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.

Influencing factors on preventive health behaviours for Zika virus in pregnant women and their partners.

AIMS AND OBJECTIVES: To identify Zika virus-related knowledge, attitudes to Zika virus and health motivations of pregnant women, women preparing for pregnancy and their partners, and to identify factors that influence preventive health behaviours for Zika virus infection. BACKGROUND: Prevention of Zika virus infection is becoming a major worldwide public health effort. The high-risk group for Zika virus infection comprises women who are pregnant or preparing for pregnancy. Few studies have addressed preventive health behaviours for Zika virus infection by gender. DESIGN: This study was a descriptive cross-sectional survey. METHODS: Participants (112 males and 147 females) were selected by convenience sampling from three obstetric clinics located in South Korea. Women and their partners who visited obstetric clinics during pregnancy or preparing for pregnancy were included as subjects. RESULTS: Factors associated with preventive health behaviours for Zika virus were checking infection status of Zika virus on destinations when planning a trip and attitudes to Zika virus in males and age, educational level, intention to travel to Zika virus endemic countries, attitudes to Zika virus and health motivation in females. CONCLUSIONS: Factors that influence preventive health behaviours for Zika virus were different by gender. However, attitudes to Zika virus were influential to both genders. Gender differences should be considered in development of health policies to improve preventive health behaviours for Zika virus. Younger and less educated women are to be targeted, and health management programmes should be developed to promote health motivation. RELEVANCE TO CLINICAL PRACTICE: Pregnant women, women preparing for pregnancy and their partners need education and public health promotion to improve self-management during travel to places where Zika virus is endemic. Health policies and health management programmes considering age and educational background should be developed to promote health motivation in women of childbearing age.

The relationship of subepidermal moisture and early stage pressure injury by visual skin assessment.

AIM: The purpose of this study was to examine the relationship of subepidermal moisture and early stage pressure injury by visual skin assessment in elderly Korean. METHODS: Twenty-nine elderly participated at a particular nursing home. Data were collected for 12 weeks by one wound care nurse. Visual skin assessment and subepidermal moisture value were measured at both buttocks, both ischia, both trochanters, sacrum, and coccyx of each subject once a week. RESULTS: Subepidermal moisture value of stage 1 pressure injury was significantly higher than that of no injury and blanching erythema. After adjustment with covariates, odds ratios of blanching erythema to normal skin and stage 1 pressure injury to blanching erythema/normal skin were statistically significant (p < 0.05). Odds ratio of blanching erythema to normal skin was 1.003 (p = .047) by 1-week prior subepidermal moisture value, and that of concurrent subepidermal moisture value was 1.004 (p = .011). Odds ratio of stage 1 pressure injury to normal skin/blanching erythema was 1.003 (p = .005) by 1-week prior subepidermal moisture value, and that for concurrent subepidermal moisture value was 1.007 (p = .030). Subepidermal moisture was associated with concurrent and future (1 week later) skin damage at both trochanters. CONCLUSION: Subepidermal moisture would be used to predict early skin damage in clinical nursing field for the effective pressure injury prevention.

Surgical strategy for T1 gallbladder cancer: a nationwide multicenter survey in South Korea.

BACKGROUND: The aim of this study was to investigate the clinical features and clinical outcomes of T1 gallbladder (GB) cancer and to determine an appropriate surgical strategy for T1 GB cancer. METHODS: A nationwide multicenter study, in which 16 University Hospitals in Korea participated, was performed from 1995 to 2004. A total of 258 patients, 117 patients with T1a and 141 patients with T1b disease were enrolled. Clinicopathologic findings and long-term follow-up results were analyzed after a consensus meeting of the Korean Pancreas Surgery Club was held. RESULTS: Simple cholecystectomy was performed in 95 patients (81.2 %) with T1a tumor and in 89 patients (63.1 %) with T1b tumor (p < 0.01). Lymph node metastasis was observed in 2.9 % of T1a patients and in 9.9 % of T1b patients (p = 0.391). A significant difference in 5-year disease-specific survival (DSS) rates was observed between T1a and T1b patients (96.4 vs 84.8 %, respectively, p = 0.03). However, no significant 5-year DSS rate difference was observed between those who underwent simple cholecystectomy or extended cholecystectomy, regardless of whether lymph node dissection was performed or whether lymph node metastasis was present. There was no significant difference in recurrence-free survival between simple cholecystectomy and extended cholecystectomy. CONCLUSIONS: There was no superiority of extended cholecystectomy over simple cholecystectomy in the aspect of survival and recurrence especially in T1b gallbladder cancer. Furthermore, the effectiveness of regional lymphadenectomy for treatment purpose remains questionable. Therefore, simple cholecystectomy could be recommended as a surgical strategy of T1 gallbladder cancer.

