Classification of Sleep Stages via Machine Learning Algorithms

Bulut, Ali; Ozturk, Galip; Kaya, Ibrahim

doi:10.54856/jiswa.202205210

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…In binary classification, the independent t -test, which is commonly applied to define the significance of differences between measures of two different classes, is used for feature selection (Narin et al, 2014 ; Degirmenci et al, 2022c ). In multi-class classification, the ANOVA test is adopted for feature selection (Bulut et al, 2022 ; Degirmenci et al, 2022b ). ANOVA is a test applied when it is required to determine whether there is a difference between the means in conditions where there are two or more groups.…”

Section: Methodsmentioning

confidence: 99%

Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs

Degirmenci

Yüce²,

Perc

et al. 2023

Front. Hum. Neurosci.

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In recent studies, in the field of Brain-Computer Interface (BCI), researchers have focused on Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the interaction and communication between the paralyzed patients and the outside world for moving and controlling external devices such as wheelchair and moving cursors. However, current approaches in the Motor Imagery-BCI system design require effective feature extraction methods and classification algorithms to acquire discriminative features from EEG signals due to the non-linear and non-stationary structure of EEG signals. This study investigates the effect of statistical significance-based feature selection on binary and multi-class Motor Imagery EEG signal classifications. In the feature extraction process performed 24 different time-domain features, 15 different frequency-domain features which are energy, variance, and entropy of Fourier transform within five EEG frequency subbands, 15 different time-frequency domain features which are energy, variance, and entropy of Wavelet transform based on five EEG frequency subbands, and 4 different Poincare plot-based non-linear parameters are extracted from each EEG channel. A total of 1,364 Motor Imagery EEG features are supplied from 22 channel EEG signals for each input EEG data. In the statistical significance-based feature selection process, the best one among all possible combinations of these features is tried to be determined using the independent t-test and one-way analysis of variance (ANOVA) test on binary and multi-class Motor Imagery EEG signal classifications, respectively. The whole extracted feature set and the feature set that contain statistically significant features only are classified in this study. We implemented 6 and 7 different classifiers in multi-class and binary (two-class) classification tasks, respectively. The classification process is evaluated using the five-fold cross-validation method, and each classification algorithm is tested 10 times. These repeated tests provide to check the repeatability of the results. The maximum of 61.86 and 47.36% for the two-class and four-class scenarios, respectively, are obtained with Ensemble Subspace Discriminant among all these classifiers using selected features including only statistically significant features. The results reveal that the introduced statistical significance-based feature selection approach improves the classifier performances by achieving higher classifier performances with fewer relevant components in Motor Imagery task classification. In conclusion, the main contribution of the presented study is two-fold evaluation of non-linear parameters as an alternative to the commonly used features and the prediction of multiple Motor Imagery tasks using statistically significant features.

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Section: Methodsmentioning

confidence: 99%

Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs

Degirmenci

Yüce²,

Perc

et al. 2023

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…In this study, a feature reduction method based on statistical significance was applied to determine relevant ITD features that provide the best discrimination of the FM imageries for each sample. The statistical significance-based feature selection method used in this study was also performed in other BCI studies (Bulut et al, 2022;Degirmenci et al, 2022cDegirmenci et al, , 2023. One-way variance analysis (ANOVA test), which is mainly used to indicate whether there is a difference between the means in conditions where there are two or more groups was used in this study.…”

Section: Itd Featuresmentioning

confidence: 99%

EEG-based finger movement classification with intrinsic time-scale decomposition

Degirmenci,

Yuce,

Perc

et al. 2024

Front. Hum. Neurosci.

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IntroductionBrain-computer interfaces (BCIs) are systems that acquire the brain's electrical activity and provide control of external devices. Since electroencephalography (EEG) is the simplest non-invasive method to capture the brain's electrical activity, EEG-based BCIs are very popular designs. Aside from classifying the extremity movements, recent BCI studies have focused on the accurate coding of the finger movements on the same hand through their classification by employing machine learning techniques. State-of-the-art studies were interested in coding five finger movements by neglecting the brain's idle case (i.e., the state that brain is not performing any mental tasks). This may easily cause more false positives and degrade the classification performances dramatically, thus, the performance of BCIs. This study aims to propose a more realistic system to decode the movements of five fingers and the no mental task (NoMT) case from EEG signals.MethodsIn this study, a novel praxis for feature extraction is utilized. Using Proper Rotational Components (PRCs) computed through Intrinsic Time Scale Decomposition (ITD), which has been successfully applied in different biomedical signals recently, features for classification are extracted. Subsequently, these features were applied to the inputs of well-known classifiers and their different implementations to discriminate between these six classes. The highest classifier performances obtained in both subject-independent and subject-dependent cases were reported. In addition, the ANOVA-based feature selection was examined to determine whether statistically significant features have an impact on the classifier performances or not.ResultsAs a result, the Ensemble Learning classifier achieved the highest accuracy of 55.0% among the tested classifiers, and ANOVA-based feature selection increases the performance of classifiers on five-finger movement determination in EEG-based BCI systems.DiscussionWhen compared with similar studies, proposed praxis achieved a modest yet significant improvement in classification performance although the number of classes was incremented by one (i.e., NoMT).

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“…The main advantage of MLPs is that they scale well with more training data and have expressive power. But they are black boxes, and it isn't easy to find the optimal configuration with many possible architectures and hyperparameter tuning [23], [24], [25].…”

Section: B the Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

Android Malware Application Detection using Multi-layer Perceptron

Altan

Pasalioglu

2022

J Intell Syst Appl

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Cyber-attacks are one of the most critical problems that seriously threaten society. Whereas there are various presentations and ways of carrying out cyber-attacks, numerous mechanisms and techniques exist to defend applications. Many malware creators have chosen the Android operating system as a target due to its popularity. Thousands of new malware samples, aiming to infect new devices daily, are trying to circumvent the security measures implemented by Android app stores. This study experiments with a multi-layer perceptron model for Android malware detection. This proposed system is based on static analysis techniques on Android. We analyzed popular machine learning algorithms with a total number of 129013 applications (5560 malicious and 123453 harmless software). We achieved higher malware-detection rates of 97.60% in the iterations.

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Classification of Sleep Stages via Machine Learning Algorithms

Cited by 5 publications

References 15 publications

Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs

Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs

EEG-based finger movement classification with intrinsic time-scale decomposition

Android Malware Application Detection using Multi-layer Perceptron

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