Convolutional neural network based on temporal-spatial feature learning for motor imagery electroencephalogram signal decoding

Traditional common spatial pattern (CSP) algorithms for upper limb neural rehabilitation face inherent challenges of overlapping cortical representations and frequency sensitivity, which hinder the decoding performance of motor imagery (MI) electroencephalogram (EEG) signals. To address these issues, this study adopts an improved discriminative filter bank CSP (DFBCSP) framework and applies it to the decoding of upper limb MI-EEG signals, achieving remarkable classification performance. EEG data were acquired from sixteen participants performing two-class (left upper limb flexion-extension vs. relaxing) and three-class (left upper limb flexion vs. right upper limb extension vs. relaxing) MI tasks. The acquired EEG data were then decomposed into nine distinct sub-bands, followed by the adoption of a mutual information-based feature selection strategy to optimize the feature sets. These optimized feature sets were subsequently input into three classification models, namely multilayer perceptron (MLP), support vector machine (SVM), and linear discriminant analysis (LDA), for MI task classification. Experimental results demonstrate that the DFBCSP + MLP method significantly outperforms the traditional CSP approach. Specifically, it achieves an accuracy of 94.83% (Kappa coefficient: 0.890) in two-class MI tasks and 86.20% (Kappa coefficient: 0.775) in three-class MI tasks. The DFBCSP + MLP framework exhibits high robustness and provides a potential technical framework and theoretical basis for future research on the rehabilitation of patients with upper limb motor dysfunction.

Motor Imagery (MI) based Brain Computer Inter-face (BCI) is a promising neurorehabilitation tool for treating motor impaired stroke survivors. It enables the MI electroencephalogram (EEG) signals to be converted/mapped into customized robotic and assisting commands. Even though stroke causes varying effects in the brain, the EEG signals have shown promises towards the MI classification of post-stroke subjects. This paper presents a MI based left and right wrist dorsiflexion classification performed on 6 stoke subjects. The MI EEG data are recorded from 16 electrode locations on the subject’s scalp. Three different feature extraction methods are used to compare and find the best performing one; the first one is based on the Wavelet Packet Decomposition (WPD) combined with Higher Order Statistics (HOS) which is compared with the widely used Common Spatial Pattern (CSP) and Filter Bank Common Spatial Pattern (FBCSP) filter method. The MI classifications are performed using Random Forest (RF) algorithm to achieve a mean accuracy that exceeds 70% for the WPD+HOS method, while outperforming the CSP and FBCSP based methods.

Electroencephalogram (EEG) based motor imagery (MI) brain-computer interface (BCI) has emerged as a promising tool for communication and control. Most MI classification methods use fixed-length time windows to intercept signals and perform subsequent analyses. However, the fixed-length time window interception method can not achieve optimal performance due to significant differences in the multiple imagining tasks of the same subject. In this paper, we present a novel interception method using long and short windows (LSWs). This method takes advantage of the subject’s motor imaginary strength at different times of the task to select specific time windows corresponding to the most salient features. The features corresponding to the selected time windows are used for the final MI classification. We compare the proposed LSW interception method with the fixed-length time window method on a public EEG dataset (BCI competition IV dataset 1) and a self-collected dataset. The results show that the classification accuracies are improved with the LSW interception method on both datasets. When using the support vector machine (SVM) classifier, the classification accuracy of common spatial pattern with the LSW method achieves 2.57% and 1.12% improvement on two datasets, respectively, and the classification accuracy of filter bank common spatial pattern (FBCSP) with the LSW method achieves 0.93% and 1.48% improvement, respectively. Among them, the classification accuracy of the LSW method with FBCSP and SVM is the highest, which is 93.43% and 91.12%, respectively. Compared with the traditional methods, this method significantly increases the classification accuracy and provides a new idea for researching the MI classification method in BCI.

