Motor Imagery Data Research Articles

Two‐phase classification: ANN and A‐SVM classifiers on motor imagery BCI

AbstractBrain–Computer Interfaces (BCIs) based on Electroencephalograms (EEG) monitor mental activity with the ultimate objective of allowing people to communicate with computers only via their thoughts. Users must create precise cerebral activity patterns that the system uses as control signals to do this. A common activity used to elicit such signals is Motor Imagery (MI), in which certain signals are created in the sensorimotor cortex while imagining the movements. The three phases of the traditional EEG–BCI processing pipeline are preprocessing, feature extraction, and classification. We provide categorization advances and track performance gains in 4‐class MI‐based BCIs. In this study, 4‐class MI events are produced via an illusory elevation of the left hand, right hand, feet, and tongue. Finally, a two‐phase classification technique is provided with ANN classifiers being used in the first phase to discriminate between different pair‐wise MI tasks. Secondly, an adaptive SVM classifier is used to assess the user's end task based on the weighted outputs of the classifiers. An adaptive classifier is one technique to maintain consistency in performance, reduce training time, and eliminate non‐stationaries, all of which are required for efficient BCI performance. The suggested approach outperformed conventional two‐stage classification algorithms on MI data, according to experimental findings. The average classification accuracy of this technique is 96% for datasets BCI competition IV 2a. This is a 4% improvement over the comparison approach.

Analysis of the Relationship Between Motor Imagery and Age-Related Fatigue for CNN Classification of the EEG Data.

BackgroundThe aging of the world population poses a major health challenge, and brain–computer interface (BCI) technology has the potential to provide assistance and rehabilitation for the elderly.ObjectivesThis study aimed to investigate the electroencephalogram (EEG) characteristics during motor imagery by comparing young and elderly, and study Convolutional Neural Networks (CNNs) classification for the elderly population in terms of fatigue analysis in both frontal and parietal regions.MethodsA total of 20 healthy individuals participated in the study, including 10 young and 10 older adults. All participants completed the left- and right-hand motor imagery experiment. The energy changes in the motor imagery process were analyzed using time–frequency graphs and quantified event-related desynchronization (ERD) values. The fatigue level of the motor imagery was assessed by two indicators: (θ + α)/β and θ/β, and fatigue-sensitive channels were distinguished from the parietal region of the brain. Then, rhythm entropy was introduced to analyze the complexity of the cognitive activity. The phase-lock values related to the parietal and frontal lobes were calculated, and their temporal synchronization was discussed. Finally, the motor imagery EEG data was classified by CNNs, and the accuracy was discussed based on the analysis results.ResultFor the young and elderly, ERD was observed in C3 and C4 channels, and their fatigue-sensitive channels in the parietal region were slightly different. During the experiment, the rhythm entropy of the frontal lobe showed a decreasing trend with time for most of the young subjects, while there was an increasing trend for most of the older ones. Using the CNN classification method, the elderly achieved around 70% of the average classification accuracy, which is almost the same for the young adults.ConclusionCompared with the young adults, the elderly are less affected by the level of cognitive fatigue during motor imagery, but the classification accuracy of motor imagery data in the elderly may be slightly lower than that in young persons. At the same time, the deep learning method also provides a potentially feasible option for the application of motor-imagery BCI (MI-BCI) in the elderly by considering the ERD and fatigue phenomenon together.

A Discriminative and Robust Feature Learning Approach for EEG-Based Motor Imagery Decoding (Student Abstract)

Convolutional neural networks (CNNs) have been commonly applied in the area of the Electroencephalography (EEG)-based Motor Imagery (MI) classification, significantly pushing the boundary of the state-of-the-art. In order to simultaneously decode the discriminative features and eliminate the negative effects of non-Gaussian noise and outliers in the motor imagery data, in this abstract, we propose a novel robust supervision signal, called Correntropy based Center Loss (CCL), for CNN training, which utilizes the correntropy induced distance as the objective measure. It is encouraging to see that the CNN model trained by the combination of softmax loss and CCL loss outperforms the state-of-the-art models on two public datasets.

