A Safe and Efficient Brain-Computer Interface Using Moving Object Trajectories and LED-Controlled Activation.

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.

Objective. Brain–computer interface (BCI) technologies have been intensely studied to provide alternative communication tools entirely independent of neuromuscular activities. Current BCI technologies use electroencephalogram (EEG) acquisition methods that require unpleasant gel injections, impractical preparations and clean-up procedures. The next generation of BCI technologies requires practical, user-friendly, nonintrusive EEG platforms in order to facilitate the application of laboratory work in real-world settings. Approach. A capacitive electrode that does not require an electrolytic gel or direct electrode–scalp contact is a potential alternative to the conventional wet electrode in future BCI systems. We have proposed a new capacitive EEG electrode that contains a conductive polymer-sensing surface, which enhances electrode performance. This paper presents results from five subjects who exhibited visual or auditory steady-state responses according to BCI using these new capacitive electrodes. The steady-state visual evoked potential (SSVEP) spelling system and the auditory steady-state response (ASSR) binary decision system were employed. Main results. Offline tests demonstrated BCI performance high enough to be used in a BCI system (accuracy: 95.2%, ITR: 19.91 bpm for SSVEP BCI (6 s), accuracy: 82.6%, ITR: 1.48 bpm for ASSR BCI (14 s)) with the analysis time being slightly longer than that when wet electrodes were employed with the same BCI system (accuracy: 91.2%, ITR: 25.79 bpm for SSVEP BCI (4 s), accuracy: 81.3%, ITR: 1.57 bpm for ASSR BCI (12 s)). Subjects performed online BCI under the SSVEP paradigm in copy spelling mode and under the ASSR paradigm in selective attention mode with a mean information transfer rate (ITR) of 17.78 ± 2.08 and 0.7 ± 0.24 bpm, respectively. Significance. The results of these experiments demonstrate the feasibility of using our capacitive EEG electrode in BCI systems. This capacitive electrode may become a flexible and non-intrusive tool fit for various applications in the next generation of BCI technologies.

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.

Visual evoked potential (VEP)-related EEG signals have attracted widespread attention in the construction of brain-computer interface (BCI) systems. However, long-term use of the BCI system is prone to fatigue. Effectively coping with fatigue is the key to expanding BCI application scenarios. In this brief, we conduct two VEP experiments and propose a novel, concise and practical convolutional neural network (CNN) model to study and solve this problem. In detail, firstly, in terms of the stimulus frequencies in VEP experiments, we intercept the spectrum sequences of the fundamental waves and two harmonics. All these spectrum sequences are then paralleled input into the proposed multi-harmonic linkage CNN (MHLCNN) model, through three branches. Finally, the linkage characteristics of multiple harmonics are combined for VEP signal classification. The experimental results demonstrate that the proposed model shows good scalability, and can respectively improve the average classification accuracy by 23.17% and 15.85% when applied to steady-state VEP (SSVEP) signals in fatigue state and steady-state motion VEP (SSMVEP) signals, compared with the benchmark methods. All these provide a feasible and high-quality solution for reducing the impact of fatigue on the performance of VEP-based BCI systems.

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) 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 interface (BCI) systems have emerged as an augmentative technology that can provide a promising solution for individuals with motor dysfunctions and for the elderly who are experiencing muscle weakness. Steady-state visually evoked potentials (SSVEPs) are widely adopted in BCI systems due to their high speed and accuracy when compared to other BCI paradigms. In this paper, we apply combined magnitude and phase features for class discrimination in a real-time SSVEP-based BCI platform. In the proposed real-time system users gain control of a motorised bed system with seven motion commands and an idle state. Experimental results amongst eight participants demonstrate that the proposed real-time BCI system can successfully discriminate between different SSVEP signals achieving high information transfer rates (ITR) of 82.73 bits/min. The attractive features of the proposed system include noninvasive recording, simple electrode configuration, excellent BCI response and minimal training requirements.

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.

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.

Brain-computer interfaces (BCIs) have tremendous application potential in communication, mechatronic control and rehabilitation. However, existing BCI systems are bulky, expensive and require laborious preparation before use. This study proposes a practical and user-friendly BCI system without compromising performance. A hybrid asynchronous BCI system was developed based on an elaborately designed wearable electroencephalography (EEG) amplifier that is compact, easy to use and offers a high signal-to-noise ratio (SNR). The wearable BCI system can detect P300 signals by processing EEG signals from three channels and operates asynchronously by integrating blink detection. The wearable EEG amplifier obtains high quality EEG signals and introduces preprocessing capabilities to BCI systems. The wearable BCI system achieves an average accuracy of 94.03±4.65%, an average information transfer rate (ITR) of 31.42±7.39 bits/min and an average false-positive rate (FPR) of 1.78%. The experimental results demonstrate the feasibility and practicality of the developed wearable EEG amplifier and BCI system. Wearable asynchronous BCI systems with fewer channels are possible, indicating that BCI applications can be transferred from the laboratory to real-world scenarios.

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.

Our findings indicate the potential use of the proposed data fusion framework utilizing the graph-based features in the hybrid BCI systems by making the motor imaginary inference more accurate and more robust.

Brain-Computer Interfacing (BCI) systems can be used to improve the quality of life of disabled individuals. Electroencephalography (EEG) Steady-State Visual Evoked Potentials (SSVEP) based BCI systems provide a non-invasive, inexpensive method of communication and control with minimal user training. Among many applications of BCI systems, robot control is widely used to restore motor functions of individuals with severe neuromuscular disabilities. In this paper, a low-cost, single-channel, SSVEP based BCI system is implemented to control the motion of the wrist and the gripper of a robot arm. The SSVEP user commands are generated by focusing the user's gaze on a set of light-emitting diodes (LED) flickering at different frequencies. For the identification of user intent to generate control commands, a classification algorithm is proposed, which is based on Euclidean distance measurement between prominent peaks of the SSVEP Fast Fourier Transform (FFT) spectrum, and the fundamental and harmonic spectral content of stimulus frequency.

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.