The creation of non-invasive Neuro-headset technology has resulted in a variety of applications such as control, automation, and vehicular control. Unmanned Aerial Vehicles (UAVs), mostly used in aerial communication, is a growing field of study that may be applied to both defense and commercial applications. As the need for drone control grows, recent advancements in the BCI-based drone control system have been made. This paper proposes a unique prototype to achieve smooth and stable control of a UAV by the human brain based on a motor imagery signal for semi-autonomous navigation. In such cases the accuracy and signal detection and response time get the utmost priority to get a decent performance of the vehicle. Currently, it is very common to use CSP for the classification of motor imagery data. However, this algorithm needs improvement in the low quantity of training samples and noisy data. Because in that scenario the over-fitting may be a problem. To overcome those drawbacks this study proposed Stacked Ensemble Common Spectral Pattern (SECSP). Comparing this method with Common spatial pattern (CSP), Filter Bank CSP (FBCSP), and Common Spatio-Spectral Patterns (CSSP) it has been proved that our model outperforms those popular methods in terms of accuracy, sensitivity, robustness, and precision for motor imagery Electroencephalogram data. The publicly available data-set for motor imagery BCI Competition IV, Data-set 1, BCI Competition III, Data-set IVA have all been tested with Stacked Ensemble CSP (SECSP). Performance is enhanced when compared to previous approaches, with an average accuracy of 87.74 percent for all subjects and 95.64 percent for the first and second data sets, respectively.

By 2021, WHO projects over a billion incapacitated people, with 20% facing daily functional impairments.Brain-Computer Interface (BCI) offers effortless machine control via direct brain-computer interaction, with Motor Imagery (MI) Electroencephalogram (EEG) as the key BCI foundation.The MI EEG signals were collected from the BNCI horizon2020 database for nine participants.The MI EEG data includes four tasks: imaginative movement of the left hand, right hand, feet, and tongue.There are 22 EEG channels with a sampling rate of 250Hz in the data.The MI EEG signals were band-pass filtered with a lower cut-off frequency of 0.5 Hz and an upper cut-off frequency of 100 Hz.Based on the energy count threshold approach (ECTA) nine channels were identified as dominating channels from the filtered MI EEG signals.Energy values for each channel were extracted in the ECTA method for the 3-sec window.And if the energy value of a channel for a particular window is greater than 60% of the maximum channel's energy, then the energy count value will be incremented by one.Finally whichever channels had larger energy counts were identified as a dominant channel.On these nine dominant MI EEG signals discrete wavelet transform using Daubechies 4 mother wavelet a four-level decomposition is applied, and Mu and beta rhythms were extracted.For a 3-sec window from the nine dominant channels, the energy and entropy feature values from the Mu and Beta rhythms were extracted.From the extracted features, 80% of the data is used for training Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) and 20% for testing classifier models.Both the models performed well on the test data and the results obtained had a highest accuracy 91.72.7 for subject-9 and these results were compared with the existing methods.The obtained results of MI EEG signals classification using BCI holds potential for revolutionizing assistive technology, stroke rehabilitation, virtual reality gaming,

Brain-computer interfaces (BCIs) were initially created to help individuals with disabilities control devices and communicate without muscle movement. Today, BCIs are used for prosthetic control, cognitive enhancement, and neurological rehabilitation. The BCI system depends on analyzing electroencephalogram (EEG) signals captured from the brain. Decoding these EEG signals is a complex process that combines multiple algorithms to extract meaningful information from these intricate and noisy signals. One of the most popular techniques is the Common Spatial Patterns (CSP), which helps preserve useful and sensitive information. This paper presents an optimized extension of the CSP model for extracting EEG data features in a multiclass setting using Riemannian geometry-based weighting. The use of weighting based on Riemannian geometry enhances the robustness of covariance matrix computation, thereby decreasing the influence of noise that can significantly distort the mean of covariance matrices in the traditional CSP method. The proposed approach is also extended by the integration of a multi-band filter bank, providing a more detailed examination of EEG signals. Three classifiers, Linear Discriminant Analysis (LDA), Random Forest Classifier (RFC), and Multi-Layer Perceptron (MLP), are employed to differentiate features across four motor imagery tasks. LDA achieves an accuracy of 80.40%, while MLP and RFC reach 80.02% and 80.90%, respectively. The results obtained using a majority vote combining the decisions of the three classifiers are 81.83% for accuracy and Recall, 82.74% for precision, and 81.87% for F1-score. The proposed architecture is evaluated using the BCI Competition IV set 2a dataset, proving its effectiveness in EEG signal classification for BCI applications.

