Theerawit Wilaiprasitporn

Vidyasirimedhi Institute of Science and Technology · School of Information Science and Technology

Identifying bio-signals based-sleep stages requires time-consuming and tedious labor 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 man...

The detection of seizures in epileptic patients via Electroencephalography (EEG) is an essential key to medical treatment. With the advances in deep learning, many approaches are proposed to tackle this problem. However, concerns such as performance, speed, and subject-independency should still be considered for practical application. Thus, we prop...

Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. Despite significant advances in MI-based BCI, EEG rhythms are specific to a subject and vario...

Respiratory rate (RR) is an important biomarker as RR changes can reflect severe medical events such as heart disease, lung disease, and sleep disorders. Unfortunately, standard manual RR counting is prone to human error and cannot be performed continuously. This study proposes a method for continuously estimating RR, RRWaveNet. The method is a com...

The elimination of ocular artifacts is critical in analyzing electroencephalography (EEG) data for various brain-computer interface (BCI) applications. Despite numerous promising solutions, electrooculography (EOG) recording or an eye-blink detection algorithm is required for the majority of artifact removal algorithms. This reliance can hinder the...

In light of the COVID-19 pandemic, patients were required to manually input their daily oxygen saturation (SpO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) and pulse rate (PR) values into a health monitoring system—unfortunately, such a process trend to be an error in typing. Several studi...

Patch classification models based on deep learning have been utilized in whole-slide images (WSI) of H&E-stained tissue samples to assist pathologists in grading follicular lymphoma patients. However, these approaches still require pathologists to manually identify centroblast cells and provide refined labels for optimal performance. To address thi...

In this paper, we utilized obstructive sleep apnea and cardiovascular disease-related photoplethysmography (PPG) features in constructing the input to deep learning (DL). The features are pulse wave amplitude (PWA), beat-to-beat or RR interval, a derivative of PWA, a derivative of RR interval, systolic phase duration, diastolic phase duration, and...

Kratom (KT) typically exerts antidepressant (AD) effects. However, evaluating which form of KT extracts possesses AD properties similar to the standard AD fluoxetine (flu) remained challenging. Here, we adopted an autoencoder (AE)-based anomaly detector called ANet to measure the similarity of mice's local field potential (LFP) features that resp...

The existing deep convolutional neural network (DCNN) models used for hand gesture recognition based on surface electromyography (sEMG) require high computational costs. Moreover, there is a lack of a comprehensive DCNN model that can handle both high-definition sEMG and low-definition sEMG in a subject-independent manner. To address these issues,...

Objective: While the microvasculature annotation within Optical Coherence Tomography Angiography (OCTA) can be leveraged using deep-learning techniques, expensive annotation processes are required to create sufficient training data. One way to avoid the expensive annotation is to use a type of weak annotation in which only the center of the vessel...

Detection of mild cognitive impairment (MCI) and dementia (DEM) is an important topic because, unless it is treated early, MCI can progress to DEM, which is an untreatable disease. This paper proposes a timed-up-and-go (TUG) task features analysis and classification of MCI and DEM using inertial measurement units (IMU) in wearable devices. Our goal...

In light of the COVID-19 pandemic, patients were required to manually input their daily oxygen saturation (SpO2) and pulse rate (PR) values into a health monitoring system-unfortunately, such a process trend to be an error in typing. Several studies attempted to detect the physiological value from the captured image using optical character recognit...

Kratom (KT) typically exerts antidepressant (AD) effects. However, evaluating which form of KT extracts possesses AD properties similar to the standard AD fluoxetine (flu) remained challenging. Here, we adopted an autoencoder (AE)-based anomaly detector called ANet to measure the similarity of mice's local field potential (LFP) features that respon...

Respiratory rate (RR) is an important biomarker as RR changes can reflect severe medical events such as heart disease, lung disease, and sleep disorders. Unfortunately, however, standard manual RR counting is prone to human error and cannot be performed continuously. This study proposes a method for continuously estimating RR, RRWaveNet. The method...

Brain biometrics based on electroencephalography (EEG) have been used increasingly for personal identification. Traditional machine learning techniques as well as modern day deep learning methods have been applied with promising results. In this paper we present EEG-BBNet, a hybrid network which integrates convolutional neural networks (CNN) with g...

While there have been increased researches using deep learning techniques for the extraction of vascular structure from the 2D en face OCTA, for such approach, it is known that the data annotation process on the curvilinear structure like the retinal vasculature is very costly and time consuming, albeit few tried to address the annotation problem....

The Sensors industry has seen extraordinary growth in the last 20 years. It is currently projected that the sensors market size will triple from 2020 to 2030. The most prevalent reason for this exponential growth is the technological trend towards hyper-intelligent systems. Future technologies are expected to dive into the Internet of Senses, Indus...

Neurophysiological characteristics of long-term Kratom users have been challenged for identification due to the lack of evidence. Long-term and high Kratom consumption caused concern, particularly in older adults. Thus, the study aims to explore EEG biomarkers in long-term Kratom users (LKU) based on consumer-grade EEG systems. The fifty-two partic...

