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Panisa Dechwechprasit

Bio: Panisa Dechwechprasit is an academic researcher from Srinakharinwirot University. The author has an hindex of 2, co-authored 2 publications receiving 5 citations.

Papers
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Proceedings ArticleDOI
01 Dec 2016
TL;DR: A case study of electroencephalogram (EEG) signal is presented by analyzing the frequency response of the red and green light to stimulate the EEG signal to locate the desired goal of the experiment.
Abstract: The study of brain activity can be done by visual stimulus flickering at specific frequencies, Steady-State Visual Evoked Potential or as known as SSVEP. SSVEP is to stimulate the EEG signal to locate the desired goal of the experiment when a visual stimulus flickering with different constant frequencies and same duration. We aim to present a case study of electroencephalogram (EEG) signal by analyzing the frequency response of the red and green light. The stimulation is based on SSVEP by dividing the trial into two trials: single light and two lights. We considered three parameters that are light color, frequency and epoch interval. The optimal experimental results showed the classification accuracy rate of 74% and 75% for single and two color lights, respectively. The results can be considerably applied to the brain-computer interface (BCI) system.

6 citations

Proceedings ArticleDOI
01 Feb 2017
TL;DR: This paper aims to investigate SSVE signal by means of magnitude-squared coherence (MSC) analysis between the red and green visual stimuli and shows the statistically significant frequency-domain response and its maximum MSC coefficient in the theta and alpha bands for green and red flickers.
Abstract: 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.

2 citations

Proceedings ArticleDOI
28 Aug 2022
TL;DR: In this paper , a terahertz single-pole double-throw (SPDT) switch based on an integrated disk resonator for a substrateless dielectric platform made of an all-silicon-based effective medium is presented.
Abstract: We present a terahertz single-pole double-throw (SPDT) switch based on an integrated disk resonator for a substrateless dielectric platform made of an all-silicon-based effective medium. The switch operates based on photoexcitation by using visible light focused onto the disk resonator to turn off the resonance, thus removing coupling between two waveguides. The result shows low insertion loss of the proposed switch due to the low dissipation of the platform. The device achieves an extinction ratio of 11 dB and 1.5 GHz of terahertz bandwidth. This terahertz switch can be employed in various terahertz applications including phase shifting and beam steering.

1 citations

Proceedings ArticleDOI
01 Nov 2022
TL;DR: In this paper , a disk resonator and a photonic crystal cavity based on a sub-strateless dielectric waveguide platform are proposed for terahertz applications.
Abstract: In the past two decades, terahertz technology has been steadily improved with a wide range of scientific studies to develop terahertz applications. Recently, a substrateless dielec-tric waveguide platform based on effective medium has been proposed. Waveguiding on this silicon-based platform can be realized with low loss and low dispersion. One important series of components for this platform includes resonant cavities of different characteristics that are crucial for terahertz integrated systems. In this article, we present one design of a disk resonator, and one design of a photonic crystal cavity based on this sub-strateless dielectric waveguide platform. These cavities operate within the frequency range of 220–330 GHz. The simulation and measurement results of these resonant cavities show a strong resonant behavior, with a resonance Q-factor that can be tuned. These cavities can be employed in various terahertz applications including sensing, switching, and modulation.

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Proceedings ArticleDOI
01 Sep 2017
TL;DR: The authors review the scientific challenges of electroencephalography data acquisition for BCI, the parameters influencing its variability, and the processing techniques for a correct feature extraction and data classification, and describe the post processing techniques to properly choose the right signal feature.
Abstract: Brain-Computer Interface (BCI) is an innovative communication technique mainly used in biomedical applications for assisting devices. In this context, main aim is help people with severe disabilities restore the movement ability, or replace lost motor function controlling external devices, or communicate with other people leading them to become more self-sufficient. The connection between BCI and wireless communication standards brings BCI into the Internet of Things (IoT), giving the opportunity to better interconnect the brain of both able and disable people with the surrounding physical and cyber worlds: it is called the human in the loop paradigm. The combination of BCI and IoT falls within the wider topic of Cognitive IoT technology, an enriched solution for Industry 4.0 and IoT in industry. The integration of the human role in the IoT can lead to several advantages both in human life and in technological progress. As a novel measurement device, BCI has to be widely characterized in order to improve the reliability of the obtained result. In this work, the authors review the scientific challenges of electroencephalography data acquisition for BCI, the parameters influencing its variability, and the processing techniques for a correct feature extraction and data classification. In particular, first the influence factors and the issues of EEG acquisition are reviewed. Then, the most popular devices used for BCI sensing unit are described. Finally, the authors describe the post processing techniques, the feature extraction algorithms, and the the classifier to properly choose the right signal feature.

