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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
    ;
    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.
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    Time-frequency based coherence analysis of red and green flickering visual stimuli for EEG-controlled applications
    (2017-03-23)
    Tantisatirapong, Suchada
    ;
    Dechwechprasit, Panisa
    ;
    Senavongse, Wongwit
    ;
    Phothisonothai, Montri
    The stimulus flickering at specific frequencies, or as known as steady-state visually evoked potential (SSVEP), can be recorded on an occipital area of the brain. SSVEP is used to interpret the EEG signal to detect the desired goal of the experiment. In this paper, we aim to investigate SSVE signal by means of magnitude-squared coherence (MSC) analysis between the red and green visual stimuli. In the experimental paradigm, we considered two parameters that are chromatic color and flickering frequency where an epoch interval was 10 seconds. The obtained results showed the statistically significant frequency-domain response and its maximum MSC coefficient in the theta and alpha bands for green and red flickers, respectively.
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    Brainwave spectrum analysis during paced breathing control: A pilot study
    (2017-02-21)
    Pakoktom, Nipawan
    ;
    Tretriluxana, Suradej
    ;
    Chitsakul, Kitiphol
    Investigation of Electroencephalogram (EEG) has been made during the spontaneous breathing and paced breathing control (9 breaths/minute) conditions in eight young healthy college students (Male=4, Female=4). Their selected 5-minute EEG segments were transformed into 3 frequency bands (Delta Wave: 0.5-4Hz, Theta Wave: 4-8Hz and Alpha Wave: 8-13Hz). The power in each band were compared between two conditions. Our statistical tests show no significant differences between the two conditions in all three spectrums. This pilot study needs to be further analyzed to make a firm conclusion and a larger sample size is suggested.
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