcinomas (NECs). Low-grade NENs typically necessitate surgical intervention, whereas high-grade ones often require chemotherapy. However, low-grade NENs may exhibit aggressive behavior. Therefore, it is crucial to precisely refine the diagnosis of NENs. This refinement is achievable when differentiation/non-differentiation is evident or when the Ki-67 or mitosis index is low. The challenge arises in cases of morphologically undifferentiated instances with a high Ki-67 percentage and/or high mitotic index. To address this challenge, we developed a Deep Learning (DL) system named NEToC, designed to differentiate between NETs and NECs using exclusively morphological information from immunohistochemistry images, without relying on Ki-67 or mitosis assessments. NEToC was developed using 95 NEN cases from the period 2015 to 2018 at Parc Tauli Hospital in Spain, comprising 588 images. Implemented as a Graphical User Interface (GUI) system, NEToC is intended for deployment in pathological departments of hospitals to perform federated supervision. We tested the performance of NEToC with 119 images that were not used during the Artificial Neural Network (ANN) training phase, and evaluated its robustness across various resolutions: 64 × 64, 128 × 128, 256 × 256, and 512 × 512 pixels. The achieved accuracies for these resolutions were 74 %, 98 %, 98 %, and 100 %, respectively, for an underrepresented NET G3 experiment, and 66 %, 89 %, 95 % and 94 % for a represented NET G3 experiment. Based on several measured performance metrics, the optimal resolution appears to be between 128 × 128 and 256 × 256 pixels, considering computational resources and accuracy requirements. However, we found that the 256 × 256-pixel resolution is more robust to classify underrepresented classes in the learning phase. These results imply that the information to discriminate between NECs and Grade 3 NETs needs to be resolved in regions with a pixel resolution of no more than 4 μm/pixel. Most of the misclassifications were false negatives, where NET G1-type images were erroneously classified as NEC-type. Our results demonstrate that a DL-based diagnostic algorithm provides a more accurate diagnosis in NEN cases where physicians face challenges. NEToC has been initially trained with and used to classify gastrointestinal NENs. Since the NEN morphology does not change among the different organs, the use of NEToC can be extrapolated to NENs from different organs. NEToC facilitates federated supervision, allowing pathologists to collect interchangeable files based on NEToC classification predictions. NEToC is an easy-to-use, adaptable software that integrates multiple ANNs to improve standardization and accuracy NEN diagnosis, opening up possibilities for combining DL and histological diagnosis in federated supervision systems. A future goal is to classify not only NETs, but also the three-tier system (NET G1, NET G2, and NET G3) based solely on tissue differentiation information.

Enhancing detection of SSVEP-based BCIs via a novel temporally local canonical correlation analysis

BackgroundIn recent years, spatial filter-based frequency recognition methods have become popular in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. However, these methods are ineffective in suppressing local noise, and they rely on the length of the data. In practical applications, enhancing recognition performance with short data windows is a significant challenge for the BCI systems. New methodWith extracting temporal information and eliminating local noise, a temporally local canonical correlation analysis based on training data-driven (TI-tdCCA) method is proposed to enhance the recognition performance of SSVEPs. Based on a novel framework, the filters are derived by incorporating the Laplacian matrix through the use of TI-CCA between the concatenated training data and individual templates. The target frequency is subsequently determined by applying the appropriate spatial filters and Laplacian matrix. ResultsThe experimental results on two datasets, consisting of 40 classes and recording from 35 and 70 subjects respectively, demonstrate that the proposed method consistently outperforms the eight competing methods in the majority of cases. The proposed method is simultaneously evaluated by an extended version that incorporates artificial reference signals. The extended method demonstrates a significant improvement over the proposed method. Specifically, with a time window of 0.7 s, the average recognition accuracy of the subjects increases by 10.71 % on the Benchmark dataset and by 6.98 % on the BETA dataset, respectively. Comparison with existing methodsOur extended method outperforms the state-of-the-art methods by at least 3 %, and it effectively suppresses local noise and maintains excellent scalability. Conclusions for research articlesThe proposed method can effectively combine spatial and temporal filters to improve the recognition performance of SSVEPs.