The unilateral upper limb classification from fMRI-weighted EEG signals using convolutional neural network

BackgroundUnilateral upper limb multitasking brings essential improvements to stroke rehabilitation and prosthetic control. However, the influence and recognition of multiple tasks are core issues in the Motor Imagery (MI) system. MethodsFirst, we design the unilateral upper limb MI experimental paradigm and acquire asynchronous functional Magnetic Resonance Imaging (fMRI) and Electroencephalogram based on MI (MI-EEG) data. Then, Brain activation areas for each task are statistically analyzed by fMRI data. A novel fMRI-weighted Convolutional Neural Network (CNN) is designed to reassign each channel’s weight based on brain activation areas to improve classification accuracy. Finally, the EEG data is classified by the fMRI-weighted CNN. ResultsThe four classes of MI tasks reflect significant activation in brain motor areas. The average classification accuracy of fMRI-weighted CNN is 47.0%. The visualization results show the similarity between fMRI and EEG activation areas in the same task. ConclusionsThe distinguishability of multiple tasks and the influence on motor areas of the brain are confirmed by fMRI experiments. The classification accuracy of multitasks is improved according to the fMRI-weighted CNN. This paper provides a reference for further research into asynchronous fMRI-EEG modeling and multitask MI classification.

Overall optimization of CSP based on ensemble learning for motor imagery EEG decoding

The common spatial pattern (CSP) is an effective feature extraction method in motor imagery-based brain-computer interface (BCI) system. However, CSP also has many defects. Existing CSP improvement methods only make partial improvements, without considering the overall optimization of CSP. In this paper, a new ensemble learning algorithm framework is proposed to improve the decoding performance of CSP, in which the regularization, temporal-spatial-frequency joint optimization, and pair number of spatial filters for CSP are comprehensively considered. First, a new temporal-spatial-frequency feature extraction method based on Tikhonov regularization CSP (TRCSP) is proposed, multiple feature subsets with diversity are extracted by TRCSP with different time windows, regularization parameters, and pair numbers of spatial filters. Second, the least absolute shrinkage and selection operator (LASSO) as base classification model is used for feature selection and classification, in which multiple diversified base classification models are trained. Finally, the base classification models with diversity and higher accuracy are used for ensemble model construction using a new integration rule, during which most of the temporal-spatial-frequency information is fully excavated and utilized. The effectiveness of the proposed method is verified by five motor imagery data sets and the average classification accuracy of all data sets is 85.99%. Compared with the existing CSP methods, the proposed method achieved a better classification effect, and with a small amount of calculation, low model complexity, and high robustness.

Reducing calibration time in motor imagery-based BCIs by data alignment and empirical mode decomposition.

One of the major reasons that limit the practical applications of a brain-computer interface (BCI) is its long calibration time. In this paper, we propose a novel approach to reducing the calibration time of motor imagery (MI)-based BCIs without sacrificing classification accuracy. The approach aims to augment the training set size of a new subject by generating artificial electroencephalogram (EEG) data from a few training trials initially available. The artificial EEG data are obtained by first performing empirical mode decomposition (EMD) and then mixing resulting intrinsic mode functions (IMFs). The original training trials are aligned to common reference point with Euclidean alignment (EA) method prior to EMD and pooled together with artificial trials as the expended training set, which is input into a linear discriminant analysis (LDA) classifier or a logistic regression (LR) classifier. The performance of the proposed algorithm is evaluated on two motor imagery (MI) data sets and compared with that of the algorithm trained with only real EEG data (Baseline) and the algorithm trained with expanded EEG data by EMD without data alignment. The experimental results showed that the proposed algorithm can significantly reduce the amount of training data needed to achieve a given performance level and thus is expected to facilitate the real-world applications of MI-based BCIs.

Constructing Time-Varying Directed EEG Network by Multivariate Nonparametric Dynamical Granger Causality.

Time-varying directed electroencephalography (EEG) network is the potential tool for studying the dynamical causality among brain areas at a millisecond level; which conduces to understanding how our brain effectively adapts to information processing, giving inspiration to causality- and brain-inspired machine learning. Currently, its construction still mainly relies on the parametric approach such as multivariate adaptive autoregressive (MVAAR), represented by the most widely used adaptive directed transfer function (ADTF). Restricted by the model assumption, the corresponding performance largely depends on the MVAAR modeling which usually encounters difficulty in fitting complex spectral features. In this study, we proposed to construct EEG directed network with multivariate nonparametric dynamical Granger causality (mndGC) method that infers the causality of a network, instead, in a data-driven way directly and therefore avoids the trap in the model-dependent parametric approach. Comparisons between mndGC and ADTF were conducted both with simulation and real data application. Simulation study demonstrated the superiority of mndGC both in noise resistance and capturing the instantaneous directed network changes. When applying to the real motor imagery (MI) data set, distinguishable network characters between left- and right-hand MI during different MI stages were better revealed by mndGC. Our study extends the nonparametric causality exploration and provides practical suggestions for the time-varying directed EEG network analysis.

