Surface-functionalized SERS platform for deep learning-assisted diagnosis of Alzheimer's disease.

Early diagnosis of Alzheimer's disease is crucial to stall the deterioration of brain function, but conventional diagnostic methods require complicated analytical procedures or inflict acute pain on the patient. Then, label-free Surface-enhanced Raman spectroscopy (SERS) analysis of blood-based biomarkers is a convenient alternative to rapidly obtain spectral information from biofluids. However, despite the rapid acquisition of spectral information from biofluids, it is challenging to distinguish spectral features of biomarkers due to interference from biofluidic components. Here, we introduce a deep learning-assisted, SERS-based platform for separate analysis of blood-based amyloid ß (1-42) and metabolites, enabling the diagnosis of Alzheimer's disease. SERS substrates consisting of Au nanowire arrays are fabricated and functionalized in two characteristic ways to compare the validity of different Alzheimer's disease biomarkers measured on our SERS system. The 6E10 antibody is immobilized for the capture of amyloid ß (1-42) and analysis of its oligomerization process, while various self-assembled monolayers are attached for different dipole interactions with blood-based metabolites. Ultimately, SERS spectra of blood plasma of Alzheimer's disease patients and human controls are measured on the substrates and classified via advanced deep learning techniques that automatically extract informative features to learn generalizable representations. Accuracies up to 99.5% are achieved for metabolite-based analyses, which are verified with an explainable artificial intelligence technique that identifies key spectral features used for classification and for deducing significant biomarkers.

Adapting Artifact Subspace Reconstruction Method for SingleChannel EEG using Signal Decomposition Techniques.

Artifact removal from electroencephalography (EEG) data is a crucial step in the analysis of neural signals. One method that has been gaining popularity in recent years is Artifact Subspace Reconstruction (ASR), which is highly effective in eliminating large amplitude and transient artifacts in EEG data. However, traditional ASR is not possible to use with single-channel EEG data. In this study, we propose incorporating signal decomposition techniques such as ensemble empirical mode decomposition (EEMD), wavelet transform (WT), and singular spectrum analysis (SSA) into ASR, to decompose single-channel data into multiple components. This allows the ASR process to be applied effectively to the data. Our results show that the proposed single-channel version of ASR outperforms its counterpart Independent Component Analysis (ICA) methods when tested on two open datasets. Our findings indicate that ASR has significant potential as a powerful tool for removing artifacts from EEG data analysis.Clinical Relevance- This provided artifact removal technique for single-channel EEG.

The future of wearable EEG: a review of ear-EEG technology and its applications.

Objective.This review paper provides a comprehensive overview of ear-electroencephalogram (EEG) technology, which involves recording EEG signals from electrodes placed in or around the ear, and its applications in the field of neural engineering.Approach.We conducted a thorough literature search using multiple databases to identify relevant studies related to ear-EEG technology and its various applications. We selected 123 publications and synthesized the information to highlight the main findings and trends in this field.Main results.Our review highlights the potential of ear-EEG technology as the future of wearable EEG technology. We discuss the advantages and limitations of ear-EEG compared to traditional scalp-based EEG and methods to overcome those limitations. Through our review, we found that ear-EEG is a promising method that produces comparable results to conventional scalp-based methods. We review the development of ear-EEG sensing devices, including the design, types of sensors, and materials. We also review the current state of research on ear-EEG in different application areas such as brain-computer interfaces, and clinical monitoring.Significance.This review paper is the first to focus solely on reviewing ear-EEG research articles. As such, it serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering. Our review sheds light on the exciting future prospects of ear-EEG, and its potential to advance neural engineering research and become the future of wearable EEG technology.

Decoding auditory-evoked response in affective states using wearable around-ear EEG system.

