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Evaluating Recurrent Neural Network Blind Source Separation of Event-Related Potentials Using Simulated Data
(2026-01-01)
O'Reilly, Jamie A.
Event-related potential (ERP) waveforms reflect spatiotemporal summation of potential differences resulting from the activity of current sources within the brain. Separating ERPs into underlying source waveforms and their scalp distributions is a potentially valuable for analyzing neurophysiology associated with psychophysiological events. However, ground-truth sources are unknown for real ERP data. This study aimed to evaluate recurrent neural network blind source separation (RNN-BSS) of ERP waveforms using simulated ERP data with known groundtruth source waveforms and scalp distributions. Simulated ERP waveforms were generated from seven simulated source waveforms and scalp distributions. Two simulations with positiveand negative-going source waveforms were evaluated to explore the effect of RNN source signal rectification on source representations. The source waveforms and scalp distributions extracted from applying RNN-BSS to simulated data were compared with ground-truth using Pearson's correlation coefficient. Source waveforms and scalp distributions extracted by RNN-BSS were highly correlated with their ground-truth counterparts. However, where scalp distributions of ground-truth sources were highly correlated, they were not separated perfectly by RNN-BSS. Negative-going simulations produced inverted scalp distributions. Overall, these results demonstrate efficacy of RNNBSS applied to simulated ERP waveforms with seven sources. Further evaluations are required to determine the limits of source separation when scalp distributions are highly correlated, and the influence of number of electrodes in RNN-BSS.
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Localized estimation of electromagnetic sources underlying event-related fields using recurrent neural networks
(2023-08-01)
O'Reilly, Jamie A.
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Zhu, Judy D.
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Sowman, Paul F.
Objective. To use a recurrent neural network (RNN) to reconstruct neural activity responsible for generating noninvasively measured electromagnetic signals. Approach. Output weights of an RNN were fixed as the lead field matrix from volumetric source space computed using the boundary element method with co-registered structural magnetic resonance images and magnetoencephalography (MEG). Initially, the network was trained to minimise mean-squared-error loss between its outputs and MEG signals, causing activations in the penultimate layer to converge towards putative neural source activations. Subsequently, L1 regularisation was applied to the final hidden layer, and the model was fine-tuned, causing it to favour more focused activations. Estimated source signals were then obtained from the outputs of the last hidden layer. We developed and validated this approach with simulations before applying it to real MEG data, comparing performance with beamformers, minimum-norm estimate, and mixed-norm estimate source reconstruction methods. Main results. The proposed RNN method had higher output signal-to-noise ratios and comparable correlation and error between estimated and simulated sources. Reconstructed MEG signals were also equal or superior to the other methods regarding their similarity to ground-truth. When applied to MEG data recorded during an auditory roving oddball experiment, source signals estimated with the RNN were generally biophysically plausible and consistent with expectations from the literature. Significance. This work builds on recent developments of RNNs for modelling event-related neural responses by incorporating biophysical constraints from the forward model, thus taking a significant step towards greater biological realism and introducing the possibility of exploring how input manipulations may influence localised neural activity.
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CNN–RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images
(2023-02-01)
Kanjanasurat, Isoon
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Tenghongsakul, Kasi
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Purahong, Boonchana
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Lasakul, Attasit
The 2019 coronavirus disease (COVID-19) has rapidly spread across the globe. It is crucial to identify positive cases as rapidly as humanely possible to provide appropriate treatment for patients and prevent the pandemic from spreading further. Both chest X-ray and computed tomography (CT) images are capable of accurately diagnosing COVID-19. To distinguish lung illnesses (i.e., COVID-19 and pneumonia) from normal cases using chest X-ray and CT images, we combined convolutional neural network (CNN) and recurrent neural network (RNN) models by replacing the fully connected layers of CNN with a version of RNN. In this framework, the attributes of CNNs were utilized to extract features and those of RNNs to calculate dependencies and classification base on extracted features. CNN models VGG19, ResNet152V2, and DenseNet121 were combined with long short-term memory (LSTM) and gated recurrent unit (GRU) RNN models, which are convenient to develop because these networks are all available as features on many platforms. The proposed method is evaluated using a large dataset totaling 16,210 X-ray and CT images (5252 COVID-19 images, 6154 pneumonia images, and 4804 normal images) were taken from several databases, which had various image sizes, brightness levels, and viewing angles. Their image quality was enhanced via normalization, gamma correction, and contrast-limited adaptive histogram equalization. The ResNet152V2 with GRU model achieved the best architecture with an accuracy of 93.37%, an F1 score of 93.54%, a precision of 93.73%, and a recall of 93.47%. From the experimental results, the proposed method is highly effective in distinguishing lung diseases. Furthermore, both CT and X-ray images can be used as input for classification, allowing for the rapid and easy detection of COVID-19.
