Brain Signal Classification using Genetic Algorithm for Right-Left Motion Pattern

Rahmad, Cahya; Ariyanto, Rudy; Yunianto, Dika Rizky

doi:10.14569/ijacsa.2018.091134

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…The gene mutation classification is done manually by the pathologists, but employing an efficient classification model and identifying a gene mutation through textual pieces of evidence would definitely be a breakthrough in mutation classification and subsequently facilitate the detection of cancer tumours. Figure 1(a) differentiates the structures of normal genes with the mutated genes [ 17 ], and Figure 1(b) represents the various levels of genetic mutations [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection

Gupta

Wu²,

Arora

et al. 2021

Journal of Healthcare Engineering

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A cancer tumour consists of thousands of genetic mutations. Even after advancement in technology, the task of distinguishing genetic mutations, which act as driver for the growth of tumour with passengers (Neutral Genetic Mutations), is still being done manually. This is a time-consuming process where pathologists interpret every genetic mutation from the clinical evidence manually. These clinical shreds of evidence belong to a total of nine classes, but the criterion of classification is still unknown. The main aim of this research is to propose a multiclass classifier to classify the genetic mutations based on clinical evidence (i.e., the text description of these genetic mutations) using Natural Language Processing (NLP) techniques. The dataset for this research is taken from Kaggle and is provided by the Memorial Sloan Kettering Cancer Center (MSKCC). The world-class researchers and oncologists contribute the dataset. Three text transformation models, namely, CountVectorizer, TfidfVectorizer, and Word2Vec, are utilized for the conversion of text to a matrix of token counts. Three machine learning classification models, namely, Logistic Regression (LR), Random Forest (RF), and XGBoost (XGB), along with the Recurrent Neural Network (RNN) model of deep learning, are applied to the sparse matrix (keywords count representation) of text descriptions. The accuracy score of all the proposed classifiers is evaluated by using the confusion matrix. Finally, the empirical results show that the RNN model of deep learning has performed better than other proposed classifiers with the highest accuracy of 70%.

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Section: Introductionmentioning

confidence: 99%

Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection

Gupta

Wu²,

Arora

et al. 2021

Journal of Healthcare Engineering

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“…EEG signals are processed using power spectral density (PSD) to extract the most representative features in the context of cognitive workload. These characteristics are defined as frequency bands: Delta (0.5-4 Hz), Theta (4-8 Hz), Alpha (8-12 Hz ), Beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and Gamma (30-100 Hz) [23].…”

Section: Methodsmentioning

confidence: 99%

“…GAs are adaptive and robust computational procedures based on the mechanism of natural genetic systems inspired by natural evolution theory of Charles Darwin [20]. GA is used to solve complex models' optimization problems, looking for the best feature set, especially when the search space is large and complex [18].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection Model based on EEG signals to Assess the Cognitive Workload in Drivers

Becerra-Sánchez¹,

Reyes²,

Ibáñez³

2020

Preprint

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In recent years, research has focused on generating mechanisms to assess the levels of subjects' cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools to analyze cognitive workload where the electroencephalographic (EEG) signals are the most used due to its high precision. However, one of the main challenges in the EEG signals implementing is finding the appropriate information to identify cognitive states. Here we show a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workload and structures a new dataset capable of optimizing the model's predictive process. We found that GALoRIS identifies data related to high and low cognitive workload of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50%, maximizing the model's predictive capacity-achieving a precision rate greater than 90%.

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“…GAs are adaptive and robust computational procedures based on the mechanism of natural genetic systems inspired by the natural evolution theory of Charles Darwin [ 27 ]. GA is used to solve a complex model’s optimization problems, by looking for the best feature set, especially when the search space is large and complex [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

Becerra-Sánchez

Reyes-Muñoz

Ibáñez

2020

Sensors

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In recent years, research has focused on generating mechanisms to assess the levels of subjects’ cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools for analyzing the cognitive workload, and electroencephalographic (EEG) signals have been most frequently used due to their high precision. However, one of the main challenges in implementing the EEG signals is finding appropriate information for identifying cognitive states. Here, we present a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workloads and structures a new dataset capable of optimizing the model’s predictive process. We found that GALoRIS identifies data related to high and low cognitive workloads of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50% and maximizing the model’s predictive capacity, achieving a precision rate greater than 90%.

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Brain Signal Classification using Genetic Algorithm for Right-Left Motion Pattern

Cited by 5 publications

References 16 publications

Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection

Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection

Feature Selection Model based on EEG signals to Assess the Cognitive Workload in Drivers

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

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