Visual P300 Mind-Speller Brain-Computer Interfaces: A Walk Through the Recent Developments With Special Focus on Classification Algorithms

Post-stroke aphasia rehabilitation using an adapted visual P300 brain-computer interface training: improvement over time, but specificity remains undetermined

Introduction

This study aimed to evaluate the efficacy of visual P300 brain-computer interface use to support rehabilitation of chronic language production deficits commonly experienced by individuals with a left-sided stroke resulting in post-stroke aphasia.

Methods

The study involved twelve participants, but five dropped out. Additionally, data points were missing for three participants in the remaining sample of seven participants. The participants underwent four assessments-a baseline, pre-assessment, post-assessment, and follow-up assessment. Between the pre-and post-assessment, the participants underwent at least 14 sessions of visual spelling using a brain-computer interface. The study aimed to investigate the impact of this intervention on attention, language production, and language comprehension and to determine whether there were any potential effects on quality of life and well-being.

Results

None of the participants showed a consistent improvement in attention. All participants showed an improvement in spontaneous speech production, and three participants experienced a reduction in aphasia severity. We found an improvement in subjective quality of life and daily functioning. However, we cannot rule out the possibility of unspecific effects causing or at least contributing to these results.

Conclusion

Due to challenges in assessing the patient population, resulting in a small sample size and missing data points, the results of using visual P300 brain-computer interfaces for chronic post-stroke aphasia rehabilitation are preliminary. Thus, we cannot decisively judge the potential of this approach.

Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain-Computer Interface

In this study, we investigated the feasibility of using electroencephalogram (EEG) signals to differentiate between four distinct subject-driven cognitive states: resting state, narrative memory, music, and subtraction tasks. EEG data were collected from seven healthy male participants while performing these cognitive tasks, and the raw EEG signals were transformed into time-frequency maps using continuous wavelet transform. Based on these time-frequency maps, we developed a convolutional neural network model (TF-CNN-CFA) with a channel and frequency attention mechanism to automatically distinguish between these cognitive states. The experimental results demonstrated that the model achieved an average classification accuracy of 76.14% in identifying these four cognitive states, significantly outperforming traditional EEG signal processing methods and other classical image classification algorithms. Furthermore, we investigated the impact of varying lengths of EEG signals on classification performance and found that TF-CNN-CFA demonstrates consistent performance across different window lengths, indicating its strong generalization capability. This study validates the ability of EEG to differentiate higher cognitive states, which could potentially offer a novel BCI paradigm.

Improving the performance of P300-based BCIs by mitigating the effects of stimuli-related evoked potentials through regularized spatial filtering

Objective.the P300-based brain-computer interface (BCI) establishes a communication channel between the mind and a computer by translating brain signals into commands. These systems typically employ a visual oddball paradigm, where different objects (linked to specific commands) are randomly and frequently intensified. Upon observing the target object, users experience an elicitation of a P300 event-related potential in their electroencephalography (EEG). However, detecting the P300 signal can be challenging due to its very low signal-to-noise ratio (SNR), often compromised by the sequence of visual evoked potentials (VEPs) generated in the occipital regions of the brain in response to periodic visual stimuli. While various approaches have been explored to enhance the SNR of P300 signals, the impact of VEPs has been largely overlooked. The main objective of this study is to investigate how VEPs impact P300-based BCIs. Subsequently, the study aims to propose a method for EEG spatial filtering to alleviate the effect of VEPs and enhance the overall performance of these BCIs.Approach.our approach entails analyzing recorded EEG signals from visual P300-based BCIs through temporal, spectral, and spatial analysis techniques to identify the impact of VEPs. Subsequently, we introduce a regularized version of the xDAWN algorithm, a well-established spatial filter known for enhancing single-trial P300s. This aims to simultaneously enhance P300 signals and suppress VEPs, contributing to an improved overall signal quality.Main results.analyzing EEG signals shows that VEPs can significantly contaminate P300 signals, resulting in a decrease in the overall performance of P300-based BCIs. However, our proposed method for simultaneous enhancement of P300 and suppression of VEPs demonstrates improved performance in P300-based BCIs. This improvement is verified through several experiments conducted with real P300 data.Significance.this study focuses on the effects of VEPs on the performance of P300-based BCIs, a problem that has not been adequately addressed in previous studies. It opens up a new path for investigating these BCIs. Moreover, the proposed spatial filtering technique has the potential to further enhance the performance of these systems.

Study on clinical characteristics of event-related potential P300 in elderly schizophrenics and associated risk factors

OBJECTIVE

To investigate the clinical characteristics of event-related potential P300 in elderly schizophrenics with different levels of violence and the risk factors of severe violence.

