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arXiv 2019年

作者： Choksatchawathi, Tanut Ponglertnapakorn, Puntawat Ditthapron, Apiwat Leelaarporn, Pitshaporn Wisutthisen, Thayakorn Piriyajitakonkij, Maytus Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand Computer Department Worcester Polytechnic Institute WorcesterMA United States School of Information Technology King Mongkut’s University of Technology Thonburi Bangkok Thailand

—The technological advancement in wireless health monitoring allows the development of light-weight wrist-worn wearable devices to be equipped with different sensors. Although the equipped photoplethysmography (PPG) sensors can measure the changes in the blood volume directly through the contact with skin, the motion artifact (MA) is possible to occur during an intense exercise. In this study, we attempted to perform heart rate (HR) estimation by proposing a post-calibration approach during the three possible states of average daily activity (resting, sleeping, and intense treadmill activity states) in 29 participants (130 minutes/person) on four popular wearable devices: Fitbit Charge HR, Apple Watch Series 4, TicWatch Pro, and Empatica E4. In comparison to the HR provided by Fitbit Charge HR (HRFitbit) with the highest error of 3.26 ± 0.34 bpm in resting, 2.33 ±0.23 bpm in sleeping, 9.53 ±1.47 bpm in intense treadmill activity states, and 5.02 ± 0.64 bpm in all states combined, our improving HR estimation model with rolling windows as feature reduced the mean absolute error (MAE) for 33.44% in resting, 15.88% in sleeping, 9.55% in intense treadmill activity states, and 18.73% in all states combined. Four machine learning (ML) algorithms (support vector regression (SVR), random forest (RF), Gaussian process (GP), and artificial neural network (ANN)) were formulated and trained with the tuned hyperparameters. This demonstrates the feasibility of our proposed methods in order to correct and provide HR monitoring post-calibrated with high accuracy, raising further awareness of individual fitness in the daily application. Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

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Single channel ecg for obstructive sleep apnea severity detection using a deep learning approach

arXiv

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arXiv 2018年

作者： Banluesombatkul, Nannapas Rakthanmanon, Thanawin Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Thailand Department of Computer Engineering Kasetsart University Thailand

Obstructive sleep apnea (OSA) is a common sleep disorder caused by abnormal breathing. The severity of OSA can lead to many symptoms such as sudden cardiac death (SCD). Polysomnography (PSG) is a gold standard for OSA diagnosis. It records many signals from the patient's body for at least one whole night and calculates the Apnea-Hypopnea Index (AHI) which is the number of apnea or hypopnea incidences per hour. This value is then used to classify patients into OSA severity levels. However, it has many disadvantages and limitations. Consequently, we proposed a novel methodology of OSA severity classification using a Deep Learning approach. We focused on the classification between normal subjects (AHI 30). The 15-second raw ECG records with apnea or hypopnea events were used with a series of one-dimensional Convolutional neural Networks (1-D CNN) for automatic feature extraction, deep recurrent neural networks with Long Short- Term Memory (LSTM) for temporal information extraction, and fuy-connected neural networks (DNN) for feature encoding from a large number of features until it closed to two classes. The main advantages of our proposed method include easier data acquisition, instantaneous OSA severity detection, and effective feature extraction without domain knowledge from expertise. To evaluate our proposed method, 545 subjects of which 364 were normal and 181 were severe OSA patients obtained from the MrOS sleep study (Visit 1) database were used with the kfold cross-validation technique. The accuracy of 79.45% for OSA severity classification with sensitivity, specificity, and F-score was achieved. This is significantly higher than the results from the SVM classifier with RR Intervals and ECG derived respiration (EDR) signal feature extraction. The promising result shows that this proposed method is a good start for the detection of OSA severity from a single channel ECG which can be obtained from wearable devices at home and can also be applied to near realtime

关键词： Feature extraction

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Towards asynchronous motor imagery-based brain-computer interfaces: A joint training scheme using deep learning

arXiv

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arXiv 2018年

作者： Cheng, Patcharin Autthasan, Phairot Pijarana, Boriwat Chuangsuwanich, Ekapol Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Thailand Computer Engineering Department Chulalongkorn University Bangkok Thailand

