Analysis and classification of EEG signals for brain computer interfaces

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.

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

EMG and EOG artifacts in brain computer interface systems: A survey

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.

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.

A new research methodology named Brain Computer Interface (BCI) studies novel human-computer interactions; by means of BCI electronics devices, paralyzed patients are able to interact with the environment using no muscular contractions. This technique provides an external electronics support to all persons with severe motor disabilities, by acquiring in continuous mode the electroencephalogram (EEG) signals and operating some processing to control a computer or other domotics devices. Patients are so allowed to control external devices or to communicate simple messages through the computer, just concentrating their attention either on codified movements or on a letter or icon on a digital keyboard. The use of a customized and optimized spatial filtering technique embedded in the BCI system, based on the detection of the Electroencephalographic activity, improves the accuracy of BCI system itself, thanks to the explicit separation of the signal activity of interest from artefact signals. In this chapter, after an overview of the state-of-the-art research on BCI systems, the spatial filtering problem in EEG signals acquisition will be illustrated. In particular, a spatial filtering algorithm, known as ICA (Independent Component Analysis) and its application will be discussed. Finally, the design and implementation of an embedded system for EEG signals acquisition and real-time processing for BCI applications will be presented. The system is based onto a very performing and reconfigurable hardware platform. Moreover ICA algorithm has been implemented for noise reduction and artifacts removal.

A Brain Computer Interface (BCI) system processes and translates neuronal signals, that mainly comes from EEG instruments, into commands for controlling electronic devices. This system can allow people with motor disabilities to control external devices through the real‐time modulation of their brain waves. In this context an EEG‐based BCI system that allows creative luminous artistic representations is here presented. The system that has been designed and realized in our laboratory interfaces the BCI2000 platform performing real‐time analysis of EEG signals with a couple of moving luminescent twin robots. Experiments are also presented.

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.

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.

A brain-computer interface(BCI) system is a communication channel which transforms a subject's thought process into command signals to control various devices. These systems use electroencephalographic signals or the neuronal activity of many single neurons. The presented study deals with an efficient analysis method of neuronal signals from a BCI System using an independent component analysis(ICA) algorithm. The BCI system was implemented to generate event signals coding movement information of the subject. To apply the ICA algorithm, we obtained the perievent histograms of neuronal signals recorded from prefrontal cortex(PFC) region during target-to-goal(TG) task trials in the BCI system. The neuronal signals were then smoothed over 5ms intervals by low-pass filtering. The matrix of smoothed signals was then rearranged such that each signal was represented as a column and each bin as a row. Each column was also normalized to have a unit variance. As a result, we verified that different patterns of the neuronal signals are dependent on the target position and predefined event signals.

Nowadays, brain-computer interface (BCI) systems are frequently used to connect individuals who have lost their mobility with the outside world. These BCI systems enable individuals to control external devices using brain signals. However, these systems have certain disadvantages for users. This paper proposes a novel approach to minimize the disadvantages of visual stimuli on the eye health of system users in BCI systems employing visual evoked potential (VEP) and P300 methods. The approach employs moving objects with different trajectories instead of visual stimuli. It uses a light-emitting diode (LED) with a frequency of 7 Hz as a condition for the BCI system to be active. The LED is assigned to the system to prevent it from being triggered by any involuntary or independent eye movements of the user. Thus, the system user will be able to use a safe BCI system with a single visual stimulus that blinks on the side without needing to focus on any visual stimulus through moving balls. Data were recorded in two phases: when the LED was on and when the LED was off. The recorded data were processed using a Butterworth filter and the power spectral density (PSD) method. In the first classification phase, which was performed for the system to detect the LED in the background, the highest accuracy rate of 99.57% was achieved with the random forest (RF) classification algorithm. In the second classification phase, which involves classifying moving objects within the proposed approach, the highest accuracy rate of 97.89% and an information transfer rate (ITR) value of 36.75 (bits/min) were achieved using the RF classifier.

The structure and functioning of the human brain has always attracted the attention of scientists. In the past 10-20 years, one of the popular areas of study related to the human brain is the Brain Computer Interface (BCI) systems. Different brain monitoring techniques such as EEG, fNIRS, MEG can be used to observe human brain activities in BCI systems. In addition to the differences in monitoring methods in BCI systems, the focus of system operation may also change. In some BCI systems, it may be preferable to focus on Motor Imagery in another system, while the focus is on the P300 signals in EEG data. This requires a differentiation between the systems during the operational phases of the BCI system. One of the most significant points of system differentiation is the classification stage of the BCI system. In this study, a compilation of classification methods commonly used in Brain Computer Interface systems based on EEG based Motor Imagery will be carried out. This study will provide researchers with information about the methods that can be used in their own systems for classification in Brain Computer Interface systems based on EEG based Motor Imagery.

Brain computer interface (BCI) system is field of neurotechnology that convey command from human brain to a computer. BCI is currently most growing field in the research area. BCI applications have different field such as medical, education, neuroergonomics, self-regulation, games and entertainment, production, security as well as marketing. Electronic devices can be controlled using brain signal called electroencephalography (EEG) to record electrical activity of the brain. The P300 wave is a positive peak of an event related potential (ERP) that occurs 300ms recorded by EEG. One major approach in the BCI research field is the P300 based oddball paradigm, subjects identify infrequent target stimuli within a series of rapid presented standard stimuli. The P300-speller is a widely used BCI system that allows users to communicate characters by focused attention. In our study, we are focusing on BCI system architecture different applications of BCI specifically P300 speller which classifies P300 waves and recognize targeted character, on the dataset II of the third BCI competition.

Recently single trial classification of Event Related Potentials (ERPs) has received much attention to improve Brain Computer Interface (BCI) systems. BCI is one of the most recent fields in computer science which tries to help handicapped people; however, single trial EEG analysis is hard due to its low Signal to Noise Ratio (SNR). Many BCI systems are based on the analysis of ERPs such as P300. P300 is a positive peak that occurs approximately 300ms after a visual stimulus. But electrical potentials produced by blinks and eye movements cause serious problems in analyzing of EEG data. In this paper, a two stage algorithm is presented to detect P300 waves in single trial conditions in the presence of noise and artifact. In the first stage, the raw EEG data are denoised. For this reason, an ICA-wavelet based denoising method is proposed to automatically remove ocular artifact and it is also compared to other denoising methods. In the second stage, a detection algorithm is applied on denoised EEG data in order to discover P300 waves. In this method, a set of new features are extracted by applying ICA on each unknown incoming signal and finally they are classified using a neural network. The proposed method has been tested on more than 10 runs and an average accuracy of 71.5% in these runs is achieved in detecting P300 waves.