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Classification of finger movements through optimal EEG channel and feature selection
(Frontiers Media Sa, 2025) Degirmenci, Murside; Yuce, Yilmaz Kemal; Perc, Matjaz; Isler, Yalcin
Introduction Electrencephalography (EEG)-based brain-computer interfaces (BCIs) have become popular as EEG is accepted as the simplest and non-invasive neuroimaging modality to record the brain's electrical activity. In the current BCI research context, apart from predicting extremity movements, recent BCI studies have been interested in accurately predicting finger movements of the same hand using different pattern recognition methods over EEG data collected based on motor imagery (MI), through which a mental image of the desired action is generated when a person ideally simulates or imagines carrying out a certain motor task. Although several pattern recognition methods have already been recommended in literature, majority of the studies focusing on classifying five finger movements, were based on study designs that neglected or excluded the idle state of brain (i.e., no mental task state) during which brain does not carry out any MI task. This study design may result in an increasing number of false positives and a significant decrease in the prediction rates and classification performance. Moreover, recent studies have focused on improving prediction performance using complex feature extraction and machine learning algorithms while ignoring comprehensive EEG channels and feature investigation in the prediction of finger movements from EEGs. Therefore, the objectives of this study are threefold: (i) to develop a more viable and practical system to predict the movements of five fingers and the no mental task (NoMT) state from EEG signals (ii) to analyze the effects of the statistical-significance based feature selection method over four different feature domains (nonlinear domain, time-domain, frequency-domain and time-frequency domain) and their combinations, and (iii) to test these feature sets with different and prominent classifiers.Methods In this study, our major goal is not to explore the best machine algorithm performance, but to investigate the best EEG channels and features that can be used in the classification of finger movements. Hence, the comprehensive analysis of the effectiveness of EEG channels and features is performed utilizing a statistically significant feature distribution over 19 EEG channels for each feature set independently. A bulky dataset of electroencephalographic MI for EEG-based BCIs is used in this study. A total of 1102 EEG features supplied from different feature domains have been investigated. Subsequently, these features were tested with eight well-known classifiers, comprising Decision tree, Discriminant analysis, Naive Bayes, Support vector machine, k-nearest neighbor, Ensemble learning, Neural networks, and Kernel approximation.Results For subject-dependent analysis, the maximum accuracy of 59.17% was obtained using the EEG features that were selected the most (including (i) energy and variance of five frequency bands in frequency-domain feature set, (ii) all feature types in time-domain, time-frequency domain, and nonlinear domain feature sets) and all EEG channels by the Support vector machine algorithm. For subject-independent analysis, the maximum accuracy of 39. 30% was obtained using the mostly selected EEG features (which are (i) all feature types excluding the waveform length, average amplitude change value, absolute difference in standard deviation, and slope-change value feature types in time-domain feature set, (ii) the energy and variance values of all frequency bands except gamma frequency band in frequency-domain feature set, (iii) the entropy value of five frequency bands in time-frequency-domain feature set, and (iv) SD2 and SD1/SD2 values where lag = 1 in nonlinear feature set) and EEG channels (which are (i) some definite EEG channels including 2nd, 3rd, 7th, 11th, 13th, 14th, and 15th channels in time-frequency-domain feature set and (ii) all EEG channels in time-domain, frequency-domain, and nonlinear feature sets) by the Support vector machine classifier.Discussion Experimental results demonstrate that despite the high-class number, the proposed approach obtained a modest yet considerable advancement in finger movement prediction when the results are compared to the results of similar studies. Additionally, for almost all feature sets, the statistical significance-based feature reduction method improves the prediction performance in the most of classifiers, contributing elaborate EEG channel and feature analysis. Nonetheless, in this study, we used an EEG dataset recorded from only 13 healthy subjects; therefore, a dataset covering more subjects is necessary to reach a more general conclusion.
Classification of finger movements using statistically significant time-domain EEG features
(Gazi Univ, Fac Engineering Architecture, 2024) Degirmenci, Murside; Yuce, Yilmaz Kemal; Isler, Yalcin
Purpose: The accurate decoding of individual finger movements is pivotal for advanced prosthetic control. In this study, it is aimed to show the effectiveness statistically significant time-domain features based on ANOVA in the classification of EEG signals of finger movements. Theory and Methods: We tried to differentiate the finger movements EEG signals from the dataset which is an open-available and large motor imaginary EEG dataset. The time domain features were calculated from 21 channels of EEG signals of 8 healthy subjects. The effectiveness of two different feature selection methods which are statistically significance-based feature selection (ANOVA) and Principal Component Analysis (PCA) were investigated to discriminate five finger movements and no motor imagery task condition (NoMT). The feature vectors which including all time domain features, ANOVA-selected time domain features, PCA-selected time domain features, and both ANOVA- and PCA-selected time-domain features were applied to 8 different classifiers. The proposed approaches were investigated for both subject-dependent and subject-independent conditions. Results: The highest classification testing accuracy of 35.8% obtained using ANOVA-selected time-domain features with SVM classifier in subject-independent analysis; whereas, in subject-dependent analysis for 8 subjects, the highest classification testing accuracies achieved between 33.3%-57.5% using ANOVA-selected time-domain features for SVM classifier respectively. Conclusion: The results show that the ANOVA-based feature selection method improves the classification performances. The performances of the subject-dependent classifications are higher than the performance of the subject-independent classifications. Therefore, the subject-dependent results of the proposed model have a big potential, which may pave the way for the design of advanced personalized hand prostheses.
