A study on the effect of psychophysiological signal features on classification methods

Erkan, Erdem; Kurnaz, İsmail

doi:10.1016/j.measurement.2017.01.019

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…The signal to noise ratio of ECoG is higher than EEG, which may be another cause of higher accuracy of all studies in Table 4. Erkan & Kurnaz (2017) proposed an arc detection algorithm (ADA) to select the optimum channel. The features in terms of discrete wavelet transform (DWT) were extracted and then classified with the accuracy of 95%.…”

Section: Discussionmentioning

confidence: 99%

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

Rashid

Bari

Hasan

et al. 2021

PeerJ Computer Science

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Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier’s performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k-nearest neighbour (k-NN) has been proposed to classify the motor imagery (MI) data. The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. In order to evaluate the efficacy of the proposed method, an experimental study has been implemented using four publicly available MI dataset (BCI Competition III dataset 1 (data-1), dataset IIIA (data-2), dataset IVA (data-3) and BCI Competition IV dataset II (data-4)). It was shown that the ensemble-based random subspace k-NN approach achieved the superior classification accuracy (CA) of 99.21%, 93.19%, 93.57% and 90.32% for data-1, data-2, data-3 and data-4, respectively against other models evaluated, namely linear discriminant analysis, support vector machine, random forest, Naïve Bayes and the conventional k-NN. In comparison with other classification approaches reported in the recent studies, the proposed method enhanced the accuracy by 2.09% for data-1, 1.29% for data-2, 4.95% for data-3 and 5.71% for data-4, respectively. Moreover, it is worth highlighting that the RF feature selection technique employed in the present study was able to significantly reduce the feature dimension without compromising the overall CA. The outcome from the present study implies that the proposed method may significantly enhance the accuracy of MI data classification.

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Section: Discussionmentioning

confidence: 99%

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

Rashid

Bari

Hasan

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…The main purpose of classification is to clean the unnecessary data and make the most effective classification with the most optimal number of features. It is also important to determine the effective channel subset in multi channel signals such as EEG [10,11]. Each channel determined by channel selection increases the feature size by one fold.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Effect of Features Obtained From Signal Segments on Classification Success

Erkan¹,

Erkan²

2022

European Journal of Science and Technology

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Successful classification depends on the selection of the distinctive features and the effective channel subset used in the classification. In this study, novel and practical methods are proposed for determining the distinctive features and detecting effective channel subsets in the multi channel classification systems such as EEG. Two different feature extraction methods are compared in the study. The first one is based on classical Wavelet transform and the second is our proposed approach which used the slope of signal segments. Feature vectors are generated from some signal properties such as the mean, standard deviation, numerical integral of the Wavelet coefficients for classical Wavelet transform based feature extraction method. For our proposed method, only the slopes of signal segments are used for the feature vectors. In the proposed Signal Path Slope (SPS) feature extraction method, differently from the classical Wavelet based method, a Savitzky Golay (S-G) filter with an optimal frame length is applied to the signal before segmentation to make the path of the signal more prominent in time domain. In this way, the distinctive classification features are extracted by using S-G filter. For channel selection, an iterative channel selection method based on the classification results which divide the dataset labelled dataset into two groups as % 90 pre-training and %10 pre-test data is proposed. The dataset provided as dataset-3 in BCI competition IV is used in this study. The feature vectors extracted by using the proposed methods are classified for each method with the Support Vector Machine classifier. The results are given comparatively and it is observed that our proposed method has less computational complexity and more successful classification than Wavelet based classical feature extraction methods. The highest classification accuracies of % 67.74 and % 49.27 for subject-1 and subject-2 respectively are obtained with a low dimensional feature vector by proposed SPS feature extraction method. The classification accuracies achieved in the study are increased by % 8.24 for subject-1 and % 14.97 for subject-2 when compared average of the competition results. The significant increase in the success for both subjects shows the consistency of the proposed methods. By this study, it is observed that there is a subject-specific signal pattern related to motor imagery tasks in the brain. This pattern distinctive features is successfully determined by using the proposed methods.

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“…According to many works of literature, the strength of a BCI system depends upon the methods in which the brain signals are translated into control commands of machines. A novel method namely an arc detection algorithm to find an optimal channel was proposed by Erdem Ekran and Ismail Kurnaz [4]. For feature extraction DWT was used and a number of machine learning algorithms were used for classification purposes, which were SVM, K-nearest neighbor, and Linear Discriminant Analysis.…”

Section: Introductionmentioning

confidence: 99%

A Study of the Classification of Motor Imagery Signals using Machine Learning Tools

Hashmi¹,

Khan²,

Farooq³

2021

Web, Internet Engineering &Amp; Signal Processing

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In this paper, we propose a system for the purpose of classifying Electroencephalography (EEG) signals associated with imagined movement of right hand and relaxation state using machine learning algorithm namely Random Forest Algorithm. The EEG dataset used in this research was created by the University of Tubingen, Germany. EEG signals associated with the imagined movement of right hand and relaxation state were processed using wavelet transform analysis with Daubechies orthogonal wavelet as the mother wavelet. After the wavelet transform analysis, eight features were extracted. Subsequently, a feature selection method based on Random Forest Algorithm was employed giving us the best features out of the eight proposed features. The feature selection stage was followed by classification stage in which eight different models combining the different features based on their importance were constructed. The optimum classification performance of 85.41% was achieved with the Random Forest classifier. This research shows that this system of classification of motor movements can be used in a Brain Computer Interface system (BCI) to mentally control a robotic device or an exoskeleton.

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A study on the effect of psychophysiological signal features on classification methods

Cited by 7 publications

References 9 publications

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

A Study on the Effect of Features Obtained From Signal Segments on Classification Success

A Study of the Classification of Motor Imagery Signals using Machine Learning Tools

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