A Novel Three-Dimensional P300 Speller Based on Stereo Visual Stimuli

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.

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.

A novel hybrid BCI speller based on RSVP and SSVEP paradigm

Brain–computer interface (BCI) P300 speller can help severely disabled patients communicate and control with external machines or robots, so that the classification methods of P300 electroencephalogram (EEG) signal play an important role in the development of BCI system and technology. In this article, a novel support vector neural network (SVNN) is proposed and developed to obtain more accurate and effective EEG classification results. It is the first time to combine linear variational inequality based primal-dual neural network with convex quadratic programming problem based on support vector machine to solve the classification problem. It has been proved that the SVNN globally converges to the optimal solution of convex optimization problem and iterates the parameters in the form of matrix, which means that the method has global convergence and parallelism. The proposed SVNN method is used to solve the classification problem of P300 EEG signals. Experimental results on dataset IIb from BCI competition II and dataset II from BCI competition III show that the accuracy of the proposed SVNN method is 100% and 98%, respectively. Compared with most of the state-of-the-art algorithms, SVNN has the highest recognition accuracy and information transfer rate.

Robust estimation of event-related potentials via particle filter

A variety of experimental paradigms have been proposed in the field of Brain-Computer Interface(BCI). Among them, the P300 speller allows participators to input characters to a computer directly from their own brains. Estimating available features of P300 from raw electroencephalogram(EEG) is a key step of implementing P300 speller. In this paper, a novel combination of Autoregressive model and sparse Wavelet representation is proposed to estimate the P300 features in raw EEG acquired from the P300 speller experiments. Instead of superposition, the P300 features are estimated from raw EEG of single trial in this way. By introducing this method to process signals for BCI, the number of repeated trials may be reduced so that the information transfer rate of P300 speller could be remarkably improved. The proposed approach was tested in off-line data. The results show that the number of repeated trials for a wanted character could be reduced to 4 in general when the feature estimation method is used together with the linear discriminant functions.

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.

A classification algorithm of an SSVEP brain-Computer interface based on CCA fusion wavelet coefficients

Brain-Computer Interfaces (BCIs) are powerful tools for enabling communication between people and the surrounding world by directly utilizing brain activity and avoiding motor pathways. Before moving into invasive implantation of BCIs, a key issue must be resolved—localization of the areas for implantation, which might vary depending on the chosen BCI type as well as on the individual person’s characteristics. In this study, we aimed to evaluate the possibility of non-invasive navigation of subdural electrode implantation for P300 speller BCI by using magnetoencephalogaphy (MEG). The accuracy of subdural P300 speller performance based on the sites identified with MEG was comparable with the performance based on the sites identified from subdural electrode grids—80% and 90% averaged accuracy, respectively. Our study demonstrates the feasibility of using MEG as a non-invasive tool for navigating electrode implantation required for high accuracy invasive P300 speller control.

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 Interfaces (BCIs) are bidirectional devices that have allowed people to control computers or external devices through their brain activity. The P300 Speller is one of the most widely used BCI applications, where subjects can transmit textual information mentally with satisfactory performance. However, the P300 Speller still has room for improvement in practical use, such as selecting the best balance between accuracy and speed. Based on a lack of literature in this direction, this study evaluates two distinct approaches to the P300 Speller. The first is based on rows and columns following the traditional implementation, while the second is based on regions, employing subsets of characters during spelling. In both approaches, the effects of two different stimulus presentation parameters (the number of repetitions per stimulus and the interval between them) on the accuracy and performance efficiency of the P300 Speller are studied. The results show that both approaches obtain similar values in terms of detection performance, obtaining around 75% F1-score for predicting a character with four series of 12 blinks per character. In addition, the region-based approach presents a more robust scheme for false predictions, maintaining a similar spelling duration. The theoretical study performed indicates that spelling a character requires around one minute.

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

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.

Deep learning based on Batch Normalization for P300 signal detection

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.