White Blood Cell (WBC) differential counting via blood film examination is a critical diagnostic tool for rapid and accurate clinical decision-making in systemic inflammation in animals. To address the need for automated and reliable WBC classification, this study proposes ConTexT-Net, a single-modality multi representation fusion network that achieves robust WBC differentiation by integrating contour- and texture-based representations. The proposed methodology consists of two primary stages: first, contour information is extracted using Canny edge detection to outline nuclear boundaries, and texture information is captured through adaptive thresholding to highlight fine-grained intracellular patterns. Subsequently, the preprocessed representations are fused with the RGB appearance branch using late feature-level fusion to classify WBCs. Experimental validation demonstrates that ConTexT-Net significantly outperforms a DenseNet-121 baseline, achieving an accuracy gain of 1.7 percentage points$(88.9 \%$vs.$87.2 \%)$. This enhanced performance supports the model's value as a reliable decision-support tool for veterinary clinical practice.

Bees play a crucial role in human food production and ecosystem maintenance. However, they face a global crisis characterized by significant population decline, including colony collapse disorder, which threatens their survival. Therefore, the development of precision beekeeping Internet of Things (IoT) systems is pivotal for enhancing bee colony strength. This study analyzes and categorizes the research focusing on the colony strength into two main areas based on colony activity and threat detection. Research on colony activity aids beekeepers to effectively manage their hives by analyzing behaviors, such as internal status, swarming, and traffic. In threat detection, research focuses on identifying critical predators, such as hornets and varroa destructor, that significantly affect colony survival. This study conducts a comprehensive literature review, thoroughly presenting findings on the evolution and diversification of methodologies aimed at enhancing colony strength. By delving into various innovative approaches and assessing their effectiveness, this review highlights key developments that have significantly contributed to improving the resilience of bee populations against emerging threats. Furthermore, this study identifies the limitations of current research and proposes future research directions focused on enhancing the accuracy of bee behavior analysis and threat detection to improve colony strength and productivity.

Drowsy driving is one of the greatest threats to road safety, which increases the importance of intelligent systems that can monitor driver drowsiness. Electroencephalogram (EEG)–based monitoring systems have gained attention because EEG is known to directly measure brain activities that reflect the mental state of the driver. However, calibration is necessary before using the system because EEG signals vary between and within subjects. Therefore, generalized EEG-based drowsiness estimation has become challenging. In this paper, we propose an EEG-based driver drowsiness classification framework without the need for calibration, which can be generalized to unseen subjects. We augment the features of unseen domains (i.e., subjects) with a Dirichlet mixup between prototypes of source domains to complement other domain knowledge. The parameter$\boldsymbol{\alpha}$vector of the Dirichlet distribution adjusts the intensity of the mixup, allowing for diverse enhancement. Furthermore, we utilize an auxiliary batch normalization module for augmented samples to avoid inaccurate estimation by the difference in distribution. The experiments were carried out using two EEG datasets, each measured using different drowsiness indicators, the Karolinska sleepiness scale, and reaction time. In leave-one-subject-out cross-validation, the proposed framework achieved outstanding performance in both datasets, anF1-score of 62.69% and 70.33% and an area under the receiver operating characteristic curve (AUROC) of 71.73% and 73.80%, respectively. The experimental results demonstrate the potential for practical applications of brain-computer interfaces without calibration.

In veterinary medicine, the analysis of blood smears is crucial for diagnosing diseases such as systemic inflammatory response syndrome (SIRS) and sepsis, necessitating the identification and classification of white blood cells. Traditionally, this analysis is performed manually by observers, a process that is not only time-consuming and labor-intensive but also prone to variability in results between different observers. To address these challenges, this study introduces a two-stage framework that automates the detection and classification of white blood cells in smear images. Utilizing the YOLO-v8 model to detect all intact cells and the DenseNet model for classifying six distinct cell types, the framework aims to streamline the diagnostic process. Experimental results for the proposed two-stage framework demonstrate a mAP@50 of 0.964 for white blood cells detection and an accuracy of 0.836 for classification, surpassing conventional single-object detection models in both detection accuracy and classification efficacy.