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Energy Prediction of Cleanroom-type Differential Drive Mobile Robot Based on Recurrent Neural Network
(2023-01-01)
Yanyong, Sarucha
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Konghuayrob, Poom
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Chaisiri, Punyavee
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Kaitwanidvilai, Somyot
The battery charger time is a major issue for mobile robots. The study of the power usage of each component is important for optimizing the overall power consumption. Additionally, knowing the total energy consumption before commanding a robot to execute a task is essential for effective queue management and determining which robots are ready to execute tasks or move to the charging station. In this paper, we propose an energy modeling system consisting of an energy sensing technique, logging, and a recurrent neural network prediction model. The model is configured to recognize the dynamic system of the drive unit with the support of the robot operating system. The proposed model has a prediction error of only 3.58%. The simulation and experimental results demonstrate the effectiveness of the proposed system.
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A Guided Tutorial on Modelling Human Event-Related Potentials with Recurrent Neural Networks
(2022-12-01)
O’Reilly, Jamie A.
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Wehrman, Jordan
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Sowman, Paul F.
In cognitive neuroscience research, computational models of event-related potentials (ERP) can provide a means of developing explanatory hypotheses for the observed waveforms. However, researchers trained in cognitive neurosciences may face technical challenges in implementing these models. This paper provides a tutorial on developing recurrent neural network (RNN) models of ERP waveforms in order to facilitate broader use of computational models in ERP research. To exemplify the RNN model usage, the P3 component evoked by target and non-target visual events, measured at channel Pz, is examined. Input representations of experimental events and corresponding ERP labels are used to optimize the RNN in a supervised learning paradigm. Linking one input representation with multiple ERP waveform labels, then optimizing the RNN to minimize mean-squared-error loss, causes the RNN output to approximate the grand-average ERP waveform. Behavior of the RNN can then be evaluated as a model of the computational principles underlying ERP generation. Aside from fitting such a model, the current tutorial will also demonstrate how to classify hidden units of the RNN by their temporal responses and characterize them using principal component analysis. Statistical hypothesis testing can also be applied to these data. This paper focuses on presenting the modelling approach and subsequent analysis of model outputs in a how-to format, using publicly available data and shared code. While relatively less emphasis is placed on specific interpretations of P3 response generation, the results initiate some interesting discussion points.
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Categorize Level of Crystal Sugar Making with Recurrent Neural Network
(2022-01-01)
Ounsrimuang, Pimolrat
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Nootyaskool, Supakit
This research presents the study of recurrent neural networks to predict industrial crystal sugar making. The recurrent neural network trains on six parameters consisting of liquid in the pan, Brix levels, vacuum in the pan, liquor temperatures, water steam supplier, and current for mix-motor agitator. The input variables were the trained model to predict by categorizing data in three levels high, middle, and low which the data came from human control the sugar boiler machine. The trained model for the future can be extended to make an experience meter to indicate the ability of workers to control the machine.
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Gold Investment Model on RNN and Finding Best Investment Strategy on PSO
(2022-01-01)
Kanchanakantikul, Pakamas
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Nootyaskool, Supakit
Nowadays, Algorithm trading in community and stock is interesting research, while gold is also an investment option. This research presents two steps. Three inputs sequence consists of the gold price(sell), gold spot and crude oil. Output has an order sequence indicating buy, sell, and wait for the signal. Firstly, finding the best strategy from historical data by particle swarm optimization (PSO) compared with random search (RS). That will get buying, selling, or waiting signals in the gold trading market Secondly, creating gold investment by recurrent neural network (RNN) model. The experiment result showed RNN trading model based on PSO is better than RS, which has a profit of 79.667 percent.

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