8 citations

Proceedings ArticleDOI
01 Jan 2019
TL;DR: An integrated HMI system using electrical brainwave signal and eye tracking of pupil movement and the user-friendly virtual keyboard typing for Thai language, i.e., free-form and automatic typing modes is introduced.
Abstract: Human-Machine Interaction (HMI) requires a multidisciplinary research study mainly focused on interaction modalities between humans and machines. In this paper, we introduced an integrated HMI system using electrical brainwave signal (or called Electroencephalography: EEG) and eye tracking of pupil movement (or called ET), whose are used as an alternative channel to communicate with others for people with disabilities. In this experiment, the target and non-target visual stimuli of EEG-based HMI system on the basis of event-related potential (ERP) and steady state visually evoked potential (SSVEP) signals have been performed. For the ET based framework, we proposed the user-friendly virtual keyboard typing for Thai language, i.e., free-form and automatic typing modes. The results showed that the integrated HMI using ERP-SSVEP yielded an average accuracy of 97.4% and reaction time approximately was 724.2 millisecond for control commands. The automatic typing mode performed an average accuracy of 97%, with an average printing time of 6.17 seconds per word for ET based virtual Thai keyboard.

2 citations

Journal ArticleDOI
TL;DR: A modular continuous restricted Boltzmann machine (MCRBM) is proposed to improve the performance of SSVEP-based BCIs and the experimental results showed that MCRBM produce higher accuracy compared to CRBM.
Abstract: The communication of a patient with amyotrophic lateral sclerosis is limited and then the quality of lives would be greatly reduced. The patients still maintain the cognitive ability, thus developing an assistive communication interface would greatly help them in daily live. Recently, steady state visually evoked potential (SSVEP) based brain computer interfaces (BCIs) had been successfully developed to help patients. Increasing the accuracy of SSVEP-based BCIs is able to realize the assistive communication interfaces in practical applications. In this study, a modular continuous restricted Boltzmann machine (MCRBM) is proposed to improve the performance of SSVEP-based BCIs. To precisely represent the characteristics of elicited signals of SSVEP, the frequency magnitude, the coefficients of canonical correlation analysis, and the correlations of magnitude square coherence are selected as the features. In the first layer of MCRBM, the continuous restricted Boltzmann machine based neural networks are used as the basic units and applied to accurately estimate by using different types of features. In the second layer of MCRBM, a CRBM is then designed to fuse the decisions and find the final results. The experimental results showed that MCRBM produce higher accuracy compared to CRBM. Therefore, the proposed approach can be adopted in practical applications and then help patients in communicating with others.

2 citations

Journal ArticleDOI
TL;DR: In this article , the authors compared and analyzed the experimental paradigm and decoding algorithm for the transient visual evoked potential (SSVEP) and improved SSVEP paradigms and pointed out the problems and development bottlenecks in experimental paradigm.
Abstract: The brain–computer interface (BCI) technology has received lots of attention in the field of scientific research because it can help disabled people improve their quality of life. Steady‐state visual evoked potential (SSVEP) is the most researched BCI experimental paradigm, which offers the advantages of high signal‐to‐noise ratio and short training‐time requirement by users. In a complete BCI system, the two most critical components are the experimental paradigm and decoding algorithm. However, a systematic combination of the SSVEP experimental paradigm and decoding algorithms is missing in existing studies. In the present study, the transient visual evoked potential, SSVEP, and various improved SSVEP paradigms are compared and analyzed, and the problems and development bottlenecks in the experimental paradigm are finally pointed out. Subsequently, the canonical correlation analysis and various improved decoding algorithms are introduced, and the opportunities and challenges of the SSVEP decoding algorithm are discussed.

1 citations