Comparison of EEG measurement of upper limb movement in motor imagery training system

A novel feature extraction method PSS-CSP for binary motor imagery - based brain-computer interfaces

In order to improve the performance of binary motor imagery (MI) - based brain-computer interfaces (BCIs) using electroencephalography (EEG), a novel method (PSS-CSP) is proposed, which combines spectral subtraction with common spatial pattern. Spectral subtraction is an effective denoising method which is initially adopted to process MI-based EEG signals for binary BCIs in this work. On this basis, we proposed a novel feature extraction method called power spectral subtraction-based common spatial pattern (PSS-CSP) , which calculates the differences in power spectrum between binary classes of EEG signals and uses the differences in the feature extraction process. Additionally, support vector machine (SVM) algorithm is used for signal classification. Results show the proposed method (PSS-CSP) outperforms certain existing methods, achieving a classification accuracy of 76.8% on the BCIIV dataset 2b, and 76.25% and 77.38% on the OpenBMI dataset session 1 and session 2, respectively.

Applying Common Spatial Pattern and Convolutional Neural Network to Classify Movements via EEG Signals

Developing an electroencephalography (EEG)-based brain-computer interface (BCI) system is crucial to enhancing the control of external prostheses by accurately distinguishing various movements through brain signals. This innovation can provide comfortable circumstances for the populace who have movement disabilities. This study combined the most prospering methods used in BCI systems, including one-versus-rest common spatial pattern (OVR-CSP) and convolutional neural network (CNN), to automatically extract features and classify eight different movements of the shoulder, wrist, and elbow via EEG signals. The number of subjects who participated in the experiment was 10, and their EEG signals were recorded while performing movements at fast and slow speeds. We used preprocessing techniques before transforming EEG signals into another space by OVR-CSP, followed by sending signals into the CNN architecture consisting of four convolutional layers. Moreover, we extracted feature vectors after applying OVR-CSP and considered them as inputs to KNN, SVM, and MLP classifiers. Then, the performance of these classifiers was compared with the CNN method. The results demonstrated that the classification of eight movements using the proposed CNN architecture obtained an average accuracy of 97.65% for slow movements and 96.25% for fast movements in the subject-independent model. This method outperformed other classifiers with a substantial difference; ergo, it can be useful in improving BCI systems for better control of prostheses.

Input Shape Effect on Classification Performance of Raw EEG Motor Imagery Signals with Convolutional Neural Networks for Use in Brain-Computer Interfaces

EEG signals are interpreted, analyzed and classified by many researchers for use in brain-computer interfaces. Although there are many different EEG signal acquisition methods, one of the most interesting is motor imagery signals. Many different signal processing methods, machine learning and deep learning models have been developed for the classification of motor imagery signals. Among these, Convolutional Neural Network models generally achieve better results than other models. Because the size and shape of the data is important for training Convolutional Neural Network models and discovering the right relationships, researchers have designed and experimented with many different input shape structures. However, no study has been found in the literature evaluating the effect of different input shapes on model performance and accuracy. In this study, the effects of different input shapes on model performance and accuracy in the classification of EEG motor imagery signals were investigated, which had not been specifically studied before. In addition, signal preprocessing methods, which take a long time before classification, were not used; rather, two CNN models were developed for training and classification using raw data. Two different datasets, BCI Competition IV 2A and 2B, were used in classification processes. For different input shapes, 53.03-89.29% classification accuracy and 2-23 s epoch time were obtained for 2A dataset, 64.84-84.94% classification accuracy and 4-10 s epoch time were obtained for 2B dataset. This study showed that the input shape has a significant effect on the classification performance, and when the correct input shape is selected and the correct CNN architecture is developed, feature extraction and classification can be done well by the CNN architecture without any signal preprocessing.