Durağan Durum Görsel Uyaran Potansiyellerinden Fourier Dönüşümü ile Üç Farklı Frekansın Kestirimi
(2020) Sayılgan, Ebru; Yuce, Yilmaz Kemal; Isler, Yalcin
Durağan durum görsel uyarılmış potansiyeller (DDGUP), diğer beyin bilgisayar ara yüzü (BBA) tekniklerineoranla oldukça yüksek sinyal-gürültü oranları ve bilgi aktarım hızına sahip oldukları için EEG çalışmalarındasıkça kullanılır. Ayrıca durağan durum paradigmaları, dinamik neokorteks süreçlerinde tercih edilen frekanslarıkarakterize etmek için de kullanılır. Kısa eğitim süresine sahip olan DDGUP’lar, pratik uygulamalarda önemlibir rol oynar. Sinyalleri komuta dönüştürmekte kullanılan, sinyal işleme algoritmaları, BBA sistemlerininperformansını arttırmak için kilit öneme sahiptir. Buna ek olarak, DDGUP sinyallerinin birbirinden farklıyöntemlerle sınıflandırılmasını araştıran çok az çalışma vardır. Bu çalışmada, internetten açık erişim ile alınanveri seti (AVI SSVEP Dataset) üzerinde analizler yapılmıştır. Veri setindeki EEG kayıtları, katılımcılar, rengisiyahtan beyaza hızla değişen yedi farklı frekansta yanıp sönen bir kutuya baktıkları durumda kaydedilmiştir.Oksipital bölgeden kaydedilen DDGUP sinyalleri ilk olarak Hızlı Fourier Dönüşümü uygulanarak, sinyal altbantlarına (delta, teta, alfa, beta ve gama) ayrılmıştır. Alt bantların her biri için enerji ve varyans öznitelikvektörleri çıkarılmıştır. Öznitelikler altı temel sınıflandırıcı (LDA, k-NN, SVM, Naive Bayes, ToplulukÖğrenmesi, Karar Ağacı) ile sınıflandırılmıştır. Sınıflandırma performansları birbirleri ile karşılaştırılmıştır.Sınıflandırma 5-kat çapraz doğrulama modeli ve hata matrisinden doğruluk değerleri çıkarılarak analizedilmiştir. Katılımcılar ayrı ayrı göz önüne alındığında %100’e varan sınıflandırma başarımı SVM ve k-NNsınıflandırıcılarında elde edilirken, ortalamalara göre en yüksek başarım Topluluk Öğrenmesi sınıflandırıcısında%79,73 olarak elde edilmiştir.
EEG channel and feature investigation in binary and multiple motor imagery task predictions
(Frontiers Media Sa, 2024) Degirmenci, Murside; Yuce, Yilmaz Kemal; Perc, Matjaz; Isler, Yalcin
Introduction Motor Imagery (MI) Electroencephalography (EEG) signals are non-stationary and dynamic physiological signals which have low signal-to-noise ratio. Hence, it is difficult to achieve high classification accuracy. Although various machine learning methods have already proven useful to that effect, the use of many features and ineffective EEG channels often leads to a complex structure of classifier algorithms. State-of-the-art studies were interested in improving classification performance with complex feature extraction and classification methods by neglecting detailed EEG channel and feature investigation in predicting MI tasks from EEGs. Here, we investigate the effects of the statistically significant feature selection method on four different feature domains (time-domain, frequency-domain, time-frequency domain, and non-linear domain) and their two different combinations to reduce the number of features and classify MI-EEG features by comparing low-dimensional matrices with well-known machine learning algorithms.Methods Our main goal is not to find the best classifier performance but to perform feature and channel investigation in MI task classification. Therefore, the detailed investigation of the effect of EEG channels and features is implemented using a statistically significant feature distribution on 22 EEG channels for each feature set separately. We used the BCI Competition IV Dataset IIa and 288 samples per person. A total of 1,364 MI-EEG features were analyzed in this study. We tested nine distinct classifiers: Decision tree, Discriminant analysis, Logistic regression, Naive Bayes, Support vector machine, k-Nearest neighbor, Ensemble learning, Neural networks, and Kernel approximation.Results Among all feature sets considered, classifications performed with non-linear and combined feature sets resulted in a maximum accuracy of 63.04% and 47.36% for binary and multiple MI task predictions, respectively. The ensemble learning classifier achieved the maximum accuracy in almost all feature sets for binary and multiple MI task classifications.Discussion Our research thus shows that the statistically significant feature-based feature selection method significantly improves the classification performance with fewer features in almost all feature sets, enabling detailed and effective EEG channel and feature investigation.
