A New PLV-Spatial Filtering to Improve the Classification Performance in BCI 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.

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Brain–computer interface (BCI) is an active domain which has attracted attention of the research community in recent years. It offers huge potential as a technology which can estimate the intention of a user by analysis of brain signals and establish a communication channel directly between a human brain and an external device. Electroencephalography (EEG) is the most popular signal acquisition technique due to its ease of use and simplicity. In EEG-based BCI systems, electrodes are placed on specific positions on the scalp of the subject to record electrical activity. The BCI system consists of sequential stages of signal acquisition, its preprocessing, feature extraction and feature classification. It is an active research area which has a focus on improving classification accuracy in motor imagery-based BCI systems. The first stage in a BCI system is to acquire EEG signals from different positions of the scalp of the human subject. The acquired brain signals are preprocessed to remove artifacts before these are fed to feature the extraction stage. In this paper, independent component analysis (ICA) technique is used to remove artifacts from acquired signals. Filter bank common spatial pattern (FBCSP) technique is then used for feature extraction and feature selection. A feature classification approach based on support vector machine (SVM) is proposed in this work and its performance is enhanced by optimizing its polynomial kernel parameters. Selection of kernel parameters is done by grid search method using the fivefold cross-validation procedure. The proposed approach is then executed on publicly available data set 2a of BCI Competition IV. Results show that the proposed approach offers higher classification accuracy and lower misclassification rate as compared to other methods executed on the same dataset, as reported in literature.

BackgroundA novel artefact removal algorithm is proposed for a self-paced hybrid brain-computer interface (BCI) system. This hybrid system combines a self-paced BCI with an eye-tracker to operate a virtual keyboard. To select a letter, the user must gaze at the target for at least a specific period of time (dwell time) and then activate the BCI by performing a mental task. Unfortunately, electroencephalogram (EEG) signals are often contaminated with artefacts. Artefacts change the quality of EEG signals and subsequently degrade the BCI’s performance.MethodsTo remove artefacts in EEG signals, the proposed algorithm uses the stationary wavelet transform combined with a new adaptive thresholding mechanism. To evaluate the performance of the proposed algorithm and other artefact handling/removal methods, semi-simulated EEG signals (i.e., real EEG signals mixed with simulated artefacts) and real EEG signals obtained from seven participants are used. For real EEG signals, the hybrid BCI system’s performance is evaluated in an online-like manner, i.e., using the continuous data from the last session as in a real-time environment.ResultsWith semi-simulated EEG signals, we show that the proposed algorithm achieves lower signal distortion in both time and frequency domains. With real EEG signals, we demonstrate that for dwell time of 0.0s, the number of false-positives/minute is 2 and the true positive rate (TPR) achieved by the proposed algorithm is 44.7%, which is more than 15.0% higher compared to other state-of-the-art artefact handling methods. As dwell time increases to 1.0s, the TPR increases to 73.1%.ConclusionsThe proposed artefact removal algorithm greatly improves the BCI’s performance. It also has the following advantages: a) it does not require additional electrooculogram/electromyogram channels, long data segments or a large number of EEG channels, b) it allows real-time processing, and c) it reduces signal distortion.

One of the main problems in Brain Computer Interface (BCI) systems is the non-stationary behavior of the electroencephalography (EEG) signals causing problems in real time applications. Another common problem in BCI systems is the situation where the labeled data are scarce. In this study, we take a semi-supervised learning perspective and propose solving both types of problems by updating the BCI system with labels obtained from the outputs of the classifier. To test the approach, data from motor imagery BCI system are used. Attributes extracted from EEG signals are classified with Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). With respect to the static classifiers, accuracy was improved approximately 4% using the proposed adaptation approach in the case of a training dataset. Even though the difference between the performance of static and adaptive classifiers decreases as the size of training data increases, the accuracy of our proposed adaptive classifier remains higher. The proposed approach has also improved the performance of a BCI system around 4% in the case of non-stationary signals as well.

