Classification Of Motor Imagery EEG Signals Research Articles

Motor Imagery EEG Classification Using Random Subspace Ensemble Network with Variable Length Features

Classification of electroencephalography (EEG) signals for brain-computer interface has great impact on people having various kinds of physical disabilities. Motor imagery EEG signals of hand and leg movement classification can help people whose limbs are replaced by prosthetics. In this paper, random subspace ensemble network with variable length feature sampling has been proposed for improving the prediction accuracy of motor imagery EEG signal classification. The method has been tested on eight different subjects and a hybrid dataset of two subjects data combined. Discrete wavelet transform based de-noising scheme has been adopted to remove artifacts from the EEG signal. For sub-band selection, dual-tree complex wavelet Transform has been employed. Mutual information scoring has been used for univariate feature selection from the feature space. A comparative analysis has been carried out where random subspace ensemble network outperformed other classification models. The maximum accuracy obtained by the model was 90.00%. Furthermore, the model showed better performance on the hybrid dataset with an average accuracy of 86.00%. The findings of this study are expected to be useful in artificial limb movements through brain-computer interfacing for rehabilitation of people with such physical disabilities.

Parallel Spatial-Temporal Self-Attention CNN-Based Motor Imagery Classification for BCI.

Motor imagery (MI) electroencephalography (EEG) classification is an important part of the brain-computer interface (BCI), allowing people with mobility problems to communicate with the outside world via assistive devices. However, EEG decoding is a challenging task because of its complexity, dynamic nature, and low signal-to-noise ratio. Designing an end-to-end framework that fully extracts the high-level features of EEG signals remains a challenge. In this study, we present a parallel spatial–temporal self-attention-based convolutional neural network for four-class MI EEG signal classification. This study is the first to define a new spatial-temporal representation of raw EEG signals that uses the self-attention mechanism to extract distinguishable spatial–temporal features. Specifically, we use the spatial self-attention module to capture the spatial dependencies between the channels of MI EEG signals. This module updates each channel by aggregating features over all channels with a weighted summation, thus improving the classification accuracy and eliminating the artifacts caused by manual channel selection. Furthermore, the temporal self-attention module encodes the global temporal information into features for each sampling time step, so that the high-level temporal features of the MI EEG signals can be extracted in the time domain. Quantitative analysis shows that our method outperforms state-of-the-art methods for intra-subject and inter-subject classification, demonstrating its robustness and effectiveness. In terms of qualitative analysis, we perform a visual inspection of the new spatial–temporal representation estimated from the learned architecture. Finally, the proposed method is employed to realize control of drones based on EEG signal, verifying its feasibility in real-time applications.

Multiclass Classification of Spatially Filtered Motor Imagery EEG Signals Using Convolutional Neural Network for BCI Based Applications

Brain–Computer Interface (BCI) system offers a new means of communication for those with paralysis or severe neuromuscular disorders. BCI systems based on Motor Imagery (MI) Electroencephalography (EEG) signals enable the user to convert their thoughts into actions without any voluntary muscle movement. Recently, Convolutional neural network (CNN) is used for the classification of MI signals. However, to produce good MI classification, it is necessary to effectively represent the signal as an input image to the CNN and train the deep learning classifier using large training data. In this work, EEG signals are acquired over 16 channels and are filtered using a bandpass filter with the frequency range of 1 to 100 Hz. The processed signal is spatially filtered using Common Spatial Pattern (CSP) filter. The spectrograms of the spatially filtered signals are given as input to CNN. A single convolutional layer CNN is designed to classify left hand, right hand, both hands, and feet MI EEG signals. The size of the training data is increased by augmenting the spectrograms of the EEG signals. The CNN classifier was evaluated using MI signals acquired from twelve healthy subjects. Results show that the proposed method achieved an average classification accuracy of 95.18 ± 2.51% for two-class (left hand and right hand) and 87.37 ± 1.68% for four-class (Left hand, Right hand, Both hands, and Feet) MI. Thus, the method manifests that this 2D representation of 1D EEG signal along with image augmentation shows a high potential for classification of MI EEG signals using the designed CNN model.