Hybrid mental tasks based human computer interface via integration of pronunciation and motor imagery

Objective.Among the existing active brain-computer interfaces (BCI), the motor imagination (MI) is widely used. To operate the MI BCI effectively, subjects need to carry out trainings on corresponding imagining tasks. Here, we studied how to reduce the discomfort and fatigue of active BCI imaginary tasks and the inability to concentrate on them while improving the accuracy.Approach.This paper proposes a hybrid BCI composed of MI and pronunciation imagination (PI). The electroencephalogram signals of ten subjects are recognized by the adaptive Riemannian distance classification and the improved frequency selective filter-bank Common Spatial Pattern recognition.Main results.The results show that under the new paradigm with the combination of MI and PI, the recognition accuracy is higher than the MI alone. The highest recognition rate of the proposed hybrid system can reach more than 90%. Furthermore, through the subjects' scoring results of the operation difficulty, it is concluded that the designed hybrid paradigm is more operable than the traditional BCI paradigm.Significance.The separable tasks in the active BCI are limited and the accuracy needs to be improved. The new hybrid paradigm proposed by us improves the accuracy and operability of the active BCI system, providing a new possibility for the research direction of the active BCI.

Clustered event related spectral perturbation (ERSP) feature in right hand motor imagery classification

A technology that allows humans to interact with machines more directly and efficiently would be desirable. Research on brain-computer interfaces (BCIs) provides the possibility for computers to understand human thoughts in a straightforward manner thereby facilitating communication. As a branch of BCI research, motor imagery (MI) techniques analyze the brain signals and help people in many aspects such as rehabilitation, clinical applications, entertainment, and system controlling. In this study, an imagery experiment consisting of four kinds of right-hand movements (gripping, opening, pronation, and supination) was designed. Then a novel feature, namely, clustered feature was proposed based on the event-related spectral perturbation (ERSP) calculated from the EEG signal. Based on the selected features, two classical classifiers (support vector machine and linear discriminant classifier) were trained, achieving an acceptable accurate result (80%, on average).

Medical Data Acquisition and Internet of Things Technology-Based Cerebral Stroke Disease Prevention and Rehabilitation Nursing Mobile Medical Management System

This research was aimed at exploring the application value of a mobile medical management system based on Internet of Things technology and medical data collection in stroke disease prevention and rehabilitation nursing. In this study, on the basis of radio frequency identification (RFID) technology, the signals collected by the sensor were filtered by the optimized median filtering algorithm, and a rehabilitation nursing evaluation model was established based on the backpropagation (BP) neural network. The performance of the medical management system was verified in 32 rehabilitation patients with hemiplegia after stroke and 6 healthy medical staff in the rehabilitation medical center of the hospital. The results showed that the mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the median filtering algorithm after optimization were significantly higher than those before optimization (P < 0.05). When the number of neurons was 23, the prediction accuracy of the test set reached a maximum of 89.83%. Using traingda as the training function, the model had the lowest training time and root mean squared error (RMSE) value of 2.5 s and 0.29, respectively, which were significantly lower than the traingd and traingdm functions (P < 0.01). The error percentage and RMSE of the model reached a minimum of 7.56% and 0.25, respectively, when the transfer functions of both the hidden and input layers were tansig. The prediction accuracy in stages III~VI was 90.63%. It indicated that the mobile medical management system established based on Internet of Things technology and medical data collection has certain application value for the prevention and rehabilitation nursing of stroke patients, which provides a new idea for the diagnosis, treatment, and rehabilitation of stroke patients.

An end-to-end CNN with attentional mechanism applied to raw EEG in a BCI classification task

