Khemanuwong, Thapanee

Loading...
Thumbnail Image
Preferred name
Khemanuwong, Thapanee
Main Affiliation
Email
thapanee.kh@kmitl.ac.th

Settings

Now showing 1 - 3 of 3
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Brain Function Analysis Using EEG Evidence: New Insights into English Paper-Based Versus Computer-Based Tests
    (2024-01-01)
    Khemanuwong, Thapanee
    ;
    Angsuwatanakul, Thanate
    ;
    Phairot, Ekkapon
    ;
    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).
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Mind to Motion: EEG-Based Classification of Motor Imagery and Actual Hand Movements Using LSTM Models
    (2023-01-01)
    Chakkamallisery, Apoorva Sunil
    ;
    Pelmo, Sonam Tenzin
    ;
    Angsuwatanakul, Thanate
    ;
    Pititheeraphab, Yutthana
    ;
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Memory Training in Dementia: A VR-BCI Prototype with N-Back and Brainwave Analysis
    (2025-01-01)
    Angthong, Sorawich
    ;
    Angsuwatanakul, Thanate
    ;
    Phairot, Ekkapon
    ;
    Bunyasuwan, Meena
    ;
    Khieukhajee, Jedsada
    Dementia, a neurodegenerative condition leading to significant memory impairment in the elderly, currently lacks a curative treatment. Management strategies focus on mitigating symptom progression. This study presents the development and preliminary evaluation of a novel Virtual Reality (VR) game prototype integrated with Brain Computer Interface (BCI) technology designed to enhance memory, a key deficit in dementia, and potentially delay its progression. The VR game employed an adaptive N-back task for memory training, while a 4-channel OpenBCI system non-invasively monitored electroencephalography (EEG) signals from five healthy young adult volunteers (aged 18-22). Analysis of EEG power ratios revealed a trend towards increased alpha wave activity during memory encoding phases of the game. Specifically, during the Alpha EEG test, theta wave amplitudes were consistently higher across all channels (F3, F4, O1, O2) compared to alpha and beta waves. Furthermore, a descriptive trend indicated that theta wave amplitudes tended to increase with increasing difficulty in the Alpha EEG test. During the N-back tasks, theta wave amplitudes were observed to be descriptively higher during the memory encoding period compared to the recall period in frontal channels (F3, F4). The initial observations, while not statistically significant due to the limited sample size, support the feasibility of using the VR-BCI system to engage memory-related brain activity. To thoroughly evaluate the potential of this approach for cognitive rehabilitation and dementia mitigation, further comprehensive research involving larger and older cohorts utilizing longitudinal designs is warranted.