Objective.In this paper, an around-ear EEG system is investigated as an alternative methodology to conventional scalp-EEG-based systems in classifying human affective states in the arousal-valence domain evoked in response to auditory stimuli.Approach.EEG recorded from around the ears is compared to EEG collected according to the international 10-20 system in terms of efficacy in an affective state classification task. A wearable device with eight dry EEG channels is designed for ear-EEG acquisition in this study. Twenty-one subjects participated in an experiment consisting of six sessions over three days using both ear and scalp-EEG acquisition methods. Experimental tasks consisted of listening to an auditory stimulus and self-reporting the elicited emotion in response to the said stimulus. Various features were used in tandem with asymmetry methods to evaluate binary classification performances of arousal and valence states using ear-EEG signals in comparison to scalp-EEG.Main results.We achieve an average accuracy of 67.09% ± 6.14 for arousal and 66.61% ± 6.14 for valence after training a multi-layer extreme learning machine with ear-EEG signals in a subject-dependent context in comparison to scalp-EEG approach which achieves an average accuracy of 68.59% ± 6.26 for arousal and 67.10% ± 4.99 for valence. In a subject-independent context, the ear-EEG approach achieves 63.74% ± 3.84 for arousal and 64.32% ± 6.38 for valence while the scalp-EEG approach achieves 64.67% ± 6.91 for arousal and 64.86% ± 5.95 for valence. The best results show no significant differences between ear-EEG and scalp-EEG signals for classifications of affective states.Significance.To the best of our knowledge, this paper is the first work to explore the use of around-ear EEG signals in emotion monitoring. Our results demonstrate the potential use of around-ear EEG systems for the development of emotional monitoring setups that are more suitable for use in daily affective life log systems compared to conventional scalp-EEG setups.

Neural Applications Using Immersive Virtual Reality: A Review on EEG Studies.

Recent advancements in immersive virtual reality head-mounted displays allowed users to better engage with simulated graphical environments. Having the screen egocentrically stabilized in a way such that the users may freely rotate their heads to observe virtual surroundings, head-mounted displays present virtual scenarios with rich immersion. With such an enhanced degree of freedom, immersive virtual reality displays have also been integrated with electroencephalograms, which make it possible to study and utilize brain signals non-invasively, to analyze and apply their capabilities. In this review, we introduce recent progress that utilized immersive head-mounted displays along with electroencephalograms across various fields, focusing on the purposes and experimental designs of their studies. The paper also highlights the effects of using immersive virtual reality discovered through the electroencephalogram analysis and discusses existing limitations, current trends as well as future research opportunities that may hopefully act as a useful source of information for further improvement of electroencephalogram-based immersive virtual reality applications.

A novel online BCI system using speech imagery and ear-EEG for home appliances control.

BACKGROUND AND OBJECTIVE: This paper investigates a novel way to interact with home appliances via a brain-computer interface (BCI), using electroencephalograph (EEG) signals acquired from around the user's ears with a custom-made wearable BCI headphone. METHODS: The users engage in speech imagery (SI), a type of mental task where they imagine speaking out a specific word without producing any sound, to control an interactive simulated home appliance. In this work, multiple models are employed to improve the performance of the system. Temporally-stacked multi-band covariance matrix (TSMBC) method is used to represent the neural activities during SI tasks with spatial, temporal, and spectral information included. To further increase the usability of our proposed system in daily life, a calibration session, where the pre-trained models are fine-tuned, is added to maintain performance over time with minimal training. Eleven participants were recruited to evaluate our method over three different sessions: a training session, a calibration session, and an online session where users were given the freedom to achieve a given goal on their own. RESULTS: In the offline experiment, all participants were able to achieve a classification accuracy significantly higher than the chance level. In the online experiments, a few participants were able to use the proposed system to freely control the home appliance with high accuracy and relatively fast command delivery speed. The best participant achieved an average true positive rate and command delivery time of 0.85 and 3.79 s/command, respectively. CONCLUSION: Based on the positive experimental results and user surveys, the novel ear-EEG-SI-based BCI paradigm is a promising approach for the wearable BCI system for daily life.

Separation-free bacterial identification in arbitrary media via deep neural network-based SERS analysis.