DNA adsorption monitoring with interdigital open-gate junction field effect transistor for DNA storage applications: MD modeling, design, and experimental results

This paper introduces a novel portable sensing platform designed to explore the intricate interaction between DNA molecules and silicon dioxide (SiO2) coated on a field effect transistor (FET), bearing profound implications for a spectrum of life science applications, particularly DNA storage. In-silico molecular dynamic (MD) simulations are employed to gain insights into the effects of DNA on the FET surface's potential under extreme conditions of dehydration of double-stranded DNA (dsDNA), verifying its capacity to generate surface charge when dehydrated due to the evaporation of droplets. The platform is comprised of a backgated interdigital open gate junction FET (ID-OGJFET) fabricated through an innovative electronic sensing platform foundry and accompanied by an electronic interface system incorporated with a fluidic sample holder that reads the surface charge on the sensor induced by negatively charged nucleotides in dsDNA and single-stranded DNA (ssDNA). This novel ID-OGJFET sensor coated with native SiO2 enables a direct-on-channel sensing area of >16 mm2 without top gate extension for the target application, working at low back gate voltage (<0.4 V) for adsorption analysis in the dry mode. Our study involves designing, synthesizing, and introducing DNA sequences onto the chip surface at various concentrations. This paper presents and discusses experimental results that align with MD simulations, elucidating the impact of DNA's intrinsic charge on the chip's surface under extreme dehydration conditions. These findings pave the way for developing a portable charge-sensitive DNA monitoring system, particularly in the emerging physical storage of DNA.

Spatialspectral-Backdoor: Realizing backdoor attack for deep neural networks in brain–computer interface via EEG characteristics

In recent years, electroencephalogram (EEG) based on the brain–computer interface (BCI) systems have become increasingly advanced, with researcher using deep neural networks as tools to enhance performance. BCI systems heavily rely on EEG signals for effective human–computer interactions, and deep neural networks show excellent performance in processing and classifying these signals. Nevertheless, the vulnerability to backdoor attack is still a major problem. Backdoor attack is the injection of specially designed triggers into the model training process, which can lead to significant security issues. Therefore, in order to simulate the negative impact of backdoor attack and bridge the research gap in the field of BCI, this paper proposes a new backdoor attack method to call researcher attention to the security issues of BCI. In this paper, Spatialspectral-Backdoor is proposed to effectively attack the BCI system. The method is carefully designed to target the spectral active backdoor attack of the BCI system and includes a multi-channel preference method to select the electrode channels sensitive to the target task. Ultimately, the effectiveness of the comparison and ablation experiments is validated on the publicly available BCI competition datasets. The results show that the average attack success rate and clean sample accuracy of Spatialspectral-Backdoor in the BCI scenario are 97.12% and 85.16%, respectively, compared with other backdoor attack methods. Furthermore, by observing the infection ratio of backdoor triggers and visualization of the feature space, the proposed Spatialspectral-Backdoor outperforms other backdoor attack methods.

A study of external machine control pathways based on brain-computer interfaces

Abstract. This research analyses three fundamental paradigms in brain-computer interface (BCI) systems: motor imagery (MI), sensorimotor rhythms (SMRs), and P300-based paradigms, focusing on their methodology and applications. Motor imagery-based brain-computer interface (MI-BCI) entails mentally practicing motor activities while adjusting electroencephalography (EEG) patterns and utilising multimodal stimulation to enhance the accuracy of classification. SMR-BCI employs precise EEG band oscillations to operate prosthetics and virtual environments. Sensory threshold neuromuscular electrical stimulation is used to improve performance. The P300 paradigm utilizes event-related potentials to control devices, and includes advancements such as the omitted elicited paradigm to enhance efficiency. In addition, the study examines control strategies for BCI systems, such as PID, MPC, and shared control approaches. By utilizing MATLAB as a tool, one may ascertain the distinction between different control strategies, highlighting their respective contributions to improving system responsiveness and stability.