In this paper, the deep learning (DL) approach is applied to a joint training scheme for asynchronous motor imagerybased Brain-Computer Interface (BCI). The proposed DL approach is a cascade of one-dimensional convolutional neural networks and fully-connected neural networks (CNN-FC). The focus is mainly on three types of brain responses: non-imagery EEG (background EEG), (pure imagery) EEG, and EEG during the transitional period between background EEG and pure imagery (transitional imagery). The study of transitional imagery signals should provide greater insight into real-world scenarios. It may be inferred that pure imagery and transitional EEG are high and low power EEG imagery, respectively. Moreover, the results from the CNN-FC are compared to the conventional approach for motor imagery-BCI, namely the common spatial pattern (CSP) for feature extraction and support vector machine (SVM) for classification (CSP-SVM). Under a joint training scheme, pure and transitional imagery are treated as the same class, while background EEG is another class. Ten-fold crossvalidation is used to evaluate whether the joint training scheme significantly improves the performance task of classifying pure and transitional imagery signals from background EEG. Using sparse of just a few electrode channels (Cz, C3and C4), mean accuracy reaches 71.52% and 70.27% for CNN-FC and CSPSVM, respectively. On the other hand, mean accuracy without the joint training scheme achieve only 62.68% and 52.41% for CNN-FC and CSP-SVM, respectively. Copyright © 2018, The Authors. All rights reserved.

关键词： Brain computer interface

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MetaSleepLearner: A pilot study on fast adaptation of bio-signals-based sleep stage classifier to new individual subject using Meta-Learning

arXiv

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arXiv 2020年

作者： Banluesombatkul, Nannapas Ouppaphan, Pichayoot Leelaarporn, Pitshaporn Lakhan, Payongkit Chaitusaney, Busarakum Jaimchariyatam, Nattapong Chuangsuwanich, Ekapol Chen, Wei Phan, Huy Dilokthanakul, Nat Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand Department of Otolaryngology Faculty of Medicine Chulalongkorn University Thailand Excellence Center for Sleep Disorders King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand Division of Pulmonary and Critical Care Medicine Faculty of Medicine Chulalongkorn University Thailand Head of Excellence Center for Sleep Disorders King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand The Computer Engineering Department Chulalongkorn University Bangkok Thailand The Center for Intelligent Medical Electronics Department of Electronic Engineering School of Information Science and Technology Fudan University Shanghai200433 China The School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

Identifying bio-signals based-sleep stages requires time-consuming and tedious lab.r of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification conundrum. However, the difficulties can be posed in replacing the clinicians with the automatic system due to the differences in many aspects found in individual bio-signals, causing the inconsistency in the performance of the model on every incoming individual. Thus, we aim to explore the feasibility of using a novel approach, capable of assisting the clinicians and lessening the workload. We propose the transfer learning framework, entitled MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subjects (source code is availab.e at https://***/IoBTVISTEC/MetaSleepLearner). The framework was demonstrated to require the lab.lling of only a few sleep epochs by the clinicians and allow the remainder to be handled by the system. Layer-wise Relevance Propagation (LRP) was also applied to understand the learning course of our approach. In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 5.4% to 17.7% improvement with statistical difference in the mean of both approaches. The illustration of the model interpretation after the adaptation to each subject also confirmed that the performance was directed towards reasonable learning. MetaSleepLearner outperformed the conventional approaches as a result from the fine-tuning using the recordings of both healthy subjects and patients. This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collab.ration in sleep stage classification and easing the burden of the clinicians in lab.lling the sleep stages through only several epochs rather than an entire recording. © 2020, CC BY-N

关键词： Convolutional neural networks

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Deep neural networks with weighted averaged overnight airflow features for sleep apnea-hypopnea severity classification

arXiv

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arXiv 2018年

作者： Lakhan, Payongkit Ditthapron, Apiwat Banluesombatkul, Nannapas Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Thailand Computer Department Worcester Polytechnic Institute WorcesterMA United States

Dramatic raising of Deep Learning (DL) approach and its capability in biomedical applications lead us to explore the advantages of using DL for sleep Apnea-Hypopnea severity classification. To reduce the complexity of clinical diagnosis using Polysomnography (PSG), which is multiple sensing platform, we incorporates our proposed DL scheme into one single Airflow (AF) sensing signal (subset of PSG). Seventeen features have been extracted from AF and then fed into Deep neural Networks to classify in two studies. First, we proposed a binary classifications which use the cutoff indices at AHI = 5, 15 and 30 events/hour. Second, the multiple Sleep Apnea-Hypopnea Syndrome (SAHS) severity classification was proposed to classify patients into 4 groups including no SAHS, mild SAHS, moderate SAHS, and severe SAHS. For methods evaluation, we used a higher number of patients than related works to accommodate more diversity which includes 520 AF records obtained from the MrOS sleep study (Visit 2) database. We then applied the 10-fold cross-validation technique to get the accuracy, sensitivity and specificity. Moreover, we compared the results from our main classifier with other two approaches which were used in previous researches including the Support Vector Machine (SVM) and the Adaboost-Classification and Regression Trees (AB-CART). From the binary classification, our proposed method provides significantly higher performance than other two approaches with the accuracy of 83.46%, 85.39% and 92.69% in each cutoff, respectively. For the multiclass classification, it also returns a highest accuracy of all approaches with 63.70%. Copyright © 2018, The Authors. All rights reserved.