Universal and fast bacterial detection technology is imperative for food safety analyses and diagnosis of infectious diseases. Although surface-enhanced Raman spectroscopy (SERS) has recently emerged as a powerful solution for detecting diverse microorganisms, its widespread application has been hampered by strong signals from surrounding media that overwhelm target signals and require time-consuming and tedious bacterial separation steps. By using SERS analysis boosted with a newly proposed deep learning model named dual-branch wide-kernel network (DualWKNet), a markedly simpler, faster, and effective route to classify signals of two common bacteria E. coli and S. epidermidis and their resident media without any separation procedures is demonstrated. With outstanding classification accuracies up to 98%, the synergistic combination of SERS and deep learning serves as an effective platform for "separation-free" detection of bacteria in arbitrary media with short data acquisition times and small amounts of training data.

ALIS: Learning Affective Causality Behind Daily Activities From a Wearable Life-Log System.

Human emotions and behaviors are reciprocal components that shape each other in everyday life. While the past research on each element has made use of various physiological sensors in many ways, their interactive relationship in the context of daily life has not yet been explored. In this work, we present a wearable affective life-log system (ALIS) that is robust as well as easy to use in daily life to accurately detect emotional changes and determine the cause-and-effect relationship between emotions and emotional situations in users' lives. The proposed system records how a user feels in certain situations during long-term activities using physiological sensors. Based on the long-term monitoring, the system analyzes how the contexts of the user's life affect his/her emotional changes and builds causal structures between emotions and observable behaviors in daily situations. Furthermore, we demonstrate that the proposed system enables us to build causal structures to find individual sources of mental relief suited to negative situations in school life.

Isolated mass-forming IgG4-related sclerosing cholangitis masquerading as extrahepatic cholangiocarcinoma: A case report.

BACKGROUND: IgG4-related sclerosing cholangitis (IgG4-RSC) is an uncommon benign disease, and its rarer, isolated and mass-forming subtype poses a significant challenge to differential diagnosis from cholangiocarcinoma of the extrahepatic bile duct. We herein report a case of isolated IgG4-RSC with an obstructing bile duct mass, for which extrahepatic bile duct resection was performed under the impression of proximal common bile duct (CBD) cancer. CASE SUMMARY: A 79-year-old male was admitted for jaundice that had developed 1 mo prior. There was no family history for autoimmune diseases or biliary cancer. Computed tomography (CT) and magnetic resonance cholangiopancreaticography revealed a short segmental concentric wall thickening of the proximal CBD with diffuse dilatation of the bile duct to the periphery. The endoscopic biopsy specimen showed no malignant cells. Positron emission tomography-CT showed a focal hypermetabolic lesion (SUVmax 4.2) in and around the proximal CBD area. With the impression of proximal CBD cancer, we performed segmental resection of the extrahepatic bile duct. Histopathology demonstrated marked sclerosis with diffuse lymphoplasmacytic infiltration and some eosinophils. Immunohistochemical staining for IgG4 showed increased positivity in some areas (up to 30/high-power field) and IgG4+/IgG+ cell ratio as 30%-50%. Pathologists' impression was IgG4-related sclerosing disease. Follow-up serum IgG4 levels were continuously elevated; however, no evidence of relapse or other organ involvement related to IgG4-RSC presented. CONCLUSION: Isolated and mass-forming IgG4-RSC displays striking similarity with cholangiocarcinoma. To avoid unnecessary major surgery, high index of suspicion is needed.

Impact of prognostic nutritional index on the recurrence of hepatocellular carcinoma after a curative resection.