The global prevalence of childhood and adolescent obesity is a major concern due to its association with chronic diseases and long-term health risks. Artificial intelligence technology has been identified as a potential solution to accurately predict obesity rates and provide personalized feedback to adolescents. This study highlights the importance of early identification and prevention of obesity-related health issues. To develop effective algorithms for the prediction of obesity rates and provide personalized feedback, factors such as height, weight, waist circumference, calorie intake, physical activity levels, and other relevant health information must be taken into account. Therefore, by collecting health datasets from 321 adolescents who participated in Would You Do It! application, we proposed an adolescent obesity prediction system that provides personalized predictions and assists individuals in making informed health decisions. Our proposed deep learning framework, DeepHealthNet, effectively trains the model using data augmentation techniques, even when daily health data are limited, resulting in improved prediction accuracy (acc: 0.8842). Additionally, the study revealed variations in the prediction of the obesity rate between boys (acc: 0.9320) and girls (acc: 0.9163), allowing the identification of disparities and the determination of the optimal time to provide feedback. Statistical analysis revealed that the performance of the proposed deep learning framework was more statistically significant (p 0.001) compared to the other general models. The proposed system has the potential to effectively address childhood and adolescent obesity.

Major Depressive Disorder (MDD) imposes a substantial burden within the healthcare domain, impacting millions of individuals worldwide. Functional Magnetic Resonance Imaging (fMRI) has emerged as a promising tool for the objective diagnosis of MDD, enabling the investigation of functional connectivity patterns in the brain associated with this disorder. However, most existing methods focus on a single brain atlas, which limits their ability to capture the complex, multi-scale nature of functional brain networks. To address these limitations, we propose a novel multi-atlas fusion method that incorporates early and late fusion in a unified framework. Our method introduces the concept of the holistic Functional Connectivity Network (FCN), which captures both intra-atlas relationships within individual atlases and inter-regional relationships between atlases with different brain parcellation scales. This comprehensive representation enables the identification of potential disease-related patterns associated with MDD in the early stage of our framework. Moreover, by decoding the holistic FCN from various perspectives through multiple spectral Graph Convolutional Neural Networks and fusing their results with decision-level ensembles, we further improve the performance of MDD diagnosis. Our approach is easily implemented with minimal modifications to existing model structures and demonstrates a robust performance across different baseline models. Our method, evaluated on public resting-state fMRI datasets, surpasses the current multi-atlas fusion methods, enhancing the accuracy of MDD diagnosis. The proposed novel multi-atlas fusion framework provides a more reliable MDD diagnostic technique. Experimental results show our approach outperforms both single- and multi-atlas-based methods, demonstrating its effectiveness in advancing MDD diagnosis.

Artificial intelligence (AI) has transformed medical diagnosis and improved quality of life. But in the field of veterinary medicine has been limited due to training data and obtaining high-quality data. In this study, we propose a framework for diagnosing left atrial enlargement in dogs using AI techniques. Our framework involves generating X-ray image data and utilizing the UNet model for segmentation. The results of our experiments show excellent performance, with a mean dice score of 0.9186 for segmentation. The highest classification accuracy was achieved in trial 1 for normal and overall cases, with 0.9200 and 0.8478, respectively, while trial 2 had the highest abnormal heart classification accuracy of 0.8095. Our findings indicate that generating data and training the model with a certain percentage of the generated data can lead to high classification accuracy. We conclude that the proposed framework has the potential for clinical application in veterinary medicine.

Electroencephalogram (EEG)-based brain-machine interface (BMI) has been utilized to help patients regain motor function and has recently been validated for its use in healthy people because of its ability to directly decipher human intentions. In particular, neurolinguistic research using EEGs has been investigated as an intuitive and naturalistic communication tool between humans and machines. In this study, the human mind directly decoded the neural languages based on speech imagery using the proposed deep neurolinguistic learning. Through real-time experiments, we evaluated whether BMI-based cooperative tasks between multiple users could be accomplished using a variety of neural languages. We successfully demonstrated a BMI system that allows a variety of scenarios, such as essential activity, collaborative play, and emotional interaction. This outcome presents a novel BMI frontier that can interact at the level of human-like intelligence in real time and extends the boundaries of the communication paradigm.