METHODS

A total of 138 elderly schizophrenic patients from January 2020 to December 2021 in the First Hospital of Hebei Medical University were enrolled in this retrospective analysis. Based on the violence risk assessment, 61, 102, and 145 patients were divided into high-risk, medium-risk, and low-risk groups, respectively. Clinical characteristics, P300 latency, and P300 amplitude were compared among the three groups followed by a logistic regression analysis of elderly schizophrenics with severe violence.

RESULTS

The latency of P300 in the high-risk group was higher than that in the low-risk group (p < .05). The P300 amplitude of patients in the high-risk group was significantly lower than that in the low-risk group (p < .05). Univariate logistic regression analysis showed that previous history of violence, delusion of persecution, P300 latency, and amplitude were independent influencing factors of severe violence in elderly schizophrenics (odds ratio [OR]: 0.022, 95% confidence interval [CI]: 0.007-0.067, p < .001; OR: 0.118, 95% CI: 0.043-1.763, p = .037; OR: 1.289, 95% CI: 1.142-1.673, p < .001; and OR: 0.049, 95% CI: 0.021-0.067, p < 0.001, respectively). After adjusting gender, age, and other confounding factors, multivariate logistic regression analysis showed that delusion of persecution, P300 latency, and P300 amplitude were associated with severe violence in elderly schizophrenics (OR: 2.211, 95% CI: 0.061-4.067, p < .001; OR: 2.006, 95% CI: 1.421-2.721, p = .017; and OR: 0.067, 95% CI: 0.037-0.276; p < .001; respectively).

CONCLUSION

The latency and amplitude of P300 can be used as effective neuroelectrophysiological indicators to evaluate the violence level of elderly schizophrenics. Delusion of persecution, P300 latency, and P300 amplitude were independent influencing factors of severe violence in elderly schizophrenics.

Enhancing motor imagery detection efficacy using multisensory virtual reality priming

Brain-computer interfaces (BCI) have been developed to allow users to communicate with the external world by translating brain activity into control signals. Motor imagery (MI) has been a popular paradigm in BCI control where the user imagines movements of e.g., their left and right limbs and classifiers are then trained to detect such intent directly from electroencephalography (EEG) signals. For some users, however, it is difficult to elicit patterns in the EEG signal that can be detected with existing features and classifiers. As such, new user control strategies and training paradigms have been highly sought-after to help improve motor imagery performance. Virtual reality (VR) has emerged as one potential tool where improvements in user engagement and level of immersion have shown to improve BCI accuracy. Motor priming in VR, in turn, has shown to further enhance BCI accuracy. In this pilot study, we take the first steps to explore if multisensory VR motor priming, where haptic and olfactory stimuli are present, can improve motor imagery detection efficacy in terms of both improved accuracy and faster detection. Experiments with 10 participants equipped with a biosensor-embedded VR headset, an off-the-shelf scent diffusion device, and a haptic glove with force feedback showed that significant improvements in motor imagery detection could be achieved. Increased activity in the six common spatial pattern filters used were also observed and peak accuracy could be achieved with analysis windows that were 2 s shorter. Combined, the results suggest that multisensory motor priming prior to motor imagery could improve detection efficacy.

Advances in P300 brain-computer interface spellers: toward paradigm design and performance evaluation

A brain-computer interface (BCI) is a non-muscular communication technology that provides an information exchange channel for our brains and external devices. During the decades, BCI has made noticeable progress and has been applied in many fields. One of the most traditional BCI applications is the BCI speller. This article primarily discusses the progress of research into P300 BCI spellers and reviews four types of P300 spellers: single-modal P300 spellers, P300 spellers based on multiple brain patterns, P300 spellers with multisensory stimuli, and P300 spellers with multiple intelligent techniques. For each type of P300 speller, we further review several representative P300 spellers, including their design principles, paradigms, algorithms, experimental performance, and corresponding advantages. We particularly emphasized the paradigm design ideas, including the overall layout, individual symbol shapes and stimulus forms. Furthermore, several important issues and research guidance for the P300 speller were identified. We hope that this review can assist researchers in learning the new ideas of these novel P300 spellers and enhance their practical application capability.

Effects of the concentration level, eye fatigue and coffee consumption on the performance of a BCI system based on visual ERP-P300

BACKGROUND

A widely used paradigm for Brain-Computer Interfaces (BCI) is based on detecting P300 Event-Related Potentials (ERPs) in response to stimulation and concentration tasks. An open challenge corresponds to maximizing the performance of a BCI by considering artifacts arising from the user's cognitive and physical conditions during task execution.