Objective.Motor-imagery (MI) classification base on electroencephalography (EEG) has been long studied in neuroscience and more recently widely used in healthcare applications such as mobile assistive robots and neurorehabilitation. In particular, EEG-based MI classification methods that rely on convolutional neural networks (CNNs) have achieved relatively high classification accuracy. However, naively training CNNs to classify raw EEG data from all channels, especially for high-density EEG, is computationally demanding and requires huge training sets. It often also introduces many irrelevant input features, making it difficult for the CNN to extract the informative ones. This problem is compounded by a dearth of training data, which is particularly acute for MI tasks, because these are cognitively demanding and thus fatigue inducing.Approach.To address these issues, we proposed an end-to-end CNN-based neural network with attentional mechanism together with different data augmentation (DA) techniques. We tested it on two benchmark MI datasets, brain-computer interface (BCI) competition IV 2a and 2b. In addition, we collected a new dataset, recorded using high-density EEG, and containing both MI and motor execution (ME) tasks, which we share with the community.Main results.Our proposed neural-network architecture outperformed all state-of-the-art methods that we found in the literature, with and without DA, reaching an average classification accuracy of 93.6% and 87.83% on BCI 2a and 2b, respectively. We also directly compare decoding of MI and ME tasks. Focusing on MI classification, we find optimal channel configurations and the best DA techniques as well as investigate combining data across participants and the role of transfer learning.Significance.Our proposed approach improves the classification accuracy for MI in the benchmark datasets. In addition, collecting our own dataset enables us to compare MI and ME and investigate various aspects of EEG decoding critical for neuroscience and BCI.

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain-Computer Interfaces: A Systematic Literature Review

Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain-computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

Abdul Majeed A. 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.

Digital Twin Coaching for Physical Activities: A Survey

Digital Twin technology has been rising in popularity thanks to the popularity of machine learning in the last decade. As the life expectancy of people around the world is increasing, so is the focus on physical activity to remain healthy especially in the current times where people are staying sedentary while in quarantine. This article aims to provide a survey on the field of Digital Twin technology focusing on machine learning and coaching techniques as they have not been explored yet. We also define what Digital Twin Coaching is and categorize the work done so far in terms of the objective of the physical activity. We also list common Digital Twin Coaching characteristics found in the articles reviewed in terms of concepts such as interactivity, privacy and security and also detail future perspectives in multimodal interaction and standardization, to name a few, that can guide researchers if they choose to work in this field. Finally, we provide a diagram for the Digital Twin Ecosystem showing the interaction between relevant entities and the information flow as well as an idealization of an ideal Digital Twin Ecosystem for team sports’ athlete tracking.

Multi-channel EEG recording during motor imagery of different joints from the same limb

Motor imagery (MI) is one of the important brain-computer interface (BCI) paradigms, which can be used to control peripherals without external stimulus. Imagining the movements of different joints of the same limb allows intuitive control of the outer devices. In this report, we describe an open access multi-subject dataset for MI of different joints from the same limb. This experiment collected data from twenty-five healthy subjects on three tasks: 1) imagining the movement of right hand, 2) imagining the movement of right elbow, and 3) keeping resting with eyes open, which results in a total of 22,500 trials. The dataset provided includes data of three stages: 1) raw recorded data, 2) pre-processed data after operations such as artifact removal, and 3) trial data that can be directly used for feature extraction and classification. Different researchers can reuse the dataset according to their needs. We expect that this dataset will facilitate the analysis of brain activation patterns of the same limb and the study of decoding techniques for MI.

Deep Channel-Correlation Network for Motor Imagery Decoding From the Same Limb

Motor imagery (MI) is an important brain-computer interface (BCI) paradigm, which can be applied without external stimulus. Imagining different joint movements from the same limb allows intuitive control of the outer devices. However, few researches focused on this field, and the decoding accuracy limited the applications for practical use. In this study, we aim to use deep learning methods to explore the ceiling of the decoding performance of three tasks: the resting state, the MI of right hand and right elbow. To represent the brain functional relationships, the correlation matrix that consists of correlation coefficients between electrodes (channels) was calculated as features. We proposed the Channel-Correlation Network to learn the overall representation among channels for classification. Ensemble learning was applied to integrate the output of multiple Channel-Correlation Networks. Our proposed method achieved the decoding accuracy of up to 87.03% in the 3-class scenario. The results demonstrated the effectiveness of deep learning method for decoding MI of different joints from the same limb and the potential of this fine paradigm to be applied in practice.