A New Compound-limbs Paradigm: Integrating upper-limb swing improves lower-limb stepping intention decoding from EEG.

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.

Motor imagery (MI) has been widely used to operate brain-computer interface (BCI) systems for rehabilitation and some life assistive devices. However, the current performance of an MI-based BCI cannot fully meet the needs of its in-field applications. Most of the BCIs utilizing a generalized feature for all participants have been found to greatly hamper the efficacy of the BCI system. Hence, some attempts have made on the exploration of subject-dependent parameters, but it remains challenging to enhance BCI performance as expected. To this end, in this study, we used the independent component analysis (ICA), which has been proved capable of isolating the pure motor-related component from non-motor-related brain processes and artifacts and extracting the common motor-related component across MI, motor execution (ME), and motor observation (MO) conditions. Then, a sliding window approach was used to detect significant mu-suppression from the baseline using the electroencephalographic (EEG) alpha power time course and, thus, the success rate of the mu-suppression detection could be assessed on a single-trial basis. By comparing the success rates using different parameters, we further quantified the extent of the improvement in each motor condition to evaluate the effectiveness of both generalized and individualized parameters. The results showed that in ME condition, the success rate under individualized latency and that under generalized latency was 90.0% and 77.75%, respectively; in MI condition, the success rate was 74.14% for individual latency and 58.47% for generalized latency, and in MO condition, the success rate was 67.89% and 61.26% for individual and generalized latency, respectively. As can be seen, the success rate in each motor condition was significantly improved by utilizing an individualized latency compared to that using the generalized latency. Moreover, the comparison of the individualized window latencies for the mu-suppression detection across different runs of the same participant as well as across different participants showed that the window latency was significantly more consistent in the intra-subject than in the inter-subject settings. As a result, we proposed that individualizing the latency for detecting the mu-suppression feature for each participant might be a promising attempt to improve the MI-based BCI performance.

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.

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.

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.

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.

Motor imagery (MI) has emerged as a promising therapeutic approach for Parkinson's disease (PD). MI entails mentally rehearsing motor actions without executing them. This cognitive process has garnered attention due to its potential benefits in aiding motor function recovery in patients. The purpose of this review was to highlight the findings observed in motor symptoms, balance, gait, and quality of life. A literature search was carried out in Medline, Embase, Cochrane, and Physiotherapy Evidence Database (PEDro), from the first publication to February 2024. Studies with at least one keyword to PD and MI in the title were included. The analysis included 53 studies out of the 262 identified. These comprised 12 randomized controlled trials (RCTs) with an average PEDro score of 6.6 out of 10, as well as 41 non-RCT studies. Notably, the majority of the RCTs focused on balance, gait, and lower limb exercises. The experimental group found an 85.2% improvement on the Timed Up and Go (TUG) with a cognitive task (p < 0.02), 5.8% improvement on the TUG (p < 0.05), and 5.1% improvement in walking speed (p < 0.05). Other variables did not show significant improvement. In descriptive and non-RCT studies, there were various tasks and outcomes for the lower and upper limbs. It has been demonstrated that there was no difference in execution time in MI between patients and healthy subjects (HS), whereas motor execution was slower in patients. Several tasks were analyzed for the upper limb, including thumb opposition, joystick movements, and writing tasks with variable results. RCTs were more focused on balance, lower limbs, and walking. There was no specific outcome regarding the upper limb or speech. Additionally, the heterogeneity of tasks and outcomes across studies is also a limitation. Current research on walking disorders in PD shows promise, but further investigations are crucial, particularly with an emphasis on upper limb function and speech. Studies with larger sample sizes and more precise methodologies are needed to enhance our understanding of the potential benefits of MI within the framework of comprehensive PD rehabilitation.

Asynchronous Brain Computer Interface (BCI) is an important class of BCI systems that has not received enough attention from the BCI community. In this work we introduce for the first time a system for classification of four different motor imageries in the context of an asynchronous BCI system which distinguishes between periods of movement imagination occurrence and idling or resting periods of ongoing EEG signal as well as classifying the 4 class motor imageries. We used two multi class extensions of the method of Common Spatial Patterns (CSP) for feature extraction and LDA, SVM, and MDA well known classifiers for combination purposes. We have applied our procedure to data set IIIa from BCI Competition III [2]. Offline evaluation of a prototype system demonstrated true positive rates in the range of 56%-88% with corresponding false positive rates in the range of 18%-9%.

Current Brain Computer Interface (BCI) systems are still not commercialized due to their insufficient accuracies. Although Common Spatial Pattern (CSP) is an effective method for BCI applications, it does not consider the channels' co-activation information. This study is aimed at modifying CSP method by incorporating synchronization of channels in the CSP formula and then passes the values through a nonlinear kernel function to enhance the discrimination rate between the left and right imagery movements. Inserting the synchronization of every two channels into the kernel CSP formula is performed in two different ways. Experimental results on a set of EEG signals belonged to three subjects for discrimination of left and right hand imagery movements reveal that the proposed kernel synchronous CSP method outperformed the standard CSP, synchronous CSP and kernel CSP methods for all subjects.