EEG-based finger movement classification with intrinsic time-scale decomposition
(Frontiers Media Sa, 2024) Degirmenci, Murside; Yuce, Yilmaz Kemal; Perc, Matjaz; Isler, Yalcin
Introduction Brain-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.Methods In 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.Results As 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.Discussion When 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).
Estimating Cognitive Load in a Mobile Personal Health Record Application: A Cognitive Task Analysis Approach
(Korean Soc Medical Informatics, 2023) Zayim, Nese; Yildiz, Hasibe; Yuce, Yilmaz Kemal
Objectives: Mobile health applications that are designed without considering usability criteria can lead to cognitive overload, resulting in the rejection of these apps. To avoid this problem, the user interface of mobile health applications should be evaluated for cognitive load. This evaluation can contribute to the improvement of the user interface and help prevent cognitive overload for the user. Methods: In this study, we evaluated a mobile personal health records application using the cognitive task analysis method, specifically the goals, operators, methods, and selection rules (GOMS) approach, along with the related updated GOMS model and gesture-level model techniques. The GOMS method allowed us to determine the steps of the tasks and categorize them as physical or cognitive tasks. We then estimated the completion times of these tasks using the updated GOMS model and gesture-level model. Results: All 10 identified tasks were split into 398 steps consisting of mental and physical operators. The time to complete all the tasks was 5.70 minutes and 5.45 minutes according to the updated GOMS model and gesture-level model, respectively. Mental operators covered 73% of the total fulfillment time of the tasks according to the updated GOMS model and 76% according to the gesture-level model. The inter-rater reliability analysis yielded an average of 0.80, indicating good reliability for the evaluation method. Conclusions: The majority of the task execution times comprised mental operators, suggesting that the cognitive load on users is high. To enhance the application's implementation, the number of mental operators should be reduced.
İstatistiksel anlamlı zaman alanı EEG özniteliklerinden el parmak hareketlerinin sınıflandırılması
(2024) Değirmenci, Mürşide; Yuce, Yilmaz Kemal; Isler, Yalcin
Motor Hayali Elektroensefalogram (EEG) sinyalleri, Beyin-Bilgisayar Arayüzlerinde (BBA) yaygın olarak kullanılmaktadır. Son yıllarda, büyük uzuv hareketlerinin motor hayali EEG sinyalleri, çeşitli makine öğrenme yaklaşımları kullanılarak sınıflandırılmaya çalışılmıştır. Ancak, hayali parmak hareketlerinin EEG sinyallerinin sınıflandırılması, parmak hareketlerinin ayırt edilmesini zorlaştıran daha küçük ve gürültülü sinyal özelliklerinden dolayı daha az sıklıkla analiz edilmektedir. Bu çalışma, hayali parmak hareketlerinin (Başparmak, İşaret parmağı, Orta parmak, Yüzük parmağı, Serçe parmak) ve hayali olmayan görev durumunun (NoMT) sınıflandırılması için EEG sinyal temsillerinin istatistiksel olarak anlamlı zaman alanı özniteliklerine dayalı olduğu bir yöntem önermektedir. 8 sağlıklı deneğin 21 EEG kanalından 24 farklı zaman alanı özniteliği çıkarılmaktadır. Önemli ve ilgili zaman alanı özniteliklerini belirlemek için istatistiksel anlamlılığa (ANOVA) dayalı özellik seçim yöntemi ve Temel Bileşen Analizi (TBA) kullanılmaktadır. Bu çalışma, istatistiksel olarak anlamlı özniteklilerin etkili analizi için 4 farklı yaklaşımı araştırmaktadır. Bunlar (i) tüm zaman alanı özniteliklerini, (ii) PCA tabanlı belirlenmiş temel zaman alanı bileşenlerini, (iii) ANOVA tabanlı belirlenmiş olan istatistiksel olarak anlamlı zaman alanı özniteliklerini ve (iv) ANOVA tabanlı belirlenmiş istatistiksel olarak anlamlı zaman alanı özelliklerinden PCA tabanlı belirlenmiş temel zaman alanı bileşenlerini kullanan yaklaşımlardır. Farklı parametrelere sahip sekiz farklı tipik sınıflandırıcı, 5-kat çapraz doğrulama kullanılarak 6 grubu sınıflandırmak için hesaplanmıştır. Önerilen yöntemler hem denek bağımlı hem de denek bağımsız koşullar için incelenmiştir. Sonuçlar, istatistiksel anlamlılığa dayalı öznitelik seçim yönteminin TBA tabanlı öznitelik seçimine kıyasla daha iyi performans verdiğini göstermektedir. Denekten bağımsız analizde, istatistiksel olarak anlamlı