EEG Classification of Imaginary Left and Right Leg Movements based on Multivariate Empirical Mode Decomposition and Non-Linear Support Vector Machine

Motor-imagery brain-computer interfaces, as rehabilitation tools for motor-disabled individuals, could inherently enrich neuroplasticity and subsequently restore mobility. However, this endeavour's significant challenge is classifying left and right leg motor imagery tasks from non-stationary EEG signals. A subject-independent feature extraction method is essential in a BCI system, and this work involves developing a subject-independent algorithm to classify left/right leg motion intention. The Multivariate Empirical Mode Decomposition was used to decompose EEG during left and right foot movements during imagery tasks. We validated our proposed algorithm using open-access motor imagery data to detect the user's mental intention from EEG. Five subjects of various performance categories with almost 150 trials for each left/right leg MI of hand/leg/tongue, HaLT Paradigm, utilizing C3, C4, and Cz channels were examined to generalize this study to all subjects. A set of statistical features were extracted from the intrinsic mode functions, and the most relevant features were selected for classification using Sequential Floating Feature Selection. Different classifiers were trained using extracted features, and their performances' were evaluated. The findings suggest that the non-linear support vector machine is the best classification model, resulting in the mean classification sensitivity, specificity, precision, negative predictive value, F-measure, 98.15%, 90.74%, 91.97%, 98.33%, 94.72%, 94.44%, respectively. The proposed subject-independent signal processing method significantly improved the offline calibration mode by eliminating the frequency selection step, making it the common-used method for different types of MI-based BCI participants. Offline evaluations suggest that it can lead to significant increases in classification accuracy in comparison to current approaches.

Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold.

The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (, , and ) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu’s version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.

Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network.

Motor imagery (MI)-based brain–computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain’s neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms—an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain–computer interfaces.

Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review.

The pandemic emergency of the coronavirus disease 2019 (COVID-19) shed light on the need for innovative aids, devices, and assistive technologies to enable people with severe disabilities to live their daily lives. EEG-based Brain-Computer Interfaces (BCIs) can lead individuals with significant health challenges to improve their independence, facilitate participation in activities, thus enhancing overall well-being and preventing impairments. This systematic review provides state-of-the-art applications of EEG-based BCIs, particularly those using motor-imagery (MI) data, to wheelchair control and movement. It presents a thorough examination of the different studies conducted since 2010, focusing on the algorithm analysis, features extraction, features selection, and classification techniques used as well as on wheelchair components and performance evaluation. The results provided in this paper could highlight the limitations of current biomedical instrumentations applied to people with severe disabilities and bring focus to innovative research topics.

Component-mixing strategy: A decomposition-based data augmentation algorithm for motor imagery signals

Deep learning has achieved a remarkable success in areas such as brain-computer interface systems (BCI). However, electroencephalography (EEG) signals evoked by motor imagery (MI) are sometimes limited in their amount due to invalid data caused by the subjects’ fatigue, leading to a performance degradation. To this end, in this work we extend empirical mode decomposition into multivariate empirical mode decomposition and intrinsic time-scale decomposition, proposing a component-mixing strategy (CMS) for MI data augmentation. Compared to commonly used data augmentation methods such as generative adversarial networks, CMS can generate artificial trials from a few training samples without any required training. We claim that raw and artificial data generated by CMS are consistent with respect to the distribution and power spectral density. Experiments done on the BCI Competition IV dataset 2b show that CMS can achieve a considerable improvement on the binary classification accuracy and the area under the curve score using EEGNet, wavelet neural networks and a support vector machine.