BACKGROUNDS/AIMS: The purpose of this retrospective study was to determine the association between prognostic nutritional index (PNI) and recurrence of hepatocellular carcinoma after a curative resection. METHODS: Between 2007 to 2019, 130 patients who underwent curative hepatectomy for hepatocellular carcinoma were enrolled. PNI was calculated. Its cutoff value was identified through receiver operating characteristic curve analysis. According to PNI, patients were divided into two groups. Univariate and multivariate analyses were performed to identify independent risk factors for recurrence. RESULTS: The cutoff value of PNI was 52. In univariate analysis, alcoholic liver cirrhosis (p = 0.041), protein induced by vitamin K antagonist- II ≥ 200 (p = 0.012), indocyanine green retention test (ICG R15) >10% (p = 0.001), estimated blood loss ≥ 800 mL (p = 0.037), tumor size (p = 0.001), microvascular invasion (p = 0.023), T-stage (p = 0.001), and PNI < 52 (p = 0.001) were significant factors affecting the recurrence. In multivariate analysis, alcoholic liver cirrhosis (p = 0.046), ICG R15 >10% (p = 0.025), T-stage (p = 0.003), and PNI < 52 (p = 0.046) were independent prognostic factors for disease-free survival. CONCLUSIONS: PNI, a nutritional and immunologic factor, is an independent prognostic factor that can predict the recurrence of hepatocellular carcinoma in patients undergoing a curative resection.

Speech-imagery-based brain-computer interface system using ear-EEG.

OBJECTIVE: This study investigates the efficacy of electroencephalography (EEG) centered around the user's ears (ear-EEG) for a speech-imagery-based brain-computer interface (BCI) system. APPROACH: A wearable ear-EEG acquisition tool was developed and its performance was directly compared to that of a conventional 32-channel scalp-EEG setup in a multi-class speech imagery classification task. Riemannian tangent space projections of EEG covariance matrices were used as input features to a multi-layer extreme learning machine classifier. Ten subjects participated in an experiment consisting of six sessions spanning three days. The experiment involves imagining four speech commands ('Left,' 'Right,' 'Forward,' and 'Go back') and staying in a rest condition. MAIN RESULTS: The classification accuracy of our system is significantly above the chance level (20%). The classification result averaged across all ten subjects is 38.2% and 43.1% with a maximum (max) of 43.8% and 55.0% for ear-EEG and scalp-EEG, respectively. According to an analysis of variance, seven out of ten subjects show no significant difference between the performance of ear-EEG and scalp-EEG. SIGNIFICANCE: To our knowledge, this is the first study that investigates the performance of ear-EEG in a speech-imagery-based BCI. The results indicate that ear-EEG has great potential as an alternative to the scalp-EEG acquisition method for speech-imagery monitoring. We believe that the merits and feasibility of both speech imagery and ear-EEG acquisition in the proposed system will accelerate the development of the BCI system for daily-life use.

Review of machine learning methods in soft robotics.

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots.

Improving performance in motor imagery BCI-based control applications via virtually embodied feedback.

OBJECTIVE: Brain-computer interfaces (BCIs) based on motor imagery (MI) are commonly used for control applications. However, these applications require strong and discriminant neural patterns for which extensive experience in MI may be necessary. Inspired by the field of rehabilitation where embodiment is a key element for improving cortical activity, our study proposes a novel control scheme in which virtually embodiable feedback is provided during control to enhance performance. METHODS: Subjects underwent two immersive virtual reality control scenarios in which they controlled the two-dimensional movement of a device using electroencephalography (EEG). The two scenarios only differ on whether embodiable feedback, which mirrors the movement of the classified intention, is provided. After undergoing each scenario, subjects also answered a questionnaire in which they rated how immersive the scenario and embodiable the feedback were. RESULTS: Subjects exhibited higher control performance, greater discriminability in brain activity patterns, and enhanced cortical activation when using our control scheme compared to the standard control scheme in which embodiable feedback is absent. Moreover, the self-rated embodiment and presence scores showed significantly positive linear relationships with performance. SIGNIFICANCE: The findings in our study provide evidence that providing embodiable feedback as guidance on how intention is classified may be effective for control applications by inducing enhanced neural activity and patterns with greater discriminability. By applying embodiable feedback to immersive virtual reality, our study also serves as another instance in which virtual reality is shown to be a promising tool for improving MI.