关键词： Classification (of information)

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Universal joint feature extraction for P300 EEG classification using multi-task autoencoder

arXiv

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arXiv 2018年

作者： Ditthapron, Apiwat Banluesombatkul, Nannapas Ketrat, Sombat Chuangsuwanich, Ekapol Wilaiprasitporn, Theerawit Computer Department Worcester Polytechnic Institute WorcesterMA United States Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand Computer Engineering Department Chulalongkorn University Bangkok Thailand

The process of recording Electroencephalography (EEG) signals is onerous and requires massive storage to store signals at an applicable frequency rate. In this work, we propose the Event-Related Potential Encoder Network (ERPENet);a multi-task autoencoder-based model, that can be applied to any ERP-related tasks. The strength of ERPENet lies in its capability to handle various kinds of ERP datasets and its robustness across multiple recording setups, enabling joint training across datasets. ERPENet incorporates Convolutional neural Networks (CNNs) and Long Short-Term Memory (LSTM), in an autoencoder setup, which tries to simultaneously compress the input EEG signal and extract related P300 features into a latent vector. Here, we can infer the process for generating the latent vector as universal joint feature extraction. The network also includes a classification part for attended and unattended events classification as an auxiliary task. We experimented on six different P300 datasets. The results show that the latent vector exhibits better compression capability than the previous state-of-the-art semi-supervised autoencoder model. For attended and unattended events classification, pre-trained weights are adopted as initial weights and tested on unseen P300 datasets to evaluate the adaptability of the model, which shortens the training process as compared to using random Xavier weight initialization. At the compression rate of 6.84, the classification accuracy outperforms conventional P300 classification models: XdawnLDA, DeepConvNet, and EEGNet achieving 79.37% - 88.52% classification accuracy depending on the dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Electroencephalography

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Editorial: neural computation in embodied closed-loop systems for the generation of complex behavior: From biology to technology

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Frontiers in Neurorobotics 2018年第AUG期12卷 53页

作者： Manoonpong, Poramate Tetzlaff, Christian Embodied AI and Neurorobotics Lab Centre for BioRobotics Mrsk Mc-Kinney Mller Institute University of Southern Denmark Odense Denmark Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong Thailand Institute of Bio-Inspired Structure and Surface Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Bernstein Center for Computational Neuroscience Third Institute of Physics Georg-August-Universität Göttingen Göttingen Germany

[...]actions and cognition require dynamical brain-body-environment interactions and thereby cannot be disembodied. According to this, this Research Topic called researchers from different fields (including biology, Computational Neuroscience, robotics, and Artificial Intelligence) to share their recent developments and results and to update our research community with remaining open questions. The higher integrative areas have multiple complex functions with multiple-time scales plasticity (i.e., long-term and short-term synaptic plasticity) for motor-skill learning, navigation learning, motor planning, and internal representations. Regarding interactions in embodied closed-loop systems for adaptive behavior generation, in the Research Topic, Aoi et al. review various adaptive locomotor behaviors that emerge from the interactions between the body dynamics, the nervous system, and the environment in animals and legged robots.

关键词： autonomous robot embodiment learning and memory motor planning/control neural computation neuromorphic computing orthosis and prosthesis sensorimotor coordination

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Author Correction: Rules for the Leg Coordination of Dung Beetle Ball Rolling Behaviour

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Scientific reports 2020年第1期10卷 18182页