Detection of the pilots’ mental states is particularly critical because their abnormal mental states (AbSs) could cause catastrophic accidents. In this study, we presented the feasibility of classifying the various specific AbSs (namely, low fatigue, high fatigue, low workload, high workload, low distraction, and high distraction) by applying the deep learning method. To the best of our knowledge, this study is the first attempt to classify multiple AbSs of pilots. We proposed the hybrid deep neural networks with five convolutional blocks and two long short-term memory layers for decoding multiple AbSs. We designed the model to extract the informative features from electroencephalography signals. A total of ten pilots conducted the experiment in a simulated flight environment. Compared with five conventional models, our proposed model achieved the highest grand-average accuracy of 68.04$(\pm$5.26)% which is at least 6.55% higher than other conventional models for classifying seven mental states across all subjects. Our proposed model could distinguish and classify low and high levels for each status category and give appropriate feedback to the subjects. In addition, we found nine indicators that showed the statistically significant differences between two mental states (p$<$0.05). Hence, we believe that it will contribute significantly to autonomous driving or autopilot advances based on artificial intelligence technology in the future.

Drowsy driving causes severe road traffic accidents and significantly threatens road driving. Recently, electroencephalogram (EEG)-based drowsiness state classification has gained attention in the field of brain-computer interface (BCI). Because of the inter-and intra-subject variability of EEG signals, EEG-based drowsiness state classification is still challenging in developing an estimator applicable to unseen subjects. Generally, calibration sessions are required to tune the model with subject-specific data. In this paper, we propose an EEG-based driver drowsiness state (i.e., alert and drowsy) classification framework that improves the generalization performance to unseen subjects. Style features of multi-domain instances are mixed to generate unseen domains, and the distance of labels within classes is minimized to learn robust representations. Experiments were conducted on EEG data acquired from a drowsy driving experiment in a simulated-driving environment. Our proposed framework achieved an accuracy of 77.26%, an F1-score of 0.6266, and a recall of 0.6813 across eleven subjects in leave-one-subject-out cross-validation. The experimental results showed an improvement in the generalization performance for novel target subjects in driver drowsiness state classification and demonstrated the potential for calibration-free BCI.

Brain-computer interfaces (BCIs) have been widely employed to identify and estimate a user's intention to trigger a robotic device by decoding motor imagery (MI) from an electroencephalogram (EEG). However, developing a BCI system driven by MI related to natural hand-grasp tasks is challenging due to its high complexity. Although numerous BCI studies have successfully decoded large body parts, such as the movement intention of both hands, arms, or legs, research on MI decoding of high-level behaviors such as hand grasping is essential to further expand the versatility of MI-based BCIs. In this study, we propose NeuroGrasp, a dual-stage deep learning framework that decodes multiple hand grasping from EEG signals under the MI paradigm. The proposed method effectively uses an EEG and electromyography (EMG)-based learning, such that EEG-based inference at test phase becomes possible. The EMG guidance during model training allows BCIs to predict hand grasp types from EEG signals accurately. Consequently, NeuroGrasp improved classification performance offline, and demonstrated a stable classification performance online. Across 12 subjects, we obtained an average offline classification accuracy of 0.68 (±0.09) in four-grasp-type classifications and 0.86 (±0.04) in two-grasp category classifications. In addition, we obtained an average online classification accuracy of 0.65 (±0.09) and 0.79 (±0.09) across six high-performance subjects. Because the proposed method has demonstrated a stable classification performance when evaluated either online or offline, in the future, we expect that the proposed method could contribute to different BCI applications, including robotic hands or neuroprosthetics for handling everyday objects.

Brain-computer interface (BCI) is used for communication between humans and devices by recognizing humans’ status and intention. Communication between humans and a drone using electroencephalogram (EEG) signals is one of the most challenging issues in the BCI domain. In particular, the control of drone swarms (the direction and formation) has more advantages compared to the control of a drone. The visual imagery (VI) paradigm is that subjects visually imagine specific objects or scenes. Reduction of the variability among subjects’ EEG signals is essential for practical BCI-based systems. In this study, we proposed the subepoch-wise feature encoder (SEFE) to improve the performances in the subject-independent tasks by using the VI dataset. This study is the first attempt to demonstrate the possibility of generalization among subjects in the VI-based BCI. We used the leave-one-subject-out cross-validation for evaluating the performances. We obtained higher performances when including our proposed module than excluding our proposed module. The DeepConvNet with SEFE showed the highest performance of 0.72 among six different decoding models. Hence, we demonstrated the feasibility of decoding the VI dataset in the subject-independent task with robust performances by using our proposed module.