An End-to-End Brain Computer Interface System for Mental Workload Estimation through Hybrid Deep Learning Model

AbstractIn this paper, a new cascade one-dimensional convolutional neural network (1DCNN) and bidirectional long short-term memory (BLSTM) model has been developed for binary and ternary classification of mental workload (MWL). MWL assessment is important to increase the safety and efficiency in brain–computer interface (BCI) systems and professions, where multi-tasking is required. Keeping in mind the necessity of MWL assessment, a two-fold study is presented, firstly binary classification is done to classify MWL into low and high classes. Secondly, ternary classification is applied to classify MWL into low, moderate, and high classes. The cascaded1DCNN-BLSTM deep learning architecture has been developed and tested over the Simultaneous task EEG workload (STEW) dataset. Unlike recent research in MWL, handcrafted feature extraction and engineering are not done, rather end-to-end deep learning is used over 14 channel EEG signals for classification. Accuracies exceeding the previous state-of-the-art studies have been obtained. In binary and ternary classification accuracies of 96.77% and 95.36%have been achieved with sevenfold cross-validation, respectively.

File Compression System Using Huffman Coding

Abstract—File compression, which minimizes file sizes without significantly compromising the quality of information contained in the file, is among the most critical aspects of data management since it facilitates fast flow and storage of data. Storage manage- ment has become more challenging due to the exponential growth of digital data; hence, efficient file compression has been an issue of great importance to help in decreasing storage expenses and increasing data transfer rates. This paper presents a web interface for the file compression system that is based on lossless data compression known as Huffman coding to reduce the size of the files without changing their content. A standard internet portal allows the users to upload their files to the system and get back resized files within the shortest time possible. The technique employed in the compression is astute in that it uses Huffman codes which give characters codes depending with their frequencies in the file. It then follows that since most of the file will have short codes; the file will take up less space. To safeguard the compressed information, the system generates a data structure called a Huffman tree in which no code is a sub- string of any other. This application does seize to be usable from a few selected devices such as desktops and laptop computers but can also be accessed from projectors and cell phones because of the effective design. The file compression system is also versatile and accommodates various file categories such text, image and binary among others providing users with convenience in a number of sectors. Index Terms—Data Compression, Hoffman Coding, HTML( Hypertext Markup Language), CSS ( Cascading Styling Sheet ), JavaScript.

Agreeing to Disagree: Translating Representations to Uncover a Unified Representation for Social Robot Actions

Researchers and designers of social robots often approach robot control system design from a single perspective; such as designing autonomous robots, teleoperated robots, or robots programmed by an end-user. While each design approach presents a tradeoff between some advantages and limitations, there is an opportunity to integrate these approaches where people benefit from the best-fit approach for their use case. In this work, we propose integrating these seemingly distinct robot control approaches to uncover a common data representation of social actions defining social expression by a robot. We demonstrate the value of integrating an authoring system, teleoperation interface, and robot planning system by integrating instances of these systems for robot storytelling. By relying on an integrated system, domain experts can define behaviors through end-user interfaces that teleoperators and autonomous robot programmers can use directly thus providing a cohesive expert-driven robot system.

A Novel Filter Bank and Fourier Transform Convolutional Neural Network for SSVEP Classification

Abstract. Classifying Steady-State Visual Evoked Potentials (SSVEPs) is crucial for enhancing the performance of Brain-Computer Interface (BCI) systems. This study introduces a new method for SSVEP classification within BCI frameworks, called the Filter Bank and Fourier Transform Convolutional Neural Network (FBFCNN). The FBFCNN model effectively extracts key harmonic features by first segmenting SSVEP signals into multiple sub-bands through a filter bank. Subsequently, the Fast Fourier Transform (FFT) is applied to convert these signals from the time domain into the frequency domain. The generated spectrum's real and imaginary components are then fed into a convolutional neural network (CNN). This method improves SSVEP feature extraction and increases classification accuracy by fusing the advantages of CNNs with filter banks. Experimental results, derived from publicly available SSVEP datasets, show that the FBFCNN model surpasses traditional techniques in both accuracy and Information Transfer Rate (ITR). Offering a reliable and efficient solution for real-time brain signal decoding, the FBFCNN model represents a significant advancement in SSVEP-based BCI systems.

