Sparse optimal score based on generalized elastic net model for brain computer interface

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.

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.

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.

Importance of Graphical User Interface in the design of P300 based Brain–Computer Interface systems

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.

It is commonly believed that brain has faster reaction speed and higher reaction accuracy on visual-auditory bimodal stimuli than single modal stimuli in current neuropsychological researches, while visual-auditory bimodal stimuli (VABS) do not show corresponding superiority in BCI system. This paper aims at investigating whether semantically congruent stimuli could also get better performance than semantically incongruent stimuli in Brain Computer Interface (BCI) system. Two VABS based paradigms (semantically congruent or incongruent) were conducted in this study. 10 healthy subjects participated in the experiment in order to compare the two paradigms. The results indicated that the higher Event-related potential (ERP) amplitude of semantic incongruent paradigm were observed both in target and non-target stimuli. Nevertheless, we didn't observe significant difference of classification accuracy between congruent and incongruent conditions. Most participants showed their preference on semantically congruent condition for less workload needed. This finding demonstrated that semantic congruency has positive effect on behavioral results (less workload) and insignificant effect on system efficiency.

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 brain computer interface (BCI) is an alternative communication method for those living with physically disabling conditions such as neuromuscular disorders, traumatic brain injuries, and stroke that uses a cap with embedded electrodes to read electroencephalography (EEC) signals and uses them to control a computer, Specifically, the P300, a naive response to target stimuli, is used to select letters from a matrix. The system provides those with no neuromuscular control with a novel channel for communication. To date, little to no work has investigated the effects of mental fatigue arising from short-term BCI use on the signals that are used to control the BCI itself. Methods typically used to characterize fatigue in EEG signals employ spectral analysis techniques that are confounded by periodic stimulus presentation in the BCI protocol. The characterization of the effects of fatigue could enable developers to create BCI systems that compensate for or prevent fatigue, which could improve overall usability. Eleven healthy subjects from the Philadelphia region participated in trials using the P300-based BCI alongside cognitive and self-reported measures of fatigue under informed consent. The changes in EEG signals related to increased mental fatigue occurred in a predictable manner, forming a "checkmark" pattern in P300 amplitude across BCI trials, mirroring the results of a 2006 study investigating fatigues effects on the P300 signal specifically. Future work may incorporate this pattern into adaptive BCI systems capable of detecting and compensating for mental fatigue during longterm BCI use. Additionally, a software package was designed and distributed to help accelerate and standardize research investigating event-related potentials (ERPs) such as the P300 wave. The package, created in MATLAB, allows the user to rapidly assess ERP quality and perform in-depth analysis of large quantities of data.

Recent years, researches on ERP-based brain computer interface (BCI) were increasingly popular. Visual and auditory based multimodal stimuli paradigms have attracted more and more attention, while studies on the effect of experimental parameters on system performance of them were rarely reported. This study aims at investigating the effects of stimulus-onset asynchrony (SOA) on audiovisual stimuli based BCI system. Therefore, paradigms with different SOA were conducted with long SOA (1000ms), medium SOA (500ms) and short SOA (200ms) paradigms. Visual stimuli paradigms, auditory stimuli paradigms and audiovisual bimodal stimuli paradigms were also included in the study of SOA effect on BCI system. Fifteen healthy subjects participated in the experiment. We analyzed event-related potential (ERP) of mono-visual paradigm and audiovisual paradigm. Results revealed that SOA has no significant effect discrimination of target and non-target stimuli. We found that there is little difference in the latencies of the early components among distinct paradigms, but the difference is reflected in the amplitude which is mainly related to the type of stimulus. This study could provide theoretical guidance for the design of new paradigms on audiovisual bimodal BCI system.

Functional brain network and multichannel analysis for the P300-based brain computer interface system of lying detection

This study provides critical insights into next-generation BCI systems, specifically addressing the challenges of visual and physical fatigue, as well as performance limitations, in VR and MR environments. Conventional Event-Related Potential (ERP)-based spellers typically rely on two-dimensional visual stimuli presented on desktop LED screens. With the emergence of immersive head-mounted displays (HMDs), there is growing interest in integrating ( VR ) and ( MR ) into brain-computer interface (BCI) systems. In this study, we implemented ERP speller systems across Desktop, VR , and MR environments, evaluating (1) P300/N200 mean amplitudes, (2) spelling performance, and (3) physiological and physical fatigue. Using Linear Discriminant Analysis (LDA) alongside Random Forest (RF), k-Nearest Neighbors (kNN), and XGBoost classifiers, we found that the Desktop environment shows higher P300 and N200 amplitudes, achieving a spelling accuracy of 75% and an Information Transfer Rate (ITR) of 26.1 bits/min, compared to MR (63.4% accuracy, 20.5 bits/min) and VR (60.8% accuracy, 19.8 bits/min). Cross-session experiments, analyzed via two-sided paired t -tests, revealed stable P300 signals in Desktop settings but significant performance declines in VR and MR during prolonged use. Symptom questionnaires indicated that MR induced the highest visual fatigue, while VR caused the most physical fatigue. These findings highlight critical challenges in VR / MR BCI systems, including visual and physical stress and performance degradation, while demonstrating the potential of MR -P300 spellers for applications in neurorehabilitation and assistive communication for motor-impaired individuals. Our results underscore the need for optimized 3D rendering, adaptive user-system mechanisms, and advanced machine learning to enhance next-generation BCI performance in immersive environments.

Electroencephalography (EEG) is a technique used to observe brain activity by measuring the dynamic changes of the electric field induced by neurons' activity. Brain-computer interface (BCI) systems are used in cognitive psychology examinations measuring the changes of brain activities. This paper presents a validation methodology to characterize BCI systems with wireless communication interface and the applicability on a preselected BCI system. This way, the delay, the functionality, and the frequency selectivity can be determined of the overall BCI system, taking into account the effect of the hardware, the software, and the electrodes, avoiding noise artifacts. The presented and validated BCI system proved to be successfully applied in ERP EEG measurements such as steady-state visually evoked potential, pattern-reversal visually evoked potential, and P300 event-related potential.

During recent years there has been a growing interest in Brain Computer Interface (BCI) systems as an alternative means of interaction with the external world for people with severe motor disabilities. The use of the P300 event-related potentials as control feature allows users to choose between various options (letters or icons) requiring a very short calibration phase. The aim of this work is to improve performances and flexibility of P300 based BCIs. An efficient BCI system should be able to understand user's intentions from the ongoing EEG, abstaining from doing a selection when the user is engaged in a different activity, and changing its speed of selection depending on current user's attention level. Our self-paced system addresses all these issues representing an important step beyond the classical synchronous P300 BCI that forces the user in a continuous control task. Experimentation has been performed on 10 healthy volunteers acting on a BCI-controlled domestic environment in order to demonstrate the potential usability of BCI systems in everyday life. Results show that the self-paced BCI increases information transfer rate with respect to the synchronous one, being very robust, at the same time, in avoiding false negatives when the user is not engaged in a control task.

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