We propose a deep learning-based approach to localizing a small moving object with a single millimeter-wave frequency-modulated continuous-wave (FMCW) radar. The main challenge that foils conventional localization techniques, such as 3-D fast Fourier transform (3-D-FFT), Pisarenko method, multiple signal classification (MUSIC), estimation of signal...

This review investigated research works on affective computing by using electrocardiogram (ECG) and electrodermal activity (EDA). The 27 related research papers, including 23 from IEEE Journals and 4 from other Q1 Journals in the last five years, were studied. The main goals have been to summarize common trends in this field in recent years as well...

As Technology advances in the 21st century, the inclusion of intelligence in any kind of system is becoming a necessity. First stage of intelligence, similarly to humans, is to observe and obtain information from the environment, which can only be achieved through the use of sensors. However, due to the wide diversity of the information to be obtai...

In the status quo, dementia is yet to be cured. Precise diagnosis prior to the onset of the symptoms can prevent the rapid progression of the emerging cognitive impairment. Recent progress has shown that Electroencephalography (EEG) is the promising and cost-effective test to facilitate the detection of neurocognitive disorders. However, most of th...

Human error has been implicated as a causal factor in a large proportion of road accidents. Automated driving systems purport to mitigate this risk, but self-driving systems that allow a driver to entirely disengage from the driving task also require the driver to monitor the environment and take control when necessary. Given that sleep loss impair...

Supervised deep convolutional neural networks (DCNNs) are currently one of the best computational models that can explain how the primate ventral visual stream solves object recognition. However, embodied cognition has not been considered in the existing visual processing models. From the ecological standpoint, humans learn to recognize objects by...

In the past few years, there are several researches on Parkin-son's disease (PD) recognition using single-photon emission computed tomography (SPECT) images with deep learning (DL) approach. However, the DL model's complexity usually results in difficult model interpretation when used in clinical. Even though there are multiple interpretation metho...

This comprehensive review mainly analyzes and summarizes the recently published works on IEEExplore in sensor-driven smart living contexts. We have gathered over 150 research papers, especially in the past five years. We categorize them into four major research directions: activity tracker, affective computing, sleep monitoring, and ingestive behav...

Music preference was reported as a factor, which could elicit innermost music emotion, entailing accurate ground-truth data and music therapy efficiency. This study executes statistical analysis to investigate the distinction of music preference through familiarity scores, response times (response rates), and brain response (EEG). Twenty participan...

The coronavirus disease 19 (COVID-19) pandemic that has been raging in 2020 does affect not only the physical state but also the mental health of the general population, particularly, that of the healthcare workers. Given the unprecedented large-scale impacts of the COVID-19 pandemic, digital technology has gained momentum as invaluable social inte...

Motion artifact is observed in electroencephalogram (EEG) signals during the acquisition. The elimination of this type of artifact using various signal processing approaches is considered a pre-processing task for different neural information processing applications. In this paper, the wavelet domain optimized Savitzky-Golay (WOSG) filtering approa...

Recognizing the movements during sleep is crucial for the monitoring of patients with sleep disorders. However, the utilization of Ultra-Wideband (UWB) radar for the classification of human sleep postures has not been explored widely. This study investigates the performance of the off-the-shelf single antenna UWB in a novel application of sleep pos...

Event-related desynchronization and synchronization (ERD/S) and movement-related cortical potential (MRCP) play an important role in brain-computer interfaces (BCI) for lower limb rehabilitation, particularly in standing and sitting. However, little is known about the differences in the cortical activation between standing and sitting, especially h...

The technological advancement in wireless health monitoring through the direct contact of the skin allows the development of light-weight wrist-worn wearable devices to be equipped with different sensors such as photoplethysmography (PPG) sensors. However, the motion artifact (MA) is possible to occur during daily activities. In this study, we atte...

We presented “Mind-Controlled 3D Printer” that translates brain signals from the user into 3D printed food. This system integrated an EEG recording device that measures neural activities in real-time with a machine learning algorithm that classify emotional valence and arousal levels, which determine the shape and size of the food fabricated by the...

(datasets: https://github.com/IoBT-VISTEC/EEG-Emotion-Recognition) Since the launch of the first consumer grade EEG measuring sensors 'NeuroSky Mindset' in 2007, the market has witnessed an introduction of at least one new product every year by competing manufacturers, which include NeuroSky, Emotiv, interaXon and OpenBCI. There are numerous variat...

Brain-Computer interfaces (BCIs) play a significant role in easing neuromuscular patients on controlling computers and prosthetics. Due to their high signal-to-noise ratio, steady state visually evoked potentials (SSVEPs) has been widely used to build BCIs. However, currently developed algorithms do not predict the modulation of SSVEP amplitude, wh...

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 occ...

Non-invasive devices involved in the detection of drowsiness generally include infrared camera and Electroencephalography (EEG), of which sometimes are constrained in an actual real-life scenario deployments and implementations such as in the working office environment. This study proposes a combination using the biometric features of keyboard and...