A-Situ: a computational framework for affective labeling from psychological behaviors in real-life situations.

This paper presents a computational framework for providing affective labels to real-life situations, called A-Situ. We first define an affective situation, as a specific arrangement of affective entities relevant to emotion elicitation in a situation. Then, the affective situation is represented as a set of labels in the valence-arousal emotion space. Based on psychological behaviors in response to a situation, the proposed framework quantifies the expected emotion evoked by the interaction with a stimulus event. The accumulated result in a spatiotemporal situation is represented as a polynomial curve called the affective curve, which bridges the semantic gap between cognitive and affective perception in real-world situations. We show the efficacy of the curve for reliable emotion labeling in real-world experiments, respectively concerning (1) a comparison between the results from our system and existing explicit assessments for measuring emotion, (2) physiological distinctiveness in emotional states, and (3) physiological characteristics correlated to continuous labels. The efficiency of affective curves to discriminate emotional states is evaluated through subject-dependent classification performance using bicoherence features to represent discrete affective states in the valence-arousal space. Furthermore, electroencephalography-based statistical analysis revealed the physiological correlates of the affective curves.

Observing Actions Through Immersive Virtual Reality Enhances Motor Imagery Training.

Visual information plays an essential role in enhancing neural activity during mental practices. Previous research has shown that using different visual scenarios during mental practices that involve imagining the movement of a specific body part may result in differences in performance. Many of these scenarios utilize the concept of embodiment, or one's observation of another entity to be a part of oneself, to improve practice quality of the imagined body movement. We therefore hypothesized that applying immersive virtual reality headsets, with their ability to provide rich immersion and illusion by presenting egocentrically simulated virtual scenarios, and action observation to motor imagery practice will result in significant improvement. To explore the possible synergy between immersive systems and motor imagery, we analyzed the electroencephalogram signals of our participants as they were presented the same virtual hand movement scenario with two different mediums: an immersive virtual reality headset and a monitor display. Our experimental results provide evidence that the immersive virtual reality headsets induced improved rhythmic patterns with better discriminating spatial features from the brain compared to the monitor display. These findings suggest that the use of immersive virtual reality headsets, with the illusion and embodiment they provide, can effectively improve motor imagery training.

Interactive virtual objects attract attention and induce exploratory behaviours in rats.

Animals use visual information to recognize the value of objects and respond with different behaviours, such as evasion or approach. While rodents show defensive behaviour toward an artificial looming stimulus that mimics an approaching avian predator, the visual feature that attracts them to targets with positive value, such as prey, remains unclear. Here, we reveal that rats show curiosity-related behaviours towards a virtual object on screen when it moves interactively with their movements, whereas they show less response to a static object, a regularly moving object, or interactive dislocation of the background. To mimic evading prey, we programmed the object to shrink when touched. Rats preferentially responded to interactive shrinking over interactive enlargement. These results suggest that rats exhibit a selective response to interactive objects. This would seem to be an efficient strategy for finding optimal prey using the evolutionarily conserved prey-predator relationship.

Data-driven analysis using multiple self-report questionnaires to identify college students at high risk of depressive disorder.

Depression diagnosis is one of the most important issues in psychiatry. Depression is a complicated mental illness that varies in symptoms and requires patient cooperation. In the present study, we demonstrated a novel data-driven attempt to diagnose depressive disorder based on clinical questionnaires. It includes deep learning, multi-modal representation, and interpretability to overcome the limitations of the data-driven approach in clinical application. We implemented a shared representation model between three different questionnaire forms to represent questionnaire responses in the same latent space. Based on this, we proposed two data-driven diagnostic methods; unsupervised and semi-supervised. We compared them with a cut-off screening method, which is a traditional diagnostic method for depression. The unsupervised method considered more items, relative to the screening method, but showed lower performance because it maximized the difference between groups. In contrast, the semi-supervised method adjusted for bias using information from the screening method and showed higher performance. In addition, we provided the interpretation of diagnosis and statistical analysis of information using local interpretable model-agnostic explanations and ordinal logistic regression. The proposed data-driven framework demonstrated the feasibility of analyzing depressed patients with items directly or indirectly related to depression.