Sparse learning of band power features with genetic channel selection for effective classification of EEG signals

In this paper, we present a genetic algorithm (GA) based band power feature sparse learning (SL) approach for classification of electroencephalogram (EEG) (GABSLEEG) in motor imagery (MI) based brain-computer interfacing (BCI). The band power in the alpha and beta bands was extracted from the EEG segments and used as features to construct the SL dictionary, in which the GA was employed for channel selection. The GABSLEEG system was tested in three functional areas: i) classification of MI data and idle state data; ii) performance with decreased training data size; and iii) computational efficiency. The system was evaluated by dividing the data into training, validation, and testing sets. The proposed GABSLEEG model is found to significantly outperform conventional classifiers, including the support vector machine (SVM) classifier in (i-iii), and the random forest (RF) and the k-nearest neighbour (k-NN) classifiers in (i-ii). The GABSLEEG system consistently had a higher classification accuracy, sensitivity, and specificity. The average accuracy of the proposed system was 99.65%, on BCI Competition IV dataset 1 and 96.08% for BCI Competition III dataset IVa with the idle state included as a class, which was on a par with state-of-the-art SL and even deep learning approaches.

Ensemble Regularized Common Spatio-Spectral Pattern (ensemble RCSSP) model for motor imagery-based EEG signal classification

The Brain-Computer interface system provides a communication path among the brain and computer, and recently, it is the subject of increasing attention. One of the most common paradigms of BCI systems is motor imagery. Currently, to classify motor imagery EEG signals, Common Spatial Patterns (CSP) are extensively used. Generally, the recorded motor imagery EEG signals in BCI are noisy, non-stationary, thus significantly reducing the BCI system's performance. It is shown that the CSP algorithm has a good performance in the classification of various types of motor imagery data. However, once the number of trials is low, or the data are noisy, overfitting will probably occur, which precludes extracting an appropriate spatial filter. Another drawback of the CSP is that it only extracts spatial-based filters. Therefore, the current study attempts to decrease the probability of overfitting in the CSP algorithm by presenting an improved method called Ensemble Regularized Common Spatio-Spectral Pattern (Ensemble RCSSP). Compared with other CSP and improved versions of CSP algorithms, our proposed models indicate a better accuracy, robustness, and reliability for motor imagery EEG data. The performance of the proposed Ensemble RCSSP has been tested for BCI Competition IV, Dataset 1, and BCI Competition III, Dataset Iva. Compared with other methods, performance is improved, and on average, the accuracy for all subjects is reached to 82.64% and 86.91% for the first and second datasets, respectively.

Combining generative adversarial networks and multi-output CNN for motor imagery classification

Objective. Motor imagery (MI) classification is an important task in the brain–computer interface (BCI) field. MI data exhibit highly dynamic characteristics and are difficult to obtain. Therefore, the performance of the classification model will be challenged. Recently, convolutional neural networks (CNNs) have been employed for MI classification and have demonstrated favorable performances. However, the traditional CNN model uses an end-to-end output method, and part of the feature information is discarded during the transmission process. Approach. Herein, we propose a novel algorithm, that is, a combined long short-term memory generative adversarial networks (LGANs) and multi-output convolutional neural network (MoCNN) for MI classification, and an attention network for improving model performance. Specifically, the proposed method comprises three steps. First, MI data are obtained, and preprocessing is performed. Second, additional data are generated for training. Here, a data augmentation method based on a LGAN is designed. Last, the MoCNN is proposed to improve the classification performance. Main results. The BCI competition IV datasets 2a and 2b are employed to evaluate the performance of the proposed method. With multiple evaluation indicators, the proposed generative model can generate more realistic data. The expanded training set improves the performance of the classification model. Significance. The results show that the proposed method improves the classification of MI data, which facilitates motion imagination.

A binary harmony search algorithm as channel selection method for motor imagery-based BCI