(datasets: https://github.com/IoBT-VISTEC/EEG-Emotion-Recognition) 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 res...

(IEEE Transactions on Cognitive and Developmental Systems) Electroencephalography (EEG) is another method for performing Person Identification (PI). Due to the nature of the EEG signals, EEG-based PI is typically done while a person is performing a mental task such as motor control. However, few studies used EEG-based PI while the person is in diff...

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 it...

Introduction Driving automation systems (DAS) purport to reduce the number of motor vehicle collisions and enhance driving safety by reducing driver workload, providing stable lane-keeping and automated braking when a hazard is detected. Current regulations require drivers to maintain situation awareness when supervising an autonomous vehicle in or...

The steady-state visual-evoked potential-based brain-computer interface (SSVEP-BCI) is a typically recognized visual stimulus frequency from brain responses. Each frequency represents one command to control a machine. For example, multiple target stimuli with different frequencies can be used to control the moving speeds of a robot. Each target sti...

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...

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...

Since, cancer is curable when diagnosed at an early stage, lung cancer screening plays an important role in preventive care. Although both low dose computed tomography (LDCT) and computed tomography (CT) scans provide more medical information than normal chest x-rays, there is very limited access to these technologies in rural areas. Recently, ther...

In this paper, the deep learning (DL) approach is applied to a joint training scheme for asynchronous motor imagery-based 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-imag...

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 EventRelated 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...

By applying a basic knowledge of brain-computer interfaces and brain stimulation, we introduce a novel architecture for brain-to-brain communication (B2B). Two main issues presented herein are brain synchronization and message modulation. According to our proposed B2B architecture, we assume that the higher the root mean square (RMS) of the voltage...

Recently, self-driving car has became active research areas. Numerous of automobile related research groups are working on technological development such as computer vision, self localization, sensing and etc. On the other hand, scientific knowledge of human factors while driving on autonomous cars are limited. In this preliminary study, we are foc...

Here we report the development of a personal identification number (PIN) application using a P300-based brain-computer interface (BCI). We focused on visual stimulation design for increasing the evoked potential in the brain. Single-channel electroencephalography and a computationally inexpensive algorithm were used for P300 detection. Experimental...

In this study, we propose a hybrid brain/blink computer interface based on a single-channel electroencephalography (EEG) amplifier. Eyelid closing and hard blink were selected as two possible inputs for control of the interface. A 2-min calibration was required before starting to use the interface. An algorithm for feature extraction and classifica...

In this study, we developed a hybrid brain–computer interface for drowsiness detection using electroencephalography (EEG) and electrooculography (EOG). Measurement was done with a single-channel EEG amplifier. A simple responsive task performed in a drowsy environment was used to experimentally demonstrate the advantages of the proposed system. Add...

Electrooculography (EOG) enables users to use specific eye movements as inputs for various applications, without using their fingers. However, online classification of such signals often requires long or sophisticated calibration procedures and multiple electrodes, which makes the resulting systems not practical for everyday use. To this end, a sin...

A brain-computer interface has received a lot of attention for years, because this interface can be a useful tool for locked-in patients such as amyotrophic lateral sclerosis (ALS) patients. If the placement of the electroencephalogram (EEG) electrode becomes easier to attach, this interface will be more practical and become widely used among peopl...

This research demonstrates the orientation-modulated attention effect on visual evoked potential. We combined this finding with our previous findings about the motion-modulated attention effect and used the result to develop novel visual stimuli for a personal identification number (PIN) application based on a brain–computer interface (BCI) framewo...

Member The event-related potential (ERP) P300 is an electroencephalographic correlate of target recognition in decision-making tasks. The P300 is used in several brain-computer interfaces (BCIs) as a non-motor signal of decisions, such as letter choice in the P300-Speller utility. Accuracy in choice specification depends on the difference in P300 a...

We report the development of a new modality of personal identification application based on a brain--computer interface (BCI) framework. We used our previously developed visual stimulation in the proposed system, the overall architecture of which was designed to be simple for practical application. An electroencephalography amplifier with a single...

We developed a new visual stimulation paradigm for P300-based brain-computer interfaces. The principal idea is to enhance P300 amplitude by modulation of spatial attention to a flickering visual target A small flicker matrix was used for evaluation. Six healthy volunteers participated in experiments, and brain signals were recorded by electroenceph...

In this study, we propose visual stimulation based on the primary colors (red, green, and blue) in order to investigate the characteristics of the P300 response. Eleven healthy volunteers participated in our experiment, and their brain signals were recorded by electroencephalography (EEG). Using two basic measures referred to as `on-peak' and `off-...

In this paper, we present an unsharp masking-based approach with subsequent bilateral filtering stage to noise smoothing of ultrasound (US) image. At our first processing stage, we propose image segmentation via EM to segregate two pixels populations instead of separating original image into the low- and high-frequency components. Our proposed meth...