A deep-learned skin sensor decoding the epicentral human motions.

State monitoring of the complex system needs a large number of sensors. Especially, studies in soft electronics aim to attain complete measurement of the body, mapping various stimulations like temperature, electrophysiological signals, and mechanical strains. However, conventional approach requires many sensor networks that cover the entire curvilinear surfaces of the target area. We introduce a new measuring system, a novel electronic skin integrated with a deep neural network that captures dynamic motions from a distance without creating a sensor network. The device detects minute deformations from the unique laser-induced crack structures. A single skin sensor decodes the complex motion of five finger motions in real-time, and the rapid situation learning (RSL) ensures stable operation regardless of its position on the wrist. The sensor is also capable of extracting gait motions from pelvis. This technology is expected to provide a turning point in health-monitoring, motion tracking, and soft robotics.

Clinicopathologic analysis of intraductal papillary neoplasm of bile duct: Korean multicenter cohort study.

BACKGROUND: IPNB is very rare disease and most previous studies on IPNB were case series with a small number due to low incidence. The aim of this study is to validate previously known clinicopathologic features of intraductal papillary neoplasm of bile duct (IPNB) based on the first largest multicenter cohort. METHODS: Among 587 patients previously diagnosed with IPNB and similar diseases from each center in Korea, 387 were included in this study after central pathologic review. We also reviewed all preoperative image data. RESULTS: Of 387 patients, 176 (45.5%) had invasive carcinoma and 21 (6.0%) lymph node metastasis. The 5-year overall survival was 80.9% for all patients, 88.8% for IPNB with mucosal dysplasia, and 70.5% for IPNB with invasive carcinoma. According to the "Jang & Kim's modified anatomical classification," 265 (68.5%) were intrahepatic, 103 (26.6%) extrahepatic, and 16 (4.1%) diffuse type. Multivariate analysis revealed that tumor invasiveness was a unique predictor for survival analysis. (p = 0.047 [hazard ratio = 2.116, 95% confidence interval 1.010-4.433]). CONCLUSIONS: This is the first Korean multicenter study on IPNB through central pathologic and radiologic review process. Although IPNB showed good long-term prognosis, relatively aggressive features were also found in invasive carcinoma and extrahepatic/diffuse type.

Pancreatitis, panniculitis, and polyarthritis syndrome caused by pancreatic pseudocyst: A case report.

BACKGROUND: Panniculitis, polyarthritis, and pancreatitis (PPP) syndrome is a triad comprising an extremely rare extra-pancreatic complication of pancreatic disease. Herein, we describe a patient with PPP syndrome caused by fistula formation between the inferior vena cava (IVC) and pancreatic pseudocyst. CASE SUMMARY: A 64-year-old man visited the hospital with bilateral leg pain that began one week prior. He had no specific diseases, except hypertension. His vital signs were normal. Blood test revealed the following findings: White blood cell count, 28690/µL; amylase level, 9055 U/L; lipase level, 2089 U/L; and C-reactive protein level, 12.94 mg/dL. Computed tomography of the pancreas revealed recent acute pancreatitis. Nonsteroidal anti-inflammatory drugs were administered with no improvement. After steroid administration, pain slightly improved. Skin lesions were diagnosed as panniculitis. Bone scan and knee magnetic resonance imaging revealed osteoarthritis and bone marrow infarctions. Surgical treatment was considered; total pancreatectomy with splenectomy was performed. A pseudocyst was present posterior to the head of the pancreas, forming a fistula with the suprarenal IVC. After surgery, amylase and lipase levels decreased. However, the patient died of an uncontrolled infection on the 13th postoperative day. CONCLUSION: PPP syndrome should be suspected when accompanied by skin and joint lesions. Delays in diagnosis could have catastrophic consequences.