Limpiti, Tulaya

Preferred name

Limpiti, Tulaya

Alternative Name

Limpiti, T.

Main Affiliation

School of Engineering

tulaya.li@kmitl.ac.th

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A spatiotemporal framework for MEG/EEG evoked response amplitude and latency variability estimation
(2010-03-01)
Limpiti, Tulaya
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Van Veen, Barry D.
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Wakai, Ronald T.
This paper presents a spatiotemporal framework for estimating single-trial response latencies and amplitudes from evoked response magnetoencephalographic/ electroencephalographic data. Spatial and temporal bases are employed to capture the aspects of the evoked response that are consistent across trials. Trial amplitudes are assumed independent but have the same underlying normal distribution with unknown mean and variance. The trial latency is assumed to be deterministic but unknown. We assume that the noise is spatially correlated with unknown covariance matrix. We introduce a generalized expectationmaximization algorithm called Trial Variability in Amplitude and Latency ( TriViAL) that computes the maximum likelihood (ML) estimates of the amplitudes, latencies, basis coefficients, and noise covariance matrix. The proposed approach also performs ML source localization by scanning the TriViAL algorithm over spatial bases corresponding to different locations on the cortical surface. Source locations are identified as the locations corresponding to large likelihood values. The effectiveness of the TriViAL algorithm is demonstrated using simulated data and human evoked response experiments. The localization performance is validated using tactile stimulation of the finger. The efficacy of the algorithm in estimating latency variability is shown using the known dependence of the M100 auditory response latency to stimulus tone frequency. We also demonstrate that estimation of response amplitude is improved when latency is included in the signal model. © 2006 IEEE.
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EEG-BBNet: A Hybrid Framework for Brain Biometric Using Graph Connectivity
(2025-01-01)
Lakhan, Payongkit
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Banluesombatkul, Nannapas
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Sricom, Natchaya
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Sawangjai, Phattarapong
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Sangnark, Soravitt
Most EEG-based biometrics rely on either convolutional neural networks (CNNs) or graph convolutional neural networks (GCNNs) for personal authentication, potentially overlooking the limitations of each approach. To address this, we propose EEG-BBNet, a hybrid network that combines CNNs and GCNNs. EEG-BBNet leverages CNN’s capability for automatic feature extraction and the GCNN’s ability to learn connectivity patterns between EEG electrodes through graph representation. We evaluate its performance against solely CNN-based and graph-based models across three brain–computer interface tasks, focusing on daily motor and sensory activities. The results show that while EEG-BBNet with Rho index functional connectivity metric outperforms graph-based models, it initially lags behind CNN-based models. However, with additional fine-tuning, EEG-BBNet surpasses CNN-based models, achieving a correct recognition rate of approximately 90%. This improvement enables EEG-BBNet to adapt its learning in new sessions and to acquire different domain knowledge across various BCI tasks (e.g., motor imagery to steady-state visually evoked potentials), demonstrating promise for practical authentication.
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A handheld moisture content sensor using coupled-dipole antennas
(2010-07-01)
Mearnchu, J.
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Limpiti, T.
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Torrungrueng, D.
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Akkaraekthalin, P.
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Krairiksh, M.
This paper presents an analysis and design of a handheld moisture content sensor. A dielectric property determination technique was used to measure the magnitudes of the reflection and the coupling coefficients of coupled-dipole antennas. These coefficients were plotted and their intersection determined to obtain the values of ε' <inf>r</inf> and ε" <inf>r</inf> (ε' <inf>r</inf> and ε" <inf>r</inf>, respectively, are the real and imaginary parts of the relative complex permittivity of the dielectric of interest). Moisture content measurements for paddy at various moisture content levels are shown. Comparison of measurements made by this technique with those made by conventional transmission measurement technique yielded a compensation scheme for error reduction. This sensor is useful for controlling the quality of paddy dried in a continuous microwave drying system process.
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Study of large and highly stratified population datasets by combining iterative pruning principal component analysis and structure
(2011-06-23)
Limpiti, Tulaya
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Intarapanich, Apichart
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Assawamakin, Anunchai
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Shaw, Philip J.
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Wangkumhang, Pongsakorn
Background: The ever increasing sizes of population genetic datasets pose great challenges for population structure analysis. The Tracy-Widom (TW) statistical test is widely used for detecting structure. However, it has not been adequately investigated whether the TW statistic is susceptible to type I error, especially in large, complex datasets. Non-parametric, Principal Component Analysis (PCA) based methods for resolving structure have been developed which rely on the TW test. Although PCA-based methods can resolve structure, they cannot infer ancestry. Model-based methods are still needed for ancestry analysis, but they are not suitable for large datasets. We propose a new structure analysis framework for large datasets. This includes a new heuristic for detecting structure and incorporation of the structure patterns inferred by a PCA method to complement STRUCTURE analysis.Results: A new heuristic called EigenDev for detecting population structure is presented. When tested on simulated data, this heuristic is robust to sample size. In contrast, the TW statistic was found to be susceptible to type I error, especially for large population samples. EigenDev is thus better-suited for analysis of large datasets containing many individuals, in which spurious patterns are likely to exist and could be incorrectly interpreted as population stratification. EigenDev was applied to the iterative pruning PCA (ipPCA) method, which resolves the underlying subpopulations. This subpopulation information was used to supervise STRUCTURE analysis to infer patterns of ancestry at an unprecedented level of resolution. To validate the new approach, a bovine and a large human genetic dataset (3945 individuals) were analyzed. We found new ancestry patterns consistent with the subpopulations resolved by ipPCA.Conclusions: The EigenDev heuristic is robust to sampling and is thus superior for detecting structure in large datasets. The application of EigenDev to the ipPCA algorithm improves the estimation of the number of subpopulations and the individual assignment accuracy, especially for very large and complex datasets. Furthermore, we have demonstrated that the structure resolved by this approach complements parametric analysis, allowing a much more comprehensive account of population structure. The new version of the ipPCA software with EigenDev incorporated can be downloaded from http://www4a.biotec.or.th/GI/tools/ippca. © 2011 Limpiti et al; licensee BioMed Central Ltd.
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INJclust: Iterative neighbor-joining tree clustering framework for inferring population structure
(2014-09-01)
Limpiti, Tulaya
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Amornbunchornvej, Chainarong
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Intarapanich, Apichart
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Assawamakin, Anunchai
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Tongsima, Sissades
Understanding genetic differences among populations is one of the most important issues in population genetics. Genetic variations, e.g., single nucleotide polymorphisms, are used to characterize commonality and difference of individuals from various populations. This paper presents an efficient graph-based clustering framework which operates iteratively on the Neighbor-Joining (NJ) tree called the iNJclust algorithm. The framework uses well-known genetic measurements, namely the allele-sharing distance, the neighbor-joining tree, and the fixation index. The behavior of the fixation index is utilized in the algorithm's stopping criterion. The algorithm provides an estimated number of populations, individual assignments, and relationships between populations as outputs. The clustering result is reported in the form of a binary tree, whose terminal nodes represent the final inferred populations and the tree structure preserves the genetic relationships among them. The clustering performance and the robustness of the proposed algorithm are tested extensively using simulated and real data sets from bovine, sheep, and human populations. The result indicates that the number of populations within each data set is reasonably estimated, the individual assignment is robust, and the structure of the inferred population tree corresponds to the intrinsic relationships among populations within the data.

Limpiti, Tulaya

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