A brain-computer interface (BCI) is used to control external devices for healthy people as well as to rehabilitate motor functions for motor-disabled patients. Decoding movement intention is one of the most significant aspects for performing arm movement tasks using brain signals. Decoding movement execution (ME) from electroencephalogram (EEG) signals have shown high performance in previous works, however movement imagination (MI) paradigm-based intention decoding has so far failed to achieve sufficient accuracy. In this study, we focused on a robust MI decoding method with transfer learning for the ME and MI paradigm. We acquired EEG data related to arm reaching for 3D directions. We proposed a BCI-transfer learning method based on a Relation network (BTRN) architecture. Decoding performances showed the highest performance compared to conventional works. We confirmed the possibility of the BTRN architecture to contribute to continuous decoding of MI using ME datasets.

A brain-computer interface (BCI) provides a direct communication pathway between user and external devices. EEG-based motor imagery paradigm is widely used in non-invasive BCI to obtain encoded signals contained user intention of movement execution. However, EEG has intricate and non-stationary properties resulting in insufficient decoding performance. By imagining numerous movements of a single-arm, decoding performance can be improved without artificial command matching. In this study, we collected intuitive EEG data contained the nine different types of movements of a single-arm from 9 subjects. We propose an end-to-end role assigned convolutional neural network (ERA-CNN) which considers discriminative features of each upper limb region by adopting the principle of a hierarchical CNN architecture. The proposed model outperforms previous methods on 3-class, 5-class and two different types of 7-class classification tasks. Hence, we demonstrate the possibility of decoding user intention by using only EEG signals with robust performance using the ERA-CNN.

In this study, we adopted visual motion imagery, which is a more intuitive brain-computer interface (BCI) paradigm, for decoding the intuitive user intention. We developed a 3-dimensional BCI training platform and applied it to assist the user in performing more intuitive imagination in the visual motion imagery experiment. The experimental tasks were selected based on the movements that we commonly used in daily life, such as picking up a phone, opening a door, eating food, and pouring water. Nine subjects participated in our experiment. We presented statistical evidence that visual motion imagery has a high correlation from the prefrontal and occipital lobes. In addition, we selected the most appropriate electroencephalography channels using a functional connectivity approach for visual motion imagery decoding and proposed a convolutional neural network architecture for classification. As a result, the averaged classification performance of the proposed architecture for 4 classes from 16 channels was 67.50 (±1.52)% across all subjects. This result is encouraging, and it shows the possibility of developing a BCI-based device control system for practical applications such as neuroprosthesis and a robotic arm.

Communication using brain-computer interface (BCI) has developed in attempts toward an intuitive system by decoding the imagined speech or visual imagery. However, discrimination between the two paradigms may be ambiguous because the user intention contains their original meaning. A clear distinction between the two paradigms may facilitate the active use of them leading to an intuitive BCI conversation system. In this study, we compared imagined speech and visual imagery in the perspective of its presence, spatial features, and classification performance based on electroencephalography. Seven subjects performed both imagined speech and visual imagery of twelve words/phrases. We showed the presence of the two paradigms, having distinct brain region from each other. The maximum thirteen-class classification accuracy including rest class was 34.2 % for imagined speech and 26.7 % for visual imagery. Therefore, we investigated the possibility of multiclass classification of more than ten classes in both paradigms, showing the potential of them to be used in the real world communication system. These findings could further be utilized in the intuitive communication for locked-in patients sending commands to the external world simply by thinking of `the very thing' that the user wants to deliver.