Background/Objectives: Motor imagery (MI) is the mental practice of motor actions with temporal dynamics and neural features in common with motor execution (ME). Although MI can improve motor performance, it remains unclear how closely performance-related signatures of MI resemble those of ME during learning, particularly in tasks with intrinsic variability. This study investigated similarities and differences between MI and ME during a probabilistic sequence-learning task. Methods: Participants performed a finger-tapping serial reaction time task in either a motor execution (ME; n = 10) or motor imagery (MI; n = 10) condition. The task consisted of 750 auditory stimuli mapped to right-hand finger movements and generated by a probabilistic sequence with deterministic and variable events. Reaction times were analyzed using ANOVA designs to assess the effects of Group, Block, Event Type, and the Last Variable event. Results: The MI group showed a significant reduction in reaction times across blocks (p < 0.001), indicating learning-related performance improvement, whereas no block-wise improvement was observed in the ME group. Both groups were sensitive to the probabilistic structure of the sequence, with reaction times differing across event types. A significant Group × Event interaction (p < 0.01) indicated distinct performance signatures for MI and ME. In both groups, reaction times were modulated by the last variable event. Conclusions: Motor imagery supported learning in a probabilistic sequence task but was influenced by factors distinct from those governing motor execution, suggesting partially different underlying mechanisms.

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.

The demand for public datasets has increased as data-driven methodologies have been introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are available in various platforms or repositories on the web, and the studies that have employed these datasets appear to be increasing. Motor imagery is one of the significant control paradigms in the BCI field, and many datasets related to motor tasks are open to the public already. However, to the best of our knowledge, these studies have yet to investigate and evaluate the datasets, although data quality is essential for reliable results and the design of subject− or system-independent BCIs. In this study, we conducted a thorough investigation of motor imagery/execution EEG datasets recorded from healthy participants published over the past 13 years. The 25 datasets were collected from six repositories and subjected to a meta-analysis. In particular, we reviewed the specifications of the recording settings and experimental design, and evaluated the data quality measured by classification accuracy from standard algorithms such as Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for comparison and compatibility across the datasets. As a result, we found that various stimulation types, such as text, figure, or arrow, were used to instruct subjects what to imagine and the length of each trial also differed, ranging from 2.5 to 29 s with a mean of 9.8 s. Typically, each trial consisted of multiple sections: pre-rest (2.38 s), imagination ready (1.64 s), imagination (4.26 s, ranging from 1 to 10 s), the post-rest (3.38 s). In a meta-analysis of the total of 861 sessions from all datasets, the mean classification accuracy of the two-class (left-hand vs. right-hand motor imagery) problem was 66.53%, and the population of the BCI poor performers, those who are unable to reach proficiency in using a BCI system, was 36.27% according to the estimated accuracy distribution. Further, we analyzed the CSP features and found that each dataset forms a cluster, and some datasets overlap in the feature space, indicating a greater similarity among them. Finally, we checked the minimal essential information (continuous signals, event type/latency, and channel information) that should be included in the datasets for convenient use, and found that only 71% of the datasets met those criteria. Our attempts to evaluate and compare the public datasets are timely, and these results will contribute to understanding the dataset’s quality and recording settings as well as the use of using public datasets for future work on BCIs.

The Common Spatial Patterns (CSP) method is a widely used spatial filtering technique that can extract discriminative features for Electroencephalogram (EEG)-based brain computer interface (BCI) classification tasks. Since the EEG signal acquired on the scalp is a nonlinear composition of multiple signal and noise sources, in order to characterize the nonlinear data structure, nonlinear CSP methods have been proposed by using the kernel technique. Most kernel CSP methods calculate temporal covariance structure in a kernel feature space that leads to a large kernel matrix with each dimension equal to the number of time points multiplied by the number of classes. In this work, a kernel CSP method exploiting spatial covariance structure in the feature space is developed where the size of kernel matrix is the number of EEG channels, which is usually much less than that of time points. The proposed method was evaluated using motor imagery EEG data. Results indicate that the kernel CSP using spatial analysis can provide comparable performance to the existing methods using temporal analysis with less computational load.

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.

Brain computer interface (BCI) is known as a good way to communicate between brain and computer or other device. There are many kinds of physiological signal can operate BCI systems. Motor imagery (MI) has been demonstrated to be a good way to operate a BCI system. In some recent studies about MI based BCI systems, low accuracy rate and time consuming are common problems. In this thesis, a novel motor imagery algorithm is proposed to improve the accuracy rate and computational efficiency at the same time. The architecture of many BCI system is quite complex and they involve time consuming processing. The electroencephalography (EEG) signal is the most commonly used inputs for BCI applications but EEG is often contaminated with noise. To overcome such drawbacks, in this paper we use the common spatial pattern (CSP) for feature extraction from EEG and the linear discriminant analysis (LDA) for motor imagery classification. In this study, CSP and LDA have been used to reduce the artifact and classify Mi-based EEG signal. We have used two-level cross validation scheme to determine the subject specific best time window and number of CSP features. We have compared the performance of our system with BCI competition results. This novel algorithm with high accuracy rate and efficiency can be applied to real time BCI system in real-life applications.