Classification of Non-Discriminant ERD/ERS Comprising Motor Imagery Electroencephalography Signals

Classification of Motor Imagery (MI) Electroencephalography (EEG) signals has always been an important aspect of Brain Computer Interface (BCI) systems. Event Related Desynchronization (ERD)/ Event Related Synchronization (ERS) plays a significant role in finding discriminant features of MI EEG signals. ERD/ERS is one type and Evoked Potential (EP) is another type of brain response. This study focuses upon the classification of MI EEG signals by Removing Evoked Potential (REP) from non-discriminant MI EEG data in filter band selection, called REP. This optimization is done to enhance the classification performance. A comprehensive comparison of several pipelines is presented by using famous feature extraction methods, namely Common Spatial Pattern (CSP), XDawn. The effectiveness of REP is demonstrated on the PhysioNet dataset which is an online data resource. Comparison is done between the performance of pipelines including proposed one (Common Spatial Pattern (CSP) and Gaussian Process Classifier (GPC)) as well as before and after applying REP. It is observed that the REP approach has improved the classification accuracy of all the subjects used as well as all the pipelines, including state of the art algorithms, up to 20%.

Orthogonal matching pursuit-based feature selection for motor-imagery EEG signal classification

This paper focuses on a framework that uses a small number of features to obtain high-quality classification accuracy of left/right-hand movement motor-imagery EEG signal. Motor-imagery EEG signal has been filtered, and suitable features are extracted using a temporal sliding window-based approach. These extracted features from overlapping and non-overlapping approaches are further compared based on three different types of feature extraction techniques: band power, wavelet energy entropy, and adaptive autoregressive model. The overlapping segments with wavelet energy entropy provide the best classification accuracy over other alternatives. The obtained classification accuracy is 91.43%, the highest ever reported accuracy for BCI Competition II data set III. Subsequently, Orthogonal Matching Pursuit (OMP) technique is used to select the subset of most discriminating features from the entire feature-set. It reduces the computation cost but still retains the quality of the classification results with only 1.43% information loss (that is, 90% classification accuracy), whereas the features-set size reduction is 75% for the same. It is found that the wavelet energy entropy technique performs consistently well in all the variants of our experiments and obtains a mean accuracy difference of 0.95% only.

Orthogonal matching pursuit-based feature selection for motor-imagery EEG signal classification

Orthogonal matching pursuit-based feature selection for motor-imagery EEG signal classification

Brain wave classification using long short-term memory network based OPTICAL predictor

Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of techniques have been proposed aiming to be able to classify brain waves with high accuracy. However, the ability to classify brain waves and its implementation in real-time is still limited. In this study, we introduce a novel scheme for classifying motor imagery (MI) tasks using electroencephalography (EEG) signal that can be implemented in real-time having high classification accuracy between different MI tasks. We propose a new predictor, OPTICAL, that uses a combination of common spatial pattern (CSP) and long short-term memory (LSTM) network for obtaining improved MI EEG signal classification. A sliding window approach is proposed to obtain the time-series input from the spatially filtered data, which becomes input to the LSTM network. Moreover, instead of using LSTM directly for classification, we use regression based output of the LSTM network as one of the features for classification. On the other hand, linear discriminant analysis (LDA) is used to reduce the dimensionality of the CSP variance based features. The features in the reduced dimensional plane after performing LDA are used as input to the support vector machine (SVM) classifier together with the regression based feature obtained from the LSTM network. The regression based feature further boosts the performance of the proposed OPTICAL predictor. OPTICAL showed significant improvement in the ability to accurately classify left and right-hand MI tasks on two publically available datasets. The improvements in the average misclassification rates are 3.09% and 2.07% for BCI Competition IV Dataset I and GigaDB dataset, respectively. The Matlab code is available at https://github.com/ShiuKumar/OPTICAL.

