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arXiv subjects

Theerawit Wilaiprasitporn

Publications and source records attributed to Theerawit Wilaiprasitporn.

27 records · Page 2Linked to original sources

arXiv2019

A Single-Channel Consumer-Grade EEG Device for Brain-Computer Interface: Enhancing Detection of SSVEP and Its Amplitude Modulation

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, which is known to change as a function of stimulus luminance contrast. In this study, we aim to develop an integrated approach to simultaneously estimate the frequency and contrast-related amplitude modulations of the SSVEP signal. To achieve that, we developed a behavioral task in which human participants focused on a visual flicking target which the luminance contrast can change through time in several ways. SSVEP signals from 16 subjects were then recorded from electrodes placed at the central occipital site using a low-cost, consumer-grade EEG. Our results demonstrate that the filter bank canonical correlation analysis (FBCCA) performed well in SSVEP frequency recognition, while the support vector regression (SVR) outperformed the other supervised machine learning algorithms in predicting the contrast-dependent amplitude modulations of the SSVEPs. These findings indicate the applicability and strong performance of our integrated method at simultaneously predicting both frequency and amplitude of visually evoked signals, and have proven to be useful for advancing SSVEP-based applications.

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arXiv2019

Drowsiness Detection for Office-based Workload with Mouse and Keyboard Data

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 mouse movements and eye tracking during an office-based tasks to detect and evaluate drowsiness according to the self-report Karolinska sleepiness scale (KSS) questionnaire. Using machine learning models, the results demonstrate a correlation between the predicted KSS from the biometrics and the actual KSS from the user input, indicating the feasibility of evaluating the office workers' drowsiness level of the proposed approach.

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arXiv2019

Consumer Grade Brain Sensing for 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 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.

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arXiv2019

Universal Joint Feature Extraction for P300 EEG Classification using Multi-task Autoencoder

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 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.

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arXiv2019

Affective EEG-Based Person Identification Using the Deep Learning Approach

Electroencephalography (EEG) is another mode for performing 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. \textcolor{red}{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. \textcolor{red}{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 \emph{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. \textcolor{red}{Furthermore, CNN-GRU overcomes the influence of affective states in EEG-Based PI reported in the previous works.

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arXiv2018

Automatic Lung Cancer Prediction from Chest X-ray Images Using Deep Learning Approach

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, there is a trend in using computer-aided diagnosis (CADx) to assist in screening and diagnosing of cancer from biomedical images. In this study, the 121-layer convolutional neural network also known as DenseNet-121 by G. Huang et. al., along with the transfer learning scheme was explored as a means to classify lung cancer using chest X-ray images. The model was trained on a lung nodules dataset before training on the lung cancer dataset to alleviate the problem of a small dataset. The proposed model yields 74.43$\pm$6.01\% of mean accuracy, 74.96$\pm$9.85\% of mean specificity, and 74.68$\pm$15.33\% of mean sensitivity. The proposed model also provides a heatmap for identifying the location of the lung nodule. These findings are promising for further development of chest x-ray-based lung cancer diagnosis using the deep learning approach. Moreover, these findings solve the problem of small dataset.

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arXiv2018

Single Channel ECG for Obstructive Sleep Apnea Severity Detection using a Deep Learning Approach

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 $<$ 5) and severe OSA patients (AHI $>$ 30). The 15-second raw ECG records with apnea or hypopnea events were used with a series of deep learning models. 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 k-fold 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 real-time alerting systems such as before SCD occurs.

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arXiv2018

Deep Neural Networks with Weighted Averaged Overnight Airflow Features for Sleep Apnea-Hypopnea Severity Classification

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 %.

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arXiv2018

Towards Asynchronous Motor Imagery-Based Brain-Computer Interfaces: a joint training scheme using deep learning

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-imagery EEG (\textit{background EEG}), (\textit{pure imagery}) EEG, and EEG during the transitional period between background EEG and pure imagery (\textit{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 cross-validation 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 ($C_{z}$, $C_{3}$ and $C_{4}$), mean accuracy reaches 71.52 % and 70.27 % for CNN-FC and CSP-SVM, 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.

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