Advancements in EEG-based BCI for cognitive decline diagnosis using deep learning and Transformers

Abstract. Deep learning and Transformer models have revolutionized medical diagnostics, particularly in the advanced analysis of large datasets. Given the individual variability in cognitive decline status, the accuracy of diagnosing elderly cognitive decline diseases is crucial. Since it allows for the initiation of treatment and interventions at the earliest possible stage, potentially slowing the progression of the disease. In this paper, the author reviews and summarizes studies on the use of Transformers and deep learning in Electroencephalography (EEG) signal decoding, which is the most prevalent signal-detecting method in Brain-Computer Interface (BCI) systems for elderly cognitive decline. The review indicates that deep learning and transformer models outperform traditional methods in classifying cognitive decline, offering effective methods and improved accuracy by extracting and analyzing in-depth features from EEG data. Differently, deep learning models are proficient in single modality learning, but transformer models offer a unified approach to process and integrate information from diverse data sources and, with the increase in data volume, Transformers have the potential to further improve the accuracy of diagnosis.

Fluorous and Organic Extraction Systems: A Comparison from the Perspectives of Coordination Structures, Interfaces, and Bulk Extraction Phases.

Microscopic structures in liquid-liquid extraction, such as structuration between extractants or extracted complexes in bulk organic phases and at interfaces, can influence macroscopic phenomena, such as the distribution behavior of solutes, including extraction efficiency and selectivity. In this study, we correlated the macroscopic behavior of the Zr(IV) extraction from nitric acid solutions with microscopic structural information to understand at the molecular level the key factors contributing to the higher metal ion extraction performance in the fluorous extraction system as compared to the analogous organic extraction system. The fluorous and organic extraction systems consist of tris(4,4,5,5,6,6,7,7,7-nonafluoroheptyl) phosphate (TFP) in perfluorohexane and tri-n-heptyl phosphate (THP) in n-hexane, respectively. Extended X-ray absorption fine structure, neutron reflectometry (NR), and small-angle neutron scattering revealed the structural information around the central metal ion of the complex, at the interface, and in the bulk extraction phase, respectively. NR results showed that extractant molecules did not accumulate much at the interface in both extraction system. In the fluorous extraction system, extractant aggregates with a 1.46 nm radius of gyration (Rg) were formed after contact with nitric acid, and remained even after Zr(IV) extraction through the form of a 1:3 (Zr(IV):TFP) complex. In contrast, in the organic extraction system, only extractant dimers with Rg of 0.70 nm were formed and Zr(IV) is extracted through the form of a 1:2 (Zr(IV):THP) complex. We speculate that differences in the local coordination structure around the Zr(IV) ion and the structuration of the extractant molecules in the bulk extraction phase contribute to the high Zr(IV) extraction performance in the fluorous extraction system. In particular, the size of the aggregates hardly changed with increasing Zr(IV) concentration in the fluorous phase, which may be closely related to the absence of phase splitting in the fluorous extraction system.

Incremental Classification for High-Dimensional EEG Manifold Representation Using Bidirectional Dimensionality Reduction and Prototype Learning.

In brain-computer interface (BCI) systems, symmetric positive definite (SPD) manifold within Riemannian space has been frequently utilized to extract spatial features from electroencephalogram (EEG) signals. However, the intrinsic high dimensionality of SPD matrices introduces too much computational burden to hinder the real-time applications of such BCI, especially in handling dynamic tasks, like incremental learning. Directly reducing the dimensionality of SPD matrices with conventional dimensionality reduction (DR) methods will alter the fundamental properties of SPD matrices. Moreover, current DR methods for incremental learning always necessitate retaining old data to update their representations under new mapping. To this end, a bidirectional two-dimensional principal component analysis for SPD manifold (B2DPCA-SPD) is proposed to reduce the dimensionality of SPD matrices, in such way that the reduced matrices remain on SPD manifold. Afterwards, the B2DPCA-SPD is extended to adapt to incremental learning tasks without saving old data. The incremental B2DPCA-SPD can be seamlessly integrated with the matrix-formed growing neural gas network (MF-GNG) to achieve an incremental EEG classification, where the new low-dimensional representations of the prototypes in old classifiers can be easily recalculated with the updated projection matrix. Extensive experiments are conducted on two public datasets to perform the EEG classification. The results demonstrate that our method significantly reduces computation time by 38.53% and 35.96%, and outperforms conventional methods in classification accuracy by 4.21% to 19.59%.