Feature selection for EEG Based biometrics

Department of Human Factors EngineeringEEG-based biometrics identify individuals by using personal and distinctive information in human brain. This thesis aims to evaluate the electroencephalography (EEG) features and channels for biometrics and to propose methodology that identifies individuals. In my research, I recorded fourteen EEG channel signals from thirty subjects. While record EEG signal, subjects were asked to relax and keep eyes closed for 2 minutes. In addition, to evaluate intra-individual variability, we recorded EEG ten times for each subject, and every recording conducted on different days to reduce within-day effects. After acquisition of data, for each channel, I calculated eight features: alpha/beta power ratio, alpha/theta power ratio, beta/theta power ratio, median frequency, PSD entropy, permutation entropy, sample entropy, and maximum Lyapunov exponents. Then, I scored 112 features with three feature selection algorithms: Fisher score, reliefF, and information gain. Finally, I classified EEG data using a linear discriminant analysis (LDA) with a leave-one-out cross validation method. As a result, the best feature set was composed of 23 features that highly ranked on Fisher score and yielded a 18.56% half total error rate. In addition, according to scores calculated by feature selection, EEG channels located on occipital and right temporal areas most contributed to identify individuals. Thus, with suggested methodologies and channels, implementation of efficient EEG-based biometrics is possible.ope

Reinforcement Learning Based Cooperative P2P Energy Trading between DC Nanogrid Clusters with Wind and PV Energy Resources

In order to replace fossil fuels with the use of renewable energy resources, unbalanced resource production of intermittent wind and photovoltaic (PV) power is a critical issue for peer-to-peer (P2P) power trading. To resolve this problem, a reinforcement learning (RL) technique is introduced in this paper. For RL, graph convolutional network (GCN) and bi-directional long short-term memory (Bi-LSTM) network are jointly applied to P2P power trading between nanogrid clusters based on cooperative game theory. The flexible and reliable DC nanogrid is suitable to integrate renewable energy for distribution system. Each local nanogrid cluster takes the position of prosumer, focusing on power production and consumption simultaneously. For the power management of nanogrid clusters, multi-objective optimization is applied to each local nanogrid cluster with the Internet of Things (IoT) technology. Charging/discharging of electric vehicle (EV) is performed considering the intermittent characteristics of wind and PV power production. RL algorithms, such as deep Q-learning network (DQN), deep recurrent Q-learning network (DRQN), Bi-DRQN, proximal policy optimization (PPO), GCN-DQN, GCN-DRQN, GCN-Bi-DRQN, and GCN-PPO, are used for simulations. Consequently, the cooperative P2P power trading system maximizes the profit utilizing the time of use (ToU) tariff-based electricity cost and system marginal price (SMP), and minimizes the amount of grid power consumption. Power management of nanogrid clusters with P2P power trading is simulated on the distribution test feeder in real-time and proposed GCN-PPO technique reduces the electricity cost of nanogrid clusters by 36.7%.Comment: 22 pages, 8 figures, to be submitted to Applied Energy of Elsevie

Facile Method to Prepare for the Ni2P Nanostructures with Controlled Crystallinity and Morphology as Anode Materials of Lithium-Ion Batteries

Conversion reaction materials (transition metal oxides, sulfides, phosphides, etc.) are attractive in the field of lithium-ion batteries because of their high theoretical capacity and low cost. However, the realization of these materials in lithium-ion batteries is impeded by large voltage hysteresis, high polarization, inferior cycle stability, rate capability, irreversible capacity loss in first cycling, and dramatic volume change during redox reactions. One method to overcome these problems is the introduction of amorphous materials. This work introduces a facile method to synthesize amorphous and crystalline dinickel phosphide (Ni2P) nanoparticle clusters with identical morphology and presents a direct comparison of the two materials as anode materials for rechargeable lithium-ion batteries. To assess the effect of crystallinity and hierarchical structure of nanomaterials, it is crucial to conserve other factors including size, morphology, and ligand of nanoparticles. Although it is rarely studied about synthetic methods of well-controlled Ni2P nanomaterials to meet the above criteria, we synthesized amorphous, crystalline Ni2P, and self-assembled Ni2P nanoparticle clusters via thermal decomposition of nickel-surfactant complex. Interestingly, simple modulation of the quantity of nickel acetylacetonate produced amorphous, crystalline, and self-assembled Ni2P nanoparticles. A 0.357 M nickel-trioctylphosphine (TOP) solution leads to a reaction temperature limitation (similar to 315 degrees C) by the nickel precursor, and crystalline Ni2P (c-Ni2P) nanoparticles clusters are generated. On the contrary, a lower concentration (0.1 M) does not accompany a temperature limitation and hence high reaction temperature (330 degrees C) can be exploited for the self-assembly of Ni2P (s-Ni2P) nanoparticle clusters. Amorphous Ni2P (a-Ni2P) nanoparticle clusters are generated with a high concentration (0.714 M) of nickel-TOP solution and a temperature limitation (similar to 290 degrees C). The a-Ni2P nanoparticle cluster electrode exhibits higher capacities and Coulombic efficiency than the electrode based on c-Ni2P nanoparticle clusters. In addition, the amorphous structure of Ni2P can reduce irreversible capacity and voltage hysteresis upon cycling. The amorphous morphology of Ni2P also improves the rate capability, resulting in superior performance to those of c-Ni2P nanoparticle clusters in terms of electrode performance

