Relevant Feature Selection from a Combination of Spectral-Temporal and Spatial Features for Classification of Motor Imagery EEG

Kirar, Jyoti Singh; Agrawal, R. K.

doi:10.1007/s10916-018-0931-8

Cited by 41 publications

(10 citation statements)

References 35 publications

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“…Low computation requirement and easy implementation make discriminant analysis one of ideal classifiers for EEG based-BCIs 29 , 60 . In the discriminant analysis method, the boundary among classes is defined based on maximizing the ratio of inter-class variance and minimizing intra-class variance.…”

Section: Methodsmentioning

confidence: 99%

Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals

Salimpour

Kalbkhani

Seyyedi

et al. 2022

Sci Rep

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Over the past few years, the processing of motor imagery (MI) electroencephalography (EEG) signals has been attracted for developing brain-computer interface (BCI) applications, since feature extraction and classification of these signals are extremely difficult due to the inherent complexity and tendency to artifact properties of them. The BCI systems can provide a direct interaction pathway/channel between the brain and a peripheral device, hence the MI EEG-based BCI systems seem crucial to control external devices for patients suffering from motor disabilities. The current study presents a semi-supervised model based on three-stage feature extraction and machine learning algorithms for MI EEG signal classification in order to improve the classification accuracy with smaller number of deep features for distinguishing right- and left-hand MI tasks. Stockwell transform is employed at the first phase of the proposed feature extraction method to generate two-dimensional time–frequency maps (TFMs) from one-dimensional EEG signals. Next, the convolutional neural network (CNN) is applied to find deep feature sets from TFMs. Then, the semi-supervised discriminant analysis (SDA) is utilized to minimize the number of descriptors. Finally, the performance of five classifiers, including support vector machine, discriminant analysis, k-nearest neighbor, decision tree, random forest, and the fusion of them are compared. The hyperparameters of SDA and mentioned classifiers are optimized by Bayesian optimization to maximize the accuracy. The presented model is validated using BCI competition II dataset III and BCI competition IV dataset 2b. The performance metrics of the proposed method indicate its efficiency for classifying MI EEG signals.

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Section: Methodsmentioning

confidence: 99%

Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals

Salimpour

Kalbkhani

Seyyedi

et al. 2022

Sci Rep

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“…Wang et al introduced two Parzen window-based methods to select subject-specific time segments from 21 overlapping time window candidates [ 23 ]. Huang et al [ 24 ], Miao et al [ 6 ], and Kirar et al [ 25 ] introduced methods that simultaneously optimize time windows and frequency sub-bands within the CSP to improve the performance of MI classification. Jin et al designed a novel time filter that acted together with the spatial filter to introduce the temporal information in the spatial features, and discussed the effect of different lengths of time windows to obtain the optimal time segments [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Learning Optimal Time-Frequency-Spatial Features by the CiSSA-CSP Method for Motor Imagery EEG Classification

et al. 2022

Sensors

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The common spatial pattern (CSP) is a popular method in feature extraction for motor imagery (MI) electroencephalogram (EEG) classification in brain–computer interface (BCI) systems. However, combining temporal and spectral information in the CSP-based spatial features is still a challenging issue, which greatly affects the performance of MI-based BCI systems. Here, we propose a novel circulant singular spectrum analysis embedded CSP (CiSSA-CSP) method for learning the optimal time-frequency-spatial features to improve the MI classification accuracy. Specifically, raw EEG data are first segmented into multiple time segments and spectrum-specific sub-bands are further derived by CiSSA from each time segment in a set of non-overlapping filter bands. CSP features extracted from all time-frequency segments contain more sufficient time-frequency-spatial information. An experimental study was implemented on the publicly available EEG dataset (BCI Competition III dataset IVa) and a self-collected experimental EEG dataset to validate the effectiveness of the CiSSA-CSP method. Experimental results demonstrate that discriminative and robust features are extracted effectively. Compared with several state-of-the-art methods, the proposed method exhibited optimal accuracies of 96.6% and 95.2% on the public and experimental datasets, respectively, which confirms that it is a promising method for improving the performance of MI-based BCIs.

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“…The most important factor affecting the classification success is the determination of the distinctive features used in classification. In the literature, various mathematical procedures such as Wavelet transform [1][2][3], Fourier transform [4], autoregressive model [5] and common spatial pattern [6][7][8][9] have been used to determine the most efective distinctive features from the psychophysiological signals. The main purpose of classification is to clean the unnecessary data and make the most effective classification with the most optimal number of features.…”

Section: Introductionmentioning

confidence: 99%

“…Briefly, the success of the entire system is determined by the distinctive features, channel subset and preferred classifier method. Popular classification methods such as K-Nearest Neighbors (KNN) algorithm [12], Support Vector Machines (SVM) [13][14][15][16], Linear Discriminant Analysis (LDA) [6][7][8]17,18] and artificial neural networks [19][20][21][22][23][24] have been widely applied BCI classifications in the literature.…”

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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Relevant Feature Selection from a Combination of Spectral-Temporal and Spatial Features for Classification of Motor Imagery EEG

Cited by 41 publications

References 35 publications

Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals

Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals

Learning Optimal Time-Frequency-Spatial Features by the CiSSA-CSP Method for Motor Imagery EEG Classification

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

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