A novel machine learning based feature selection for motor imagery EEG signal classification in Internet of medical things environment

Abstract In Internet of Medical Things (IoMT) environment, feature selection is an efficient way of identifying the most discriminant health-related features from the original feature-set. Feature selection not only finds the best informative features, but also helps in reducing the overall dimensions of the given dataset. In this paper, the actual feature-set is obtained from Brain Computer Interface (BCI) Competition-II Dataset-III motor-imagery electroencephalogram (EEG) signal using the Adaptive Auto-regressive (AAR) feature extraction technique. Based on the order (number of AR coefficients) of the AAR algorithm, two variants of datasets have been generated: 12 ( o r d e r = 6 per electrode) and 24 ( o r d e r = 12 per electrode) AAR features datasets. Here, a new fuzzified version of discernibility matrix has been proposed to determine a subset of features, which provides the best classification accuracy. In order to find the best feature subset, various types of dissimilarity measures have been used and compared with one another in our proposed fuzzy discernibility matrix (FDM) based feature selection technique. We have implemented the proposed algorithm on the given datasets using both the holdout technique as well as the 10-fold cross-validation in our study. The performances of the selected feature-subsets are evaluated based on accuracies using the Support Vector Machine (SVM) and Ensemble variants of classifiers. The empirical results obtained from our experiments in this paper is competitive in terms of accuracy and outperformed the other popular t-test, Kullback–Leibler Divergence (KLD), Bhattacharyya distance and Gini index based feature selection techniques. Our proposed FDM based feature selection algorithm using holdout technique provides 80% and 78.57% accuracies for the 12 and 24 features AAR datasets respectively. The results obtained in the holdout technique with only 50% of the best discriminant features are even better than the performances obtained while using the original feature-sets (without using any feature selection technique). Again, it gives 78.57% and 75.57% mean-accuracies from 5 × 10-fold cross-validations using only 6and 12 most discriminant AAR features from the actual 12 & 24 features-sets respectively.

A Deep Transfer Convolutional Neural Network Framework for EEG Signal Classification

Nowadays, motor imagery (MI) electroencephalogram (EEG) signal classification has become a hotspot in the research field of brain computer interface (BCI). More recently, deep learning has emerged as a promising technique to automatically extract features of raw MI EEG signals and then classify them. However, deep learning-based methods still face two challenging problems in practical MI EEG signal classification applications: (1) Generally, training a deep learning model successfully needs a large amount of labeled data. However, most of the EEG signal data is unlabeled and it is quite difficult or even impossible for human experts to label all the signal samples manually. (2) It is extremely time-consuming and computationally expensive to train a deep learning model from scratch. To cope with these two challenges, a deep transfer convolutional neural network (CNN) framework based on VGG-16 is proposed for EEG signal classification. The proposed framework consists of a VGG-16 CNN model pre-trained on the ImageNet and a target CNN model which shares the same structure with VGG-16 except for the softmax output layer. The parameters of the pre-trained VGG-16 CNN model are directly transferred to the target CNN model used for MI EEG signal classification. Then, front-layers parameters in the target model are frozen, while later-layers parameters are fine-tuned by the target MI dataset. The target dataset is composed of time-frequency spectrum images of EEG signals. The performance of the proposed framework is verified on the public benchmark dataset 2b from the BCI competition IV. The experimental results show that the proposed framework improves the accuracy and efficiency performance of EEG signal classification compared with traditional methods, including support vector machine (SVM), artificial neural network (ANN), and standard CNN.

Channel space weighted fusion-oriented feature pyramid network for motor imagery EEG signal recognition

In order to solve the problems of weak generalization ability and low classification accuracy in motor imagery EEG signal classification, this paper proposes a channel space weighted fusion-oriented feature pyramid network for motor imagery EEG signal recognition. First, the shor