zaman alanı öznitelikleri ve Destek Vektör Makinesi (SVM) algoritması kullanılarak en yüksek eğitim doğrulama doğruluğu ve test doğruluğu değerleri %37,8 ve %35,8 olarak hesaplanmıştır. Deneğe bağlı analizlerde istatistiksel olarak anlamlı zaman alanı öznitelikleri ve DVM kullanılarak 8 kişinin en yüksek eğitim doğruluk değerleri %27,7-%53,0 olarak hesaplanmıştır ve 8 kişinin test doğruluk değerleri %33,3-%57,5 olarak hesaplanmıştır. Çalışma sonucunda, denek bağımlı sınıflandırmaların performansları denek bağımsız sınıflamalara göre daha yüksektir. Deneğe bağlı bu en yüksek sonuçlar, gelecek zamanda kişiselleştirilmiş el protezlerinin tasarımı çalışmalarında EEG tabanlı BBA sistemlerinin tasarımı için ümit vericidir.
Motor imaginary task classification using statistically significant time-domain EEG features
(Ieee, 2022) Degirmenci, Murside; Yuce, Yilmaz Kemal; Isler, Yalcin
Motor imaginary (MI) task classification based on electroencephalogram (EEG) signals is among the most common brain-computer interface (BCI) studies. One of the most widely used open-access datasets for this purpose is BCI Competition IV Dataset-IIa. In this data set, there are EEG data recorded during MI movements of the left hand, right hand, foot and tongue. In this study, four MI tasks were tried to be differentiated with the classifiers that are frequently used in the literature utilizing these 22-channel EEG data from a total of nine subjects. In addition, the effect of selecting statistically significant features from the features extracted on the dataset was investigated. These feature sets were differentiated using 11 different classification algorithms and 5-fold cross-validation. Each algorithm was tested 10 times to analyzed the repeatability of the results. As a result, classifier performances of %44.38 were obtained in the Ensemble classification Subspace Discriminant algorithm using all time-domain EEG features and %44.00 in the Linear Discriminant Analysis algorithm using only the features selected by ANOVA. Although the highest classifier performance seems to have decreased, it was observed that the feature selection process with ANOVA increased the performance in 6 classifiers, did not change the performance in 1 classifier, and decreased the performance in 4 classifiers. Accordingly, it was concluded that the method of selecting statistically significant features generally increased the classifier performance, but it was difficult to reach a general decision.
Principal component analysis and manifold learning techniques for the design of brain-computer interfaces based on steady-state visually evoked potentials
(Elsevier, 2023) Yesilkaya, Bartu; Sayilgan, Ebru; Yuce, Yilmaz Kemal; Perc, Matjaz; Isler, Yalcin
Steady-state visually evoked potentials (SSVEP) are stochastic and nonstationary bioelectric signals. Because of these properties, it is difficult to achieve high classification accuracy, especially when many considered features lead to a complex structure. We therefore propose a manifold learning framework to decrease the number of features and to classify SSVEP data by comparing lower dimensional matrices with well-known machine learning algorithms. We use the AVI-SSVEP Dataset, which includes stimuli at seven different frequencies and 15360 samples per person. The SSVEP features are extracted from relevant and distinctive frequency -domain, time-domain, and time-frequency domain properties, creating a total of 55 feature vectors. We then analyze and compare five divergent manifold learning methods with respect to their performance on nine different machine-learning algorithms. Among all considered manifold learning methods, we show that the Principal Component Analysis has the best classifier performance with an average of 22 components. Moreover, the Naive Bayes classifier with the Principal Component Analysis achieves the maximum accuracy of 50.0%-80.95% for a 7-class classification problem. Our research thus shows that the proposed analytical framework can significantly improve the decoding accuracy of 7-class SSVEP problems, and that it exhibits notable robustness and efficiency for small group datasets.
Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs
(Frontiers Media Sa, 2023) Degirmenci, Murside; Yuce, Yilmaz Kemal; Perc, Matjaz; Isler, Yalcin
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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