Brain-machine interface (BMI) provides a new control strategy for both patients and healthy people. An endogenous paradigm such as motor imagery (MI) for BMI is commonly used for detecting user intention without external stimuli. However, manipulating the dexterous robotic arm by using limited MI commands is challenging issues. In this paper, we designed a shared robotic arm control system using the intuitive MI and vision guidance. To accomplish the user's intention on the robotic arm, we used arm reach MI (left, right, and forward), hand grasp MI, and wrist twist MI by using electroencephalogram (EEG) signals. The Kinect sensor is used to match the decoded user intention with the detected object based on the location of the workspace. In addition, to decode intuitive MI successfully, we propose a novel convolutional neural network (CNN) based user intention decoding model. Ten subjects participated in our experiments, and five of them were selected to perform online tasks. The proposed method could decode various user intention (five intuitive MI classes and resting state) with a grand-averaged classification accuracy of 55.91% in offline analysis. For sufficient control on the online shared robotic arm control, the proposed online system was only started, once the patient shows higher performance than 60% in the offline analysis. For the online drinking tasks, we confirmed the averaged 78% success rate. Hence, we confirmed the possibility of the shared robotic arm control based on intuitive BMI and vision guidance with high performance.

Data prefetching is a hardware-based optimization mechanism used in most of the modern microprocessors. It fetches data to the cache before it is needed. In this paper, we present a novel microarchitectural attack that exploits the prefetching mechanism. Our attack targets Instruction pointer (IP)-based stride prefetching in Intel processors. Stride prefetcher detects memory access patterns with a regular stride, which are likely to be found in lookup table-based cryptographic implementations. By monitoring the prefetching activities near the lookup table, attackers can extract sensitive information such as secret keys from victim applications. This kind of leakage from prefetching has never been considered in the design of constant time algorithm to prevent side-channel attacks. We show the potential of the proposed attack by applying it against the Elliptic Curve Diffie-Hellman (ECDH) algorithm built upon the latest version of OpenSSL library. To the best of our knowledge, this is the first microarchitectural side-channel attack exploiting the hardware prefetching of modern microprocessors.

The development of brain-computer interface (BCI) systems that are based on electroencephalography (EEG), and driven by spontaneous movement intentions, is useful for rehabilitation and external device control. In this study, we analyzed the decoding of five different hand executions and imageries from EEG signals, for a robot hand control. Five healthy subjects participated in this experiment. They executed and imagined five sustained hand motions. In this motor execution (ME) and motor imagery (MI) experiment, we proposed a subject-specific time interval selection method, and we used common spatial patterns (CSP) and the regularized linear discriminant analysis (RLDA) for the data analysis. As a result, we classified the five different hand motions offline and obtained average classification accuracies of 56.83% for ME, and 51.01% for MI, respectively. Both results were higher than the obtained accuracies from a comparison method that used a standard fixed time interval method. This result is encouraging, and the proposed method could potentially be used in future applications, such as a BCI-driven robot hand control.

This paper presents the feasibility of an electroencephalography (EEG)-based robot arm control system using a decoding of multi-directional arm reaching movement imagery. To do that, we have designed and implemented an experimental environment that can acquire non-invasive brain signals about multi-directional arm reaching movement. Five subjects participated in our experiments and the subjects performed four directional reaching tasks (Left, right, forward, and backward) with actual movement and movement imagery. The filter-bank common spatial pattern (FBCSP) was applied to extract spatio-frequency features from the acquired EEG signals. The regularized linear discriminant analysis (RLDA) was also applied as a classifier. As a result, the averaged classification accuracies of the actual movement and movement imagery were represented 67.04% and 59.19%, respectively. These results showed a feasibility of the EEG-based robot arm control system based on multi-directional arm reaching movement imagery.

Recently, there has been an increased demand for Brain-Machine Interface (BMI) toolboxes for neuroscientifc research. In many BMI applications, speller systems can provide an efficient communication channel for users with disabilities. Here, we introduce an open-source BMI toolbox termed `OpenBMI', which supports the various signal processing chains for common BMI paradigms, such as event-related potentials (ERPs) and steady-state visual evoked potentials (SSVEP). The OpenBMI framework consists of ready-to-use experimental paradigms, offline data analysis techniques, online feedback as well as evaluation modules. The data analysis modules provide essential pre-processing steps (segmentation, baseline correction, etc.) as well as signal processing algorithms such as temporal and spatial filtering, artifact rejection, among others. The experimental paradigms of ERP and SSVEP are available with fully open-sourced demo scripts. Users can easily modify or extend the demo scripts for their needs. In this article, the OpenBMI framework, its features as well as its future development plan is introduced.