Syndecan transmembrane domain specifically regulates downstream signaling events of the transmembrane receptor cytoplasmic domain

Despite the known importance of the transmembrane domain (TMD) of syndecan receptors in cell adhesion and signaling, the molecular basis for syndecan TMD function remains un-known. Using in vivo invertebrate models, we found that mammalian syndecan-2 rescued both the guidance defects in C. elegans hermaphrodite-specific neurons and the impaired development of the midline axons of Drosophila caused by the loss of endogenous syndecan. These compensatory ef-fects, however, were reduced significantly when syndecan-2 dimerization-defective TMD mutants were introduced. To further investigate the role of the TMD, we generated a chimera, 2eTPC, com-prising the TMD of syndecan-2 linked to the cytoplasmic domain of platelet-derived growth factor receptor (PDGFR). This chimera exhibited SDS-resistant dimer formation that was lost in the corre-sponding dimerization-defective syndecan-2 TMD mutant, 2eT(GL)PC. Moreover, 2eTPC specifically enhanced Tyr 579 and Tyr 857 phosphorylation in the PDGFR cytoplasmic domain, while the TMD mutant failed to support such phosphorylation. Finally, 2eTPC, but not 2eT(GL)PC, induced phosphorylation of Src and PI3 kinase (known downstream effectors of Tyr 579 phosphorylation) and promoted Src-mediated migration of NIH3T3 cells. Taken together, these data suggest that the TMD of a syndecan-2 specifically regulates receptor cytoplasmic domain function and subsequent downstream signaling events controlling cell behavior. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.1

Targeting metastatic breast cancer with peptide epitopes derived from autocatalytic loop of Prss14/ST14 membrane serine protease and with monoclonal antibodies

Background In order to develop a new immunotherapeutic agent targeting metastatic breast cancers, we chose to utilize autocatalytic feature of the membrane serine protease Prss14/ST14, a specific prognosis marker for ER negative breast cancer as a target molecule. Methods The study was conducted using three mouse breast cancer models, 4 T1 and E0771 mouse breast cancer cells into their syngeneic hosts, and an MMTV-PyMT transgenic mouse strain was used. Prss14/ST14 knockdown cells were used to test function in tumor growth and metastasis, peptides derived from the autocatalytic loop for activation were tested as preventive metastasis vaccine, and monoclonal and humanized antibodies to the same epitope were tested as new therapeutic candidates. ELISA, immunoprecipitation, Immunofluorescent staining, and flow cytometry were used to examine antigen binding. The functions of antibodies were tested in vitro for cell migration and in vivo for tumor growth and metastasis. Results Prss14/ST14 is critically involved in the metastasis of breast cancer and poor survival rather than primary tumor growth in two mouse models. The epitopes derived from the specific autocatalytic loop region of Prss14/ST14, based on structural modeling acted as efficient preventive metastasis vaccines in mice. A new specific monoclonal antibody mAb3F3 generated against the engineered loop structure could reduce cell migration, eliminate metastasis in PyMT mice, and can detect the Prss14/ST14 protein expressed in various human cancer cells. Humanized antibody huAb3F3 maintained the specificity and reduced the migration of human breast cancer cells in vitro. Conclusion Our study demonstrates that Prss14/ST14 is an important target for modulating metastasis. Our newly developed hybridoma mAbs and humanized antibody can be further developed as new promising candidates for the use in diagnosis and in immunotherapy of human metastatic breast cancer.This work is supported in part by the National Research Foundation (NRF) grant funded by the Korea government (MEST) (No. 2013R1A1A2009892 and No. 2017R1A2B4008109) and Inha Univeristy Research Grant awarded to MGK and (No. 2015R1A2A1A15054021) to SHK

