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Brain Function Analysis Using EEG Evidence: New Insights into English Paper-Based Versus Computer-Based Tests
(2024-01-01)
Khemanuwong, Thapanee
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Angsuwatanakul, Thanate
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Phairot, Ekkapon
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Iramina, Keiji
;
Puttasakul, Tasawan
In today's disruptive era, where digital systems and the internet are central, assessment methods are transitioning from paper-based to computer-based tests. As digital technology becomes more accessible, there is growing interest in determining whether these new formats provide more effective means of evaluating learning outcomes. To investigate this, we examined performance and objective measures of brain function during both computer-based and paper-based reading comprehension tests. This study focuses on brain function analysis of computer-based and paper-based tests measured by EEG signals. Five healthy students at the Bl CEFR level voluntarily participated in two experimental conditions: paper-based testing (PBT) and computer-based testing (CBT). During the tests, EEG signals were recorded and analyzed using MATLAB to identify various features of brain activity. The results indicate that participants who performed better on paper-based tests showed greater familiarity with the test format. The power spectral density of EEG recordings, along with the average frequency of alpha and beta waves, were positively correlated with test familiarity. Specifically, the correlation coefficients were as follows: CBT-difficult (r=0.82, p < 0.05), CBT -easy (r=0.82,p < 0.05), PBT -difficult (r=0.82, p < 0.05), and PBT -easy (r=0.65,p < 0.05).
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Mind to Motion: EEG-Based Classification of Motor Imagery and Actual Hand Movements Using LSTM Models
(2023-01-01)
Chakkamallisery, Apoorva Sunil
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Pelmo, Sonam Tenzin
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Angsuwatanakul, Thanate
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Pititheeraphab, Yutthana
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Puttasakul, Tasawan
This study presents an Electroencephalogram (EEG) based classification model tailored to discern between motor imagery and real motor actions. Additionally, the study investigates the efficacy of employing Long Short-Term Memory (LSTM) deep learning models for EEG signal analysis. The EEG analysis comprised two distinct phases, aimed at validating the hypothesis of distinguishing motor action from motor imagery. The proposed LSTM-based classification model exhibited a notable accuracy of 62.5% in discriminating motor action from motor imagery, and a promising 72.5% accuracy in distinguishing between resting state and motor action. These findings highlight the potential of EEG-based approaches in motor-related applications, thus providing auspicious avenues for the future development of brain-computer interfaces (BCIs) and motor rehabilitation technologies.
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Detection of 2,4,6-Trinitrotoluene by MIP-composite Based Electrochemical Sensor
(2021-03-10)
Puttasakul, Tasawan
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Tancharoen, Chompoonuch
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Sukjee, Wannisa
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Pintavirooj, Chuchart
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Sangma, Chak
In Thailand, there are terrorist groups active in some areas where casualties from their operations are civilians. Most of these casualties are from explosive device. The main component of the device is high explosive compound called 2,4,6-trinitrotoluene (TNT). Detection of TNT from objects and places in the suspected or area bombardment will provide useful evidence for the authority. In this work, we fabricate an electrochemical TNT sensor based on molecularly imprinted polymers (MIP) nanocomposites. The sensor aimed at TNT trace detection from solution of solid specimens. The sensor was able to detect TNT in solution with 3 log-scale linearity with 0.4 ppt lower limit of detection. The selectivity of the sensor toward TNT was considerably high compared with the other nitro compound.
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IDE Gas Sensor Based Dengue Virus Co-imprinting for Detection of 2,4,6-Trinitrotoluene
(2021-03-10)
Puttasakul, Tasawan
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Sukjee, Wannisa
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Pintavirooj, Chuchart
;
Sangma, Chak
In this work we present a method for explosive vapor detection by molecularly imprinted polymer (MIP) gas sensor. The explosive compound under investigation, 2,4,6-trinitrotoluene (TNT), in combination with dengue virus (DENV) was imprinted on polymer nanocomposites surface. This technique can create more TNT binding sites on the polymer structure. Thus, increasing the efficiency of template recognition. The TNT sensor was fabricated by coating the TNT MIP-composites on the surface of interdigitated electrodes (IDE). TNT detection was carried out by observing the capacitance change after TNT vapor expose. The result show that this technique can be used to detect TNT.
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Hydrogel Based-Electrochemical Gas Sensor for Explosive Material Detection
(2019-10-01)
Puttasakul, Tasawan
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Pintavirooj, Chuchart
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Sangma, Chak
;
Sukjee, Wannisa
In Thailand, most of the bomb devices produced by the terrorists use trinitrotoluene (TNT) as the main explosive material. Detecting TNT and preventing detonation could save many civilians. Due to its low vapor pressure, TNT requires an extremely high sensitivity method for detection. In this paper, we propose an application of the polyacrylamide gel as an electrochemical gas sensor for explosive material detection. Cyclic voltammetry signals were collected from samples, including TNT, pentaerythritol tetranitrate (PETN), and non-explosive vapors. It was found that a time-derivative calculation of the signal was unique to each chemical, rather than the signal itself, demonstrating utility for the detection of explosive material. This approach for making a gel-based sensor and the data analysis looks promising and requires further validation.

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