Brain computer interfaces (BCIs) based on motor imagery (MI) play an important role in helping to improve and restore the loss of physical function. However, traditional MI-BCIs are limited to the motion intention of gross limb, which places many restrictions on their applications. This study proposes a hybrid paradigm based on MI and the steady-state somatosensory evoked potential, with the aim of improving the spatial resolution of MI recognition. Twelve subjects participated in this study. They performed MI tasks under MI and hybrid conditions. In the MI condition, subjects only performed MI tasks, whereas, in the hybrid condition, they received an electrical stimulus while performing the same tasks. Under the hybrid condition, subjects were required not to pay extra attention to the electrical stimulation. The MI task included imagining clenching the right hand and lifting the right forearm. The classifier was built using the filter bank common spatial pattern algorithm and a support vector machine, and online experiments were used to verify the recognition of two MI tasks. During the online experiments, all subjects were able to output different commands at a recognition accuracy far higher than the random level. The average classification accuracy of the hybrid condition reached 76.39%, with a maximum value of 88.34%, which was about 11% higher than that of the MI condition. Moreover, based on offline data, the classification performance using the event-related desynchronization (ERD) feature under the hybrid condition did not differ significantly from that under the MI condition, indicating that the introduction of electrical stimulation did not interfere in the separability of ERD. The proposed paradigm improved the efficiency of decoding multiple MI locations within a single limb. Despite the introduction of external stimuli, users could still drive the new system in the same way as MI in traditional MI-BCI.

Brain-Computer Interface (BCI) systems establish a control and communication relationship between the human brain and computers including robots or other devices to help individuals with severe motor disabilities. The classification of motor and mental imagery electroencephalogram (EEG) signals is complicated because these signals are usually case-specific and distinct models must be trained for each subject to process and classify his/her EEG signals. Moreover, in BCI systems EEG signals are processed online, so the time latency must be very low. In this paper, we have proposed a method based on signal-to-image conversion to investigate image processing techniques in the pair-wise classification of motor and mental imagery EEG signals. We first decomposed EEG signals of each trial into four sub-bands. Then, for each sub-band, we converted EEG time series to 2-dimensional (2D) images using covariance between signals of all channels. Then, statistical, textural and PCA-based features were extracted from these images and fed to a support vector machine (SVM) classifier. Our results were promising in the offline processing and achieved an average classification accuracy of 79.57%.

Existing research methods for recognizing EEG (Electroencephalogram) signals in motor imagery (MI) often overlook the dynamic changes of brain networks over time, resulting in insufficient classification accuracy for MI tasks. This article addresses the recognition problem of dynamic changes in brain networks during MI tasks and applies an EEG signal classification method based on multi-layer time-varying functional brain networks. This article uses the BCI (Brain-Computer Interface) Competition IV 2a dataset to preprocess the raw EEG signals through bandpass filtering and CSP (Common Spatial Pattern) algorithm. The EEG signals of the MI task are divided into 7 1-second time windows with a step size of 0.5 seconds. Within each time window, Pearson correlation coefficients between EEG channels can be calculated to generate corresponding brain networks, and multiple time-varying functional brain networks can be constructed by stacking the brain networks from multiple time windows. The network topology features, node degree, clustering coefficient, network efficiency, and multi-layer network features of each window can be extracted, including Multiplex Clustering Coefficient (MCC), Multiplex Participation Coefficient (MPC), and inter layer correlation coefficient. By dividing the dataset through 10 fold cross validation, the random forest algorithm can be used to classify and recognize four types of motion imagination tasks. The experimental results show that the average recognition rate of the article’s method in four types of MI tasks reached 89.19%. This method can improve the classification accuracy of MI tasks and enhance a comprehensive understanding of the dynamic changes in brain networks during the process of MI.

Patients suffering from severe motor neuron diseases (MND) experience motor disability and their rehabilitation has always remained a challenge. Electroencephalogram (EEG) based brain computer interface (BCI) is a system that can be used for the rehabilitation of patients suffering from amputation or from severe disease like MND, stroke, locked in syndrome (LIS); where EEG signal is acquired from brain scalp while performing certain mental task such as motor imagery, cognitive imagery etc. In the present paper brain signals i.e. EEG for 10 motor imagery movements of upper limb acquired from 4 subjects were classified. Filter Bank Common Spatial Pattern (FBCSP) algorithm was used for extracting features of EEG signal captured from 5 electrodes placed over motor cortex and mutual information is used for feature selection. Classification algorithm followed was linear Support Vector Machine (SVM) in MATLAB 2015a.