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

LSTM-CNN model of drowsiness detection from multiple consciousness states acquired by EEG

This study aimed to design a deep neural network for electroencephalography (EEG)-based drowsiness detection in multiple consciousness states, i.e., “awake,” “sleep,” and “drowsiness.” Few studies have seriously considered the optimal input vector size or labeling method in classifying multiple consciousness states, which may affect classification performance. To determine the optimal input vector length, i.e., window length, three neural network models (long short-term memory [LSTM], convolutional neural network [CNN], and combined LSTM and CNN) and four feature-based models were tested with six different levels of window length. The EEG dataset was acquired from 19 participants with randomly assigned auditory stimuli and button responses. The EEG data were labeled into three classes (awake, sleep, and drowsiness) based on the defined button response pattern corresponding to the stimuli. The results demonstrated that when the input vector size exceeded 8 sec, the performance of the neural network models dropped rapidly; however, when the window size was less than 8 sec, the performance change according to the window size was small. In contrast, the performance of feature-based models increased continuously as the window size increased. The LSTM model yielded the best accuracy (86%) for a 1 sec window length, and the LSTM-CNN model yielded the best kappa index (0.77) for a 4 sec window length. In addition, the proposed model was applied to the binary classification of normal consciousness (awake) and low consciousness (drowsiness and sleep) states to determine whether this model works appropriately in actual applications such as drowsiness detection in a driving environment. For binary classification, the LSTM-CNN model resulted in 0.95 F1 scores in 4000-ms. When a short input data (500 msec) is used, the LSTM-CNN model resulted in an average accuracy of 85.6% and a kappa index of 0.77 for the three-class classification problem and 0.94 F1 scores for the binary classification problem. In conclusion, we demonstrated that the proposed model could effectively detect drowsiness. Furthermore, a significant correlation was found between reaction time and drowsiness. However, using the reaction time as an index for labeling drowsiness was challenging because of the high false-negative ratio. © 2022 The Author(s)TRU

Feature selection using mutual information for EEG-based biometrics

Recently, electroencephalography (EEG) has emerged as a novel means to identify an individual for biometric authentication. Successful application of EEG to biometrics relies on how well the signal features of EEG represent individual identities. In this study, we propose a new approach to the selection of an optimal EEG feature set, using a mutual information technique. The EEG data were recorded with 21 dry electrodes from 7 subjects while they rested with eyes closed for 2 minutes. Seven features (alpha/theta, alpha/beta, theta/beta power ratio, sample entropy, permutation entropy, entropy, and median values of distribution) were calculated for each EEG channel, and mutual information between each pair of features was calculated for each subject. Then we selected the optimal features that exhibited the largest intra-subject mutual information. Using the selected features, we performed an authentication test by means of a Bhattacharyya distance-based nearest-neighbor method with leave-one-out cross-validation. As a result, with best nine features we achieved a 95% accuracy rate. Our results suggest a feasibility of using a mutual-information-based feature selection scheme for EEG-based biometrics

Anomaly detection of smart metering system for power management with battery storage system/electric vehicle

A novel smart metering technique capable of anomaly detection was proposed for real-time home power management system. Smart meter data generated in real-time were obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short-term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was executed by multi-objective optimization in the presence of a battery storage system and an electric vehicle. The results of the power management employing the proposed smart metering technique indicate a reduction in electricity cost and amount of power supplied by the grid compared to the results of power management without anomaly detection

ECG based User Authentication for Wearable Devices using Short Time Fourier Transform

Electrocardiogram (ECG) is a promising biometric. There has been much research on ECG based user authentication and identification, but there have been few works to investigate ECG biometrics for stand-alone wearable ECG sensors, for quick response time using a single pulse ECG, and for small wearable devices that may have limited access to others??? ECG information. Recently, ECG user authentication method using spectrogram yielded excellent detection performance. However, spectrogram only utilizes magnitude of short time Fourier transform (STFT) and phase information was overlooked for ECG features. In this paper, we address the issues of wearable ECG sensors, quick response time, and limited access to others??? ECG information using a new STFT based method that uses phase information. Our proposed method yielded 0.9% EER for ECG data set from wearable ECG sensors (15 subjects) and 2.2% EER (equal error rate) for public ECG-ID database (89 subjects)