This paper presents an automatic selected channel method for improving brain-computer interface (BCI) performance. Finding an effective channel composition in a BCI system is important for parsing the complexity of these systems. In this study, several statistical methods based on an energy extraction method were used to automatically detect the composition of selected channels. We introduce four techniques for automatic channel selection to optimize the energy extraction method, such as mean, high mean, box, and high in box. The performance of all of the proposed techniques was evaluated based on performance accuracy, compression ratio, and channel mapping on a brain scalp. The performance of the proposed BCI framework was compared against a BCI framework that used a manual technique as the energy extraction method. The proposed BCI framework system used a conventional common spatial pattern (CSP) to extract features from two-class motor imagery EEG signals before employing extreme learning machine (ELM) to classify the features of the EEG signal. As a result, the proposed automatic channel selection methods were found effective in finding optimal channels and provided better performance accuracy. In general, the proposed method improved the conventional BCI performance by up to 16% accuracy and 87% channel compression size. Besides that, the automatic technique also yielded better BCI performance accuracy of up to 5% compared to the BCI system that used the manual technique of energy extraction as its EEG channel selection method.

Most brain-computer interfaces (BCIs) utilize univariate features (i.e., power spectrum or amplitude) for motor imagery (MI) pattern recognition, while less attention has been paid on multivariate analysis that considers information flow between various brain areas through brain connectivity estimations. Most recently, researches have proved that connectivity features were able to characterize different MI tasks. In this study, we investigated the performance of functional connectivity features measured by phase lag index (PLI), weighted phase lag index(wPLI) and phase-locking value(PLV) on MI classification. The widely-used filter-bank common spatial pattern (FBCSP) approach was employed here for performance assessment. A linear support vector machine was trained to classify different MI tasks using two publicly-available datasets from BCI Competition III and IV. The classification results showed that connectivity features achieved classification accuracy >85% in most cases and PLI outperformed all other methods including FBCSP. Our work suggested that functional connectivity features could be utilized as a powerful tool for recognizing different motor intention.

Brain-computer interface (BCI) systems based on spontaneous electroencephalography (EEG) hold the promise to implement human voluntary control of lower-extremity powered exoskeletons. However, current EEG-BCI paradigms do not consider the cooperation of upper and lower limbs during walking, which is inconsistent with natural human stepping patterns. To deal with this problem, this study proposed a stepping-matched human EEG-BCI paradigm that involved actions of both unilateral lower and contralateral upper limbs (also referred to as compound-limbs movement). Experiments were conducted in motor execution (ME) and motor imagery (MI) conditions to validate the feasibility. Common spatial pattern (CSP) proposed subject-specific CSP (SSCSP), and filter-bank CSP (FBCSP) methods were applied for feature extraction, respectively. The best average classification results based on SSCSP indicated that the accuracies of compound-limbs paradigms in ME and MI conditions achieved 89.02% ± 12.84% and 73.70% ± 12.47%, respectively. Although they were 2.03% and 5.68% lower than those of the single-upper-limb mode that does not match human stepping patterns, they were 24.30% and 11.02% higher than those of the single-lower-limb mode. These findings indicated that the proposed compound-limbs EEG-BCI paradigm is feasible for decoding human stepping intention and thus provides a potential way for natural human control of walking assistance devices.

The modern commercial industry is increasingly dependent on the analysis of vast data records. Analysing big electroencephalogram (EEG) signal data (called brain signal data) plays an important role in a wide variety of applications such as healthcare practices, brain computer interface (BCI) systems, innovative education, privacy and security, and biometrics. The key objective of this paper is to establish a methodological framework for identifying communicative intentions of motor disabled people from EEG data for application in BCI systems. The proposed framework is designed based on common spatial pattern (CSP) data method and optimized ensemble (OE) machine learning method for the application of BCI technologies. The CSP method is used for discovering important features from EEG data and finally the extracted features are fed as an input to optimized ensemble (OE) method. The proposed method was tested on BCI Competition III dataset IVa, which contains motor imagery-based EEG signal data. The experimental results show that our proposed method can handle brain signal big data for identifying communicative intentions for an advanced BCI system. We compared the performance of our proposed method with several other existing methods. In comparison with other established methods, our method achieves higher classification accuracy performance. This research assists the experts in processing and analysing EEG signals for BCI applications. It also supports technologists to create a new EEG data analyser for BCI systems.KeywordsElectroencephalographyBrain computer interfaceCommon spatial patternMachine learning

Brain Computer Interface (BCI) systems have gained great visibility in the last years as they represent a quite innovative way of communication and a new instrument aimed at exploring brain functions. A lot of research labs are developing their own BCI system, everyone being involved in some particular aspects of them. At the "Tor Vergata" University our purpose is to develop tools for the evaluation and the optimization of the performances of BCI systems and to delineate some criteria for the analysis and implementation of different BCI systems; also we have defined file formats for BCI data in order to allow the sharing of tools among groups and to create models for the generalization and therefore the unification of the resources. All the tools and routines mentioned are part of the Body Language Framework++ 2.0.