NEW METHOD

In this study, an analysis of the performance of a visual BCI-P300 system was performed under the metrics of Sensitivity (Sen), Specificity (Spe), Accuracy (Acc), and Area-Under the ROC Curve (AUC), considering the main reported factors affecting the neurophysiological behavior of the P300 signal: Concentration Level, Eye Fatigue, and Coffee Consumption.

COMPARISON WITH EXISTING METHODS

We compared the performance of three P300 signal detection methods (MA-LDA, CCA-RLR, and MA+CCA-RLR) using a public database (GigaScience) in different groups. Data were segmented according to three factors of interest: high and low levels of concentration, high and low eye fatigue, and coffee consumption at different times.

RESULTS

The results showed a significant improvement between 3% and 6% for the metrics evaluated for identifying the P300 signal in relation to concentration levels and coffee consumption.

CONCLUSION

P300 signal can be influenced by physical and mental factors during the execution of ERPs evocation tasks, which could be controlled to maximize the interface's capacity to detect the individual's intention.

Classification of Event-Related Potentials with Regularized Spatiotemporal LCMV Beamforming

The usability of EEG-based visual brain–computer interfaces (BCIs) based on event-related potentials (ERPs) benefits from reducing the calibration time before BCI operation. Linear decoding models, such as the spatiotemporal beamformer model, yield state-of-the-art accuracy. Although the training time of this model is generally low, it can require a substantial amount of training data to reach functional performance. Hence, BCI calibration sessions should be sufficiently long to provide enough training data. This work introduces two regularized estimators for the beamformer weights. The first estimator uses cross-validated L2-regularization. The second estimator exploits prior information about the structure of the EEG by assuming Kronecker–Toeplitz-structured covariance. The performances of these estimators are validated and compared with the original spatiotemporal beamformer and a Riemannian-geometry-based decoder using a BCI dataset with P300-paradigm recordings for 21 subjects. Our results show that the introduced estimators are well-conditioned in the presence of limited training data and improve ERP classification accuracy for unseen data. Additionally, we show that structured regularization results in lower training times and memory usage, and a more interpretable classification model.

Experimental Validation of the Cumulative MDRM in theP300 Speller Machine

The P300 speller Machine is among the leading applications of the electroencephalography (EEG)-based brain computer interfaces (BCIs), it is still a benchmark and a persistent challenge for the BCI Community. EEG signal classification represents the key piece of a BCI chain. The minimum distance to Riemannian mean (MDRM) belongs to these classification methods emerging in different BCI applications such as text spelling by thought. Based on a binary classification of each covariance matrix separately, character prediction is done according to the highest score across the whole set of all repetitions. Minimum cumulative distance to Riemannian mean (MCDRM) is a Cumulative variant of the MDRM, perfectly adapted to the P300 Speller Machine. The power of this variant is that prediction takes a more global proceeding involving the n repetitions together. Indeed, thanks to cumulative distances selected row and column are those having the covariance matrices both closer to the Target barycenter and farther from the non-Target one. This variant overcomes the main MDRM limitations as it improves inter-sessional generalization, allows optimal use of all repetitions and reduces considerably the risk of conflict appearing during the selection of rows and columns leading to character prediction. We applied this variant to the raw signals of Data set II-b of Berlin BCI and compared to the published results the MCDRM offers significantly higher results: 97.5% of correct predictions compared to the 96.5% of the competition winner. The MCDRM fits best with the P300 Speller machine, especially when dealing with noisy signals that requires intelligent and optimal usage of the n repetitions.

Extending Brain-Computer Interface Access with a Multilingual Language Model in the P300 Speller

Brain-computer interfaces (BCI) such as the P300 speller have the potential to restore communication to advanced-stage neuromuscular disease patients. Research has improved typing speed and accuracy through innovations including the use of language models. While significant advances have been made, implementations have largely been restricted to a single language, primarily English. It is unclear whether these improvements would extend to other languages that present potential technical hurdles due to different alphabets and grammatical structures. Here, we adapt a language model-based classifier designed for English to two other languages, Spanish and Greek, to demonstrate the generalizability of these methods. Online experimental trials with 30 healthy native English, Spanish, and Greek speakers showed no significant difference between performances using the different versions of the system (66.20 vs. 61.97 vs. 60.89 bits/minute). Extending these methods across languages allows for expanding access to BCI systems to other populations, particularly in the developing world.