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.

Awareness and practice of wound disinfection among wound care nurses in Korea.

PURPOSE: The aim of this study was to assess the awareness and practice of wound disinfection among Korean wound care nurses. SUBJECTS AND SETTING: The target sample comprised 100 members of the Korea Wound Ostomy Continence Nurse Association. Seventy-eight wound care nurses responded to the questionnaire, yielding a response rate of 78%. METHODS: Potential respondents were contacted via e-mail and asked to complete a questionnaire designed for this study. RESULTS: Pressure ulcers were the most common wound type managed by WOC nurses, followed by surgical wounds. The mean score of items measuring awareness of wound disinfection practice and reported wound disinfection practice were 3.38 on a scale of 0 to 4 scale, and 3.14 on a scale of 0 to 4, respectively. Scores among items that queried awareness and practice in various methods for disinfecting a chronic wound disinfection were highest (3.74 and 3.67 out of 4, respectively) and scores of items querying disinfection with sodium hypochlorite were lowest (2.80 and 2.05 out of 4, respectively). There were significant differences between all items of awareness and reported practice except for disinfection with alcohol. Awareness of wound disinfection significantly correlated with reported practice. CONCLUSIONS: Developing an effective educational program to enhance awareness is necessary to improve practice of wound disinfection.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Antimicrobial activity of a halocidin-derived peptide resistant to attacks by proteases.

Cationic antimicrobial peptides (AMPs) have attracted a great deal of interest as a promising candidate for a novel class of antibiotics that might effectively treat recalcitrant infections caused by a variety of microbes that are resistant to currently available drugs. However, the AMPs are inherently limited in that they are inevitably susceptible to attacks by proteases generated by human and pathogenic microbes; this vulnerability severely hinders their pharmaceutical use in human therapeutic protocols. In this study, we report that a halocidin-derived AMP, designated HG1, was found to be resistant to proteolytic degradation. As a result of its unique structural features, HG1 proved capable of preserving its antimicrobial activity after incubation with trypsin, chymotrypsin, and human matrix metalloprotease 7 (MMP-7). Additionally, HG1 was observed to exhibit profound antimicrobial activity in the presence of fluid from human skin wounds or proteins extracted from the culture supernatants of Staphylococcus aureus and Pseudomonas aeruginosa. Greater understanding of the structural motifs of HG1 required for its protease resistance might provide feasible ways to solve the problems intrinsic to the development of an AMP-based antibiotic.

Factors affecting ostomy-related complications in Korea.

PURPOSE: We sought to determine the type and incidence of ostomy-related complications and identify associated factors in Korean ostomy patients. METHODS: A retrospective analysis of medical records of 1,170 patients who underwent end colostomy in Samsung Medical Center between October 1994 and February 2005 was completed. Complications were classified as stomal or peristomal. Stomal complications included bleeding, necrosis, mucocutaneous separation, prolapsed stoma, retracted stoma, and stenosis. Peristomal complications included varices, hernia, irritant contact dermatitis, allergic contact dermatitis, maceration, folliculitis, hyperplastic granulation, bacterial infection, candidal infection, malignancy in the peristomal area, mechanical damage, and pyoderma gangrenosum. A flat (flush) stoma was the most common stomal complication, occurring in 8.5% of subjects. Irritant contact dermatitis, occurring in 15.5%, was the most common peristomal complication. Gender and body mass index were associated with irritant contact dermatitis, hyperplasia, peristomal hernias, flat stomas, and retracted stomas. CONCLUSIONS: Education for preventing irritant contact dermatitis, such as proper pouching and peristomal skin protection, and for weight control, should be emphasized in a self-care program for persons living with an ostomy. Preoperative marking by a WOC nurse is needed to reduce the prevalence of flat (flush) stomas.

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.