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.

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 Interfacing (BCI) systems, which are a new communicating channel between humans and the computers are growing rapidly. One such a method is based on the Steady State Visual Evoked Potentials (SSVEP), which can be recorded during visual stimulating of the subject by a twinkling light source with a fixed frequency. An important parameter to be considered is the effect of the inter-sources distance on the accuracy of such BCI systems. In particular inter-sources (LEDs) distances of 4, 14, 24, 44 and 64 cm when the sources plane is located 60 cm away from the subject's eyes (producing inter-sources visual angles of 3.8°, 13.4°, 22.6°, 40.2° and 56° respectively) were examined. In addition, four different sweep lengths of 0.5, 1, 2 and 3 seconds are considered. In addition, due to the usage of the AR models for feature extraction from the SSVEP signals, selection of the best AR model together with the best classifier among the LDA, the SVM and the Naive Bayes are studied. It is showed that the BCI system with D=44 cm, AR order of 13 and either the LDA or the SVM classifiers could produce the best results compared to the other cases.

Mostly in natural disaster situations, for example an earthquake, we will face a situation where people get trapped and unable to be found as soon as possible due to the unpredictable terrain. Hence, robot is being seen as scouts that able to adapt and perceive, and usually snake type robot is able to move throughout rubble and small confines. Then, the idea of using modular self-reconfigurable (MSR) robots based on CEBOT in 1988 spark an interest to researchers to fit the search and rescue purpose. Besides that, the idea of controlling robots without manual control but by using “thinking” have fascinated researchers and lead to the development of interfaces between human and machines. In 1970, a Brain Computer Interface (BCI) system has been establish and the BCI system usually is based on the Electroencephalogram (EEG) which is the electrical potentials generated in which the information generated by user and independent of any muscular activities. The implementations of both technologies to each other will frontier for a new alternatives which improves self-reconfigurable modular robot in term of control strategy. As a preliminary framework, a simulation is needed to determine both systems capabilities, and it can be achieved by interfacing the robot simulator, OpenVIBE and Virtual Experimentation Platform (V-REP) with the Python programming language. This study presentation will focus on how to control the modular robot in V-REP environment by using Python language which can be used for interfacing with BCI system created in OpenVIBE software and the simulated robot configuration. Each joint handles of modular robot based on V-REP hierarchy being streamed by Python language. There are several methods for establishment of communication between OpenVIBE and other applications. The method purpose in this paper will utilizing Lab Streaming Layer (LSL) and Python script as mediator. The outcome that is expected to be obtained for this study will be the success of communication establish to control virtual modular robot based on data from OpenVIBE. The future work based on this study will involving the BCI system designed using OpenVIBE and implementation to real modular robot might be applied.

The notion of developing thought controlled devices (games, robots, cars etc.) is becoming increasingly popular with the introduction of low cost commercial headsets that record neuroelectric activity and the extensive research in the area of Brain Computer Interfaces (BCIs). In this paper, we study the feasibility of using a commercial low cost EEG amplifier which has only limited number of electrodes, to develop a motor control BCI system. The objective is to extract brain activity responsible for direction specific imagined and executed motor activity, which can be used to identify the motor task performed by the user using the simultaneously recorded EEG. An experiment is conducted to engage the user in bi-directional horizontal movement execution and imagination of the dominant hand. The analysis includes investigation of the time-frequency bins of the recorded EEG that provides maximum discrimination of directional movement. Further, the features are extracted using Filter Bank Common Spatial Pattern (FBCSP), followed by Fisher Linear Discriminant (FLD) for classification. The classification performance at various time instants of each trial are considered, and a control strategy was introduced at the classifier output to enhance performance. The performance in terms of average classification accuracy over five subjects is obtained as 81.3 % (movement execution) and 82.4 % (movement imagination). The results indicate the applicability of this EEG-BCI system to provide directional motor control to an interfaced device such as a robotic arm or a game element.