作者： Binggwong Leung Nienke Bijma Emily Baird Marie Dacke Stanislav Gorb Poramate Manoonpong Bio-Inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong 21210 Thailand. binggwong.l_s17@vistec.ac.th. Functional Morphology and Biomechanics Zoological Institute Kiel University Kiel 24118 Germany. Division of Functional Morphology Department of Zoology Stockholm University Stockholm SE 10691 Sweden. Department of Biology Lund Vision Group Lund University Sölvegatan 35 223 62 Lund Sweden. Bio-Inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong 21210 Thailand. poramate.m@vistec.ac.th. Embodied AI and Neurorobotics Lab SDU Biorobotics The Mærsk Mc-Kinney Møller Institute The University of Southern Denmark Odense 5230 Denmark. poramate.m@vistec.ac.th.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Affective EEG-Based Person Identification Using the Deep Learning Approach

arXiv

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arXiv 2018年

作者： Wilaiprasitporn, Theerawit Ditthapron, Apiwat Matchaparn, Karis Tongbuasirilai, Tanaboon Banluesombatkul, Nannapas Chuangsuwanich, Ekapol Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Engineering Rayong Thailand Computer Department Worcester Polytechnic Institute WorcesterMA United States Computer Engineering Department King Mongkut’s University of Technology Thonburi Bangkok Thailand Department of Science and Technology Linköping University Sweden Computer Engineering Department Chulalongkorn University Bangkok Thailand

Electroencephalography (EEG) is another mode forperforming Person Identification (PI). Due to the nature of the EEG signals, EEG-based PI is typically done while the person is performing some kind of mental task, such as motor control. However, few works have considered EEG-based PI while the person is in different mental states (affective EEG). The aim of this paper is to improve the performance of affective EEG-based PI using a deep learning approach. We proposed a cascade of deep learning using a combination of Convolutional neural Networks (CNNs) and Recurrent neural Networks (RNNs). CNNs are used to handle the spatial information from the EEG while RNNs extract the temporal information. We evaluated two types of RNNs, namely, Long Short-Term Memory (CNN-LSTM) and Gated Recurrent Unit (CNN-GRU). The proposed method is evaluated on the state-of-the-art affective dataset DEAP. The results indicate that CNN-GRU and CNN-LSTM can perform PI from different affective states and reach up to 99.90–100% mean Correct Recognition Rate (CRR), significantly outperforming a support vector machine (SVM) baseline system that uses power spectral density (PSD) features. Notably, the 100% mean CRR comes from only 40 subjects in DEAP dataset. To reduce the number of EEG electrodes from thirty-two to five for more practical applications, the frontal region gives the best results reaching up to 99.17% CRR (from CNN-GRU). Amongst the two deep learning models, we find CNN-GRU to slightly outperform CNN-LSTM, while having faster training time. Furthermore, CNN-GRU overcomes the influence of affective states in EEG-Based PI reported in the previous works. Copyright © 2018, The Authors. All rights reserved.

关键词： Electroencephalography

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Consumer grade brain sensing for emotion recognition

arXiv

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arXiv 2018年

作者： Lakhan, Payongkit Banluesombatkul, Nannapas Changniam, Vongsagon Dhithijaiyratn, Ratwade Leelaarporn, Pitshaporn Boonchieng, Ekkarat Hompoonsup, Supanida Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand Department of Tool and Materials Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand Department of Electrical Engineering Chulalongkorn University Bangkok Thailand Center of Excellence in Community Health Informatics Chiang Mai University Chiang Mai Thailand Learning Institute King Mongkut’s University of Technology Thonburi Bangkok Thailand

For several decades, electroencephalography (EEG) has featured as one of the most commonly used tools in emotional state recognition via monitoring of distinctive brain activities. An array of datasets have been generated with the use of diverse emotion-eliciting stimuli and the resulting brainwave responses conventionally captured with high-end EEG devices. However, the applicability of these devices is to some extent limited by practical constraints and may prove difficult to be deployed in highly mobile context omnipresent in everyday happenings. In this study, we evaluate the potential of OpenBCI to bridge this gap by first comparing its performance to research grade EEG system, employing the same algorithms that were applied on benchmark datasets. Moreover, for the purpose of emotion classification, we propose a novel method to facilitate the selection of audio-visual stimuli of high/low valence and arousal. Our setup entailed recruiting 200 healthy volunteers of varying years of age to identify the top 60 affective video clips from a total of 120 candidates through standardized self assessment, genre tags, and unsupervised machine learning. Additional 43 participants were enrolled to watch the pre-selected clips during which emotional EEG brainwaves and peripheral physiological signals were collected. These recordings were analyzed and extracted features fed into a classification model to predict whether the elicited signals were associated with a high or low level of valence and arousal. As it turned out, our prediction accuracies were decidedly comparable to those of previous studies that utilized more costly EEG amplifiers for data acquisition. Copyright © 2018, The Authors. All rights reserved.

关键词： Electrophysiology

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