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions

Brain-Computer Interface (BCI) is a technology that uses electroencephalographic (EEG) signals to control external devices, such as Functional Electrical Stimulation (FES). Visual BCI paradigms based on P300 and Steady State Visually Evoked potentials (SSVEP) have shown high potential for clinical purposes. Numerous studies have been published on P300- and SSVEP-based non-invasive BCIs, but many of them present two shortcomings: (1) they are not aimed for motor rehabilitation applications, and (2) they do not report in detail the artificial intelligence (AI) methods used for classification, or their performance metrics. To address this gap, in this paper the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to prepare a systematic literature review (SLR). Papers older than 10 years, repeated or not related to a motor rehabilitation application, were excluded. Of all the studies, 51.02% referred to theoretical analysis of classification algorithms. Of the remaining, 28.48% were for spelling, 12.73% for diverse applications (control of wheelchair or home appliances), and only 7.77% were focused on motor rehabilitation. After the inclusion and exclusion criteria were applied and quality screening was performed, 34 articles were selected. Of them, 26.47% used the P300 and 55.8% the SSVEP signal. Five applications categories were established: Rehabilitation Systems (17.64%), Virtual Reality environments (23.52%), FES (17.64%), Orthosis (29.41%), and Prosthesis (11.76%). Of all the works, only four performed tests with patients. The most reported machine learning (ML) algorithms used for classification were linear discriminant analysis (LDA) (48.64%) and support vector machine (16.21%), while only one study used a deep learning algorithm: a Convolutional Neural Network (CNN). The reported accuracy ranged from 38.02 to 100%, and the Information Transfer Rate from 1.55 to 49.25 bits per minute. While LDA is still the most used AI algorithm, CNN has shown promising results, but due to their high technical implementation requirements, many researchers do not justify its implementation as worthwile. To achieve quick and accurate online BCIs for motor rehabilitation applications, future works on SSVEP-, P300-based and hybrid BCIs should focus on optimizing the visual stimulation module and the training stage of ML and DL algorithms.

A Spelling Paradigm With an Added Red Dot Improved the P300 Speller System Performance

The traditional P300 speller system uses the flashing row or column spelling paradigm. However, the classification accuracy and information transfer rate of the P300 speller are not adequate for real-world application. To improve the performance of the P300 speller, we devised a new spelling paradigm in which the flashing row or column of a virtual character matrix is covered by a translucent green circle with a red dot in either the upper or lower half (GC-RD spelling paradigm). We compared the event-related potential (ERP) waveforms with a control paradigm (GC spelling paradigm), in which the flashing row or column of a virtual character matrix was covered by a translucent green circle only. Our experimental results showed that the amplitude of P3a at the parietal area and P3b at the frontal–central–parietal areas evoked by the GC-RD paradigm were significantly greater than those induced by the GC paradigm. Higher classification accuracy and information transmission rates were also obtained in the GC-RD system. Our results indicated that the added red dots increased attention and visuospatial information, resulting in an amplitude increase in both P3a and P3b, thereby improving the performance of the P300 speller system.

A P300 Brain-Computer Interface With a Reduced Visual Field

A P300 brain-computer interface (BCI) is a paradigm, where text characters are decoded from event-related potentials (ERPs). In a popular implementation, called P300 speller, a subject looks at a display where characters are flashing and selects one character by attending to it. The selection is recognized as the item with the strongest ERP. The speller performs well when cortical responses to target and non-target stimuli are sufficiently different. Although many strategies have been proposed for improving the BCI spelling, a relatively simple one received insufficient attention in the literature: reduction of the visual field to diminish the contribution from non-target stimuli. Previously, this idea was implemented in a single-stimulus switch that issued an urgent command like stopping a robot. To tackle this approach further, we ran a pilot experiment where ten subjects operated a traditional P300 speller or wore a binocular aperture that confined their sight to the central visual field. As intended, visual field restriction resulted in a replacement of non-target ERPs with EEG rhythms asynchronous to stimulus periodicity. Changes in target ERPs were found in half of the subjects and were individually variable. While classification accuracy was slightly better for the aperture condition (84.3 ± 2.9%, mean ± standard error) than the no-aperture condition (81.0 ± 2.6%), this difference was not statistically significant for the entire sample of subjects (N = 10). For both the aperture and no-aperture conditions, classification accuracy improved over 4 days of training, more so for the aperture condition (from 72.0 ± 6.3% to 87.0 ± 3.9% and from 72.0 ± 5.6% to 97.0 ± 2.2% for the no-aperture and aperture conditions, respectively). Although in this study BCI performance was not substantially altered, we suggest that with further refinement this approach could speed up BCI operations and reduce user fatigue. Additionally, instead of wearing an aperture, non-targets could be removed algorithmically or with a hybrid interface that utilizes an eye tracker. We further discuss how a P300 speller could be improved by taking advantage of the different physiological properties of the central and peripheral vision. Finally, we suggest that the proposed experimental approach could be used in basic research on the mechanisms of visual processing.