BackgroundChannel selection is a key topic in brain-computer interface (BCI). Task-irrelevant and redundant channels used in BCI may lead to low classification accuracy, high computational complexity, and inconvenience for application. By selecting optimal channels, the performance of BCI could enhance significantly. MethodIn this paper, a new binary harmony search (BHS) is proposed to select the optimal channel sets and optimize the system accuracy. The BHS is implemented on the training data sets to select the optimal channels and the test data sets are used to evaluate the classification performance on the selected channels. The sparse representation-based classification, linear discriminant analysis, and support vector machine are performed on the common spatial pattern (CSP) features for motor imagery (MI) classification. ResultsTwo public EEG datasets are employed to validate the proposed BHS method. The paired t-test is conducted on the test classification performance between the BHS and traditional CSP with all channels. The results reveal that the proposed BHS method significantly improved classification accuracy as compared to the conventional CSP method (p < 0.05). ConclusionThis study proposed the BHS method to select the optimal channels in MI -based BCI. On the one hand, the results confirm the validity of the BHS algorithm as a channel selection method for motor imagery data. On the other hand, the BHS method with costing shorter computation time relatively yields a better average test accuracy than the steady-state genetic algorithms. The proposed method could significantly improve the practicability and convenience of the BCI system.

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

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.

An improved version of local activities estimation to enhance motor imagery classification

ObjectiveThe common spatial pattern (CSP) and its variants are popular in the EEG-based motor imagery BCIs. However, this method has some drawbacks, especially when a few labeled samples are available. The local activities estimation (LAE) method works well with small training sets, but it is more sensitive to the position of the electrodes. Here, we suggest a combination of the LAE and CSP, namely LAE-CSP, which performs better than both methods. MethodsIn LAE-CSP, the EEG signal passes through regularized CSP and LAE spatial filters after band-pass filtering and then the data dimension are reduced based on the physiological data. Afterwards, the features are extracted using fast Fourier transform (FFT). In this work, CSP, FBRCSP, EA-CSP, LAE and LAE-CSP, methods were evaluated and compared. Three sets of motor imagery data from BCI competition III and IV along with Cho et al. dataset, including EEG signals from 28 subjects were used in this study. For each dataset, the training set were selected in 21 different sizes. ResultsLAE-CSP performs better than all tested methods. Particularly it has good performance using only ten labeled samples per class, with an average accuracy of almost 80 %. ConclusionsLAE-CSP reliably enhances the performance of the motor imagery-based brain-computer interfaces. SignificanceThe LAE-CSP takes advantage of the LAE and CSP and compensates for the drawbacks of both methods.

Enhanced Accuracy for Motor Imagery Detection Using Deep Learning for BCI

Brain-Computer Interface (BCI) is a system that provides a link between the brain of humans and the hardware directly. The recorded brain data is converted directly to the machine that can be used to control external devices. There are four major components of the BCI system: acquiring signals, preprocessing of acquired signals, features extraction, and classification. In traditional machine learning algorithms, the accuracy is insignificant and not up to the mark for the classification of multi-class motor imagery data. The major reason for this is, features are selected manually, and we are not able to get those features that give higher accuracy results. In this study, motor imagery (MI) signals have been classified using different deep learning algorithms. We have explored two different methods: Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). We test the classification accuracy on two datasets: BCI competition III-dataset IIIa and BCI competition IV-dataset IIa. The outcome proved that deep learning algorithms provide greater accuracy results than traditional machine learning algorithms. Amongst the deep learning classifiers, LSTM outperforms the ANN and gives higher classification accuracy of 96.2%.

A Matrix Determinant Feature Extraction Approach for Decoding Motor and Mental Imagery EEG in Subject-Specific Tasks

This study introduces a novel matrix determinant feature extraction approach for efficient classification of motor and mental imagery activities from electroencephalography (EEG) signals. First, the multiscale principal component analysis was utilized to obtain clean EEG signals. Second, denoised data were sequentially arranged to form a square matrix of different orders (e.g.,10, 13, 16, and 20) and determinant was computed for each order matrix. Finally, the extracted matrix determinant features were provided to several machine learning and neural network classification models for classification. All experiments were carried out using a 10-fold cross-validation approach on three publicly accessible data sets: 1) data set IV-a; 2) data set IV-b; and 3) data set V of BCI competition III. Also, this study designs a computerized automatic detection of motor and mental imagery graphical user interface that can assist physicians/experts to efficiently analyses motor and mental imagery data. The experimental results reveal that the highest average classification accuracy of 99.55% (for data set IV-a), 99.52% (for data set IV-b), and 91.80% (for data set V) was obtained for motor and mental imagery, respectively, with 20-order matrix determinant using a feedforward neural network classifier. The experimental results suggest that the proposed framework provides a robust biomarker with the least computational complexity for the development of automated brain–computer interfaces.