IntroductionThe application of motor imagery in human–computer interaction and rehabilitative medicine has attracted growing attention due to recent advances in brain–computer interface technologies. However, traditional EEG decoding paradigms based on fixed frequency–band segmentation often exhibit limited performance because they fail to capture individual variability in brain rhythms.MethodsThis work proposes an adaptive method that integrates the sparrow search algorithm (SSA) with Filter Bank Common Spatial Pattern (FBCSP) to optimize sub–band segmentation for motor imagery EEG decoding. SSA adaptively searches for optimal sub–band boundaries, enabling individualized frequency–band selection.ResultsExperiments on the BCI Competition IV 2a dataset under a cross–session evaluation protocol (training on session T, testing on session E) demonstrated that SSA–FBCSP effectively improves frequency–band adaptability. The SSA–FBCSP approach was further combined with Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and k–Nearest Neighbor (KNN) classifiers to evaluate the influence of different downstream classifiers.ConclusionAmong them, SSA–FBCSP–LDA achieved the best performance, outperforming the conventional uniform sub–band approach by 21.76% and reaching an average accuracy of 89.92%. The adaptively selected sub–bands closely matched the ERD/ERS distribution, confirming the method’s effectiveness in frequency–band optimization. Compared with recent deep–learning–based MI–EEG models, the proposed technique offers a balance of accuracy, interpretability, and computational efficiency, providing a promising direction for personalized brain–computer interface systems.

Brain-computer interface (BCI) systems are proposed as a means of communication for locked-in patients. One common BCI paradigm is motor imagery in which the user controls a BCI by imagining movements of different body parts. It is known that imagining different body parts results in event-related desynchronization (ERD) in various frequency bands. Existing methods such as common spatial patterns (CSP) and its refinement filterbank common spatial patterns (FB-CSP) aim at finding features that are informative for classification of the motor imagery class. Our proposed method is a temporally adaptive common spatial patterns implementation of the commonly used filter-bank common spatial patterns method using convolutional neural networks; hence it is called TA-CSPNN. With this method we aim to: (1) make the feature extraction and classification end-to-end, (2) base it on the way CSP/FBCSP extracts relevant features, and finally, (3) reduce the number of trainable parameters compared to existing deep learning methods to improve generalizability in noisy data such as EEG. More importantly, we show that this reduction in parameters does not affect performance and in fact the trained network generalizes better for data from some participants. We show our results on two datasets, one publicly available from BCI Competition IV, dataset 2a and another in-house motor imagery dataset.

Common Spatial Pattern (CSP) and Support Vector Machine (SVM) are usually adopted for feature extraction and classification of two-class motor imagery. However, in a motor imagery based BCI-FES rehabilitation system, stroke patients usually are not able to conduct correct motor imagery like healthy people due to the injury of motor cortex. Therefore, motor imagery EEG of stroke patients lacks of specific discriminant features as appearances of healthy people, which significantly blocks CSP to seek the optimal projection subspace. In this paper, a method, which filters EEG into a variety of bands and improves performance through boosting principle based on a set of weak CSP-SVM classifiers, was proposed to solve the problem mentioned above and was evaluated on the EEG datasets of three stroke subjects. The proposed method outperformed the traditional CSP-SVM method in terms of classification accuracy. From data analysis, we observed that optimal spectral band for classification had been changing along with rehabilitation training, which may reveal mechanisms that dominant frequency band may be changed along with rehabilitation training and spectral power distribution may be changed in different stages of rehabilitation. In addition, this work also demonstrated the feasibility of our SJTU-BCMI BCI-FES rehabilitation training system.

Optimal Spatio-spectral Variable Size Subbands Filter for Motor Imagery Brain Computer Interface

Motor imagery (MI)-based brain-computer interface (BCI) systems are being increasingly employed to provide alternative means of communication and control for people suffering from neuro-motor impairments, with a special effort to bring these systems out of the controlled lab environments. Hence, accurately classifying MI from brain signals, e.g., from electroencephalography (EEG), is essential to obtain reliable BCI systems. However, MI classification is still a challenging task, because the signals are characterized by poor SNR, high intra-subject and cross-subject variability. Deep learning approaches have started to emerge as valid alternatives to standard machine learning techniques, e.g., filter bank common spatial pattern (FBCSP), to extract subject-independent features and to increase the cross-subject classification performance of MI BCI systems. In this paper, we first present a review of the most recent studies using deep learning for MI classification, with particular attention to their cross-subject performance. Second, we propose DynamicNet, a Python-based tool for quick and flexible implementations of deep learning models based on convolutional neural networks. We show-case the potentiality of DynamicNet by implementing EEGNet, a well-established architecture for effective EEG classification. Finally, we compare its performance with FBCSP in a 4-class MI classification over public datasets. To explore its cross-subject classification ability, we applied three different cross-validation schemes. From our results, we demonstrate that DynamicNet-implemented EEGNet outperforms FBCSP by about 25%, with a statistically significant difference when cross-subject validation schemes are applied.