P300 Brain-computer Interface Research Articles

Subject-Independent Wearable P300 Brain-Computer Interface Based on Convolutional Neural Network and Metric Learning.

The calibration procedure for a wearable P300 brain-computer interface (BCI) greatly impact the user experience of the system. Each user needs to spend additional time establishing a decoder adapted to their own brainwaves. Therefore, achieving subject independent is an urgent issue for wearable P300 BCI needs to be addressed. A dataset of electroencephalogram (EEG) signals was constructed from 100 individuals by conducting a P300 speller task with a wearable EEG amplifier. A framework is proposed that initially improves cross-subject consistency of EEG features through a common feature extractor. Subsequently, a simple and compact convolutional neural network (CNN) architecture is employed to learn an embedding sub-space, where the mapped EEG features are maximally separated, while pursuing the minimum distance within the same class and the maximum distance between different classes. Finally, the model's generalization capability was further optimized through fine-tuning. The proposed method significantly boosts the average accuracy of wearable P300 BCI to 73.23 ±7.62% without calibration and 78.75±6.37% with fine-tuning. The results demonstrate the feasibility and excellent performance of our dataset and framework. A calibration-free wearable P300 BCI system is feasible, suggesting significant potential for practical applications of the wearable P300 BCI system.

Application of hybrid SSVEP + P300 brain computer interface to control avatar movement in mobile virtual reality gaming environment

IntroductionThis research evaluated the feasibility of a hybrid SSVEP + P300 brain computer interface (BCI) for controlling the movement of an avatar in a virtual reality (VR) gaming environment (VR + BCI). Existing VR + BCI gaming environments have limitations, such as visual fatigue, a lower communication rate, minimum accuracy, and poor system comfort. Hence, there is a need for an optimized hybrid BCI system that can simultaneously evoke the strongest P300 and SSVEP potentials in the cortex. MethodsA BCI headset was coupled with a VR headset to generate a VR + BCI environment. The author developed a VR game in which the avatar’s movement is controlled using the user's cortical responses with the help of a BCI headset. Specifically designed visual stimuli were used in the proposed system to elicit the strongest possible responses from the user's brain. The proposed system also includes an auditory feedback mechanism to facilitate precise avatar movement. Results and conclusionsConventional P300 BCI and SSVEP BCI were also used to control the movements of the avatar, and their performance metrics were compared to those of the proposed system. The results demonstrated that the hybrid SSVEP + P300 BCI system was superior to the other systems for controlling avatar movement.

Effect of inverted faces as visual stimuli on the performance of the hybrid SSVEP + P300 brain computer interface

IntroductionThis study proposes a hybrid brain-computer interface (BCI) system that simultaneously evokes steady-state visual evoked potentials (SSVEP) and event-related potentials (P300). The goal of this study was to improve the performance of the current hybrid SSVEP + P300 BCI systems by incorporating inverted faces into visual stimuli. MethodsIn this study, upright and inverted faces were added to visual stimulus to elicit stronger cortical responses in a hybrid SSVEP + P300 BCI. We also considered triggering the P300 signals with facial stimuli and the SSVEP signals with non-facial stimuli. We have tested four paradigms: the upright face paradigm (UF), the inverted face paradigm (IF), the upright face and flicker paradigm (UFF), and the inverted face and flicker paradigm (IFF). Results and ConclusionsThe results showed that the IFF paradigm evoked more robust cortical responses, which led to enhanced system accuracy and ITR. The IFF paradigm had an average accuracy of 96.6% and a system communication rate of 26.45 bits per second. The UFF paradigm is the best candidate for BCI applications among other paradigms because it provides maximum comfort while maintaining a reasonable ITR.

Considering preferences, speed and the animation of multiple symbols in developing P300 brain-computer interface for children

Purpose Prior research has begun establishing the efficacy of animation in brain-computer interfaces access to augmentative and alternative communication (BCI-AAC). However, the use of animation in P300-BCI-AAC for children is in the early stages and largely limited to single item highlighting of extended durations. In pursuit of practical application, the present study aims to evaluate children’s event-related potential (ERP) characteristics and user experience during a task involving an animated P300-BCI-AAC system. Materials and methods The system utilizes multi-item zoom animations to access a 28-pictorial symbols. Participants completed a fast (100 ms) and slow (200 ms) zoom animation highlighting conditions wherein four pictorial symbols were highlighted concurrently. Results The proposed display appears feasible, eliciting all targeted ERPs. However, ERP amplitudes may be reduced in comparison to single-item animation highlighting, possibly due to distraction. Ratings of mental effort were significantly higher for the 100 ms condition, though differences in the frontal P200/P300 ERP did not achieve significance. Most participants identified a preference for the 100 ms condition, though age may impact preference. Conclusions Overall, findings support the preliminary feasibility of a proposed 28-item interface that utilises group zoom animation highlighting of pictorial symbols. Further research is needed evaluating ERP characteristics and outcomes from online (real-time) use of animation-based P300-BCI-AAC for children with severe speech and physical impairments across multiple training sessions.

Hybrid SSVEP + P300 brain-computer interface can deal with non-stationary cerebral responses with the use of adaptive classification

IntroductionThe non-stationarity of electroencephalograms (EEG) has a substantial effect on the performance of classifiers in brain-computer interface (BCI) systems. To tackle this challenge, an adaptable version of the linear discriminant analysis (LDA) classifier was proposed. Accuracy is crucial when using BCIs for neurorestorative tasks or memory improvement. The accurate comprehension of brain responses facilitates more focused interventions, which may improve neurorestorative outcomes. BCIs equipped with adaptive classifiers are useful for personalizing therapies to individual requirements and for improving neurorestorative processes. Notably, neurorestorative interventions that yield consistent, accurate, and reliable outcomes are more likely to inspire trust and elicit satisfaction in users. MethodsThe proposed classifier continuously modified its parameters in accordance with EEG signals. The covariance matrix and mean values for each pair of classes were the updating parameters. The proposed classifier modified the model parameters by prioritizing current signal data over older signal history. The proposed classifier was tested using a hybrid SSVEP + P300 BCI system. Results and conclusionsThe proposed classifier had an estimated classification accuracy of 97.4%, and was more effective than pooled mean LDA and conventional multiclass LDA classifiers. Increased classification accuracy may increase the responsiveness of neurorestorative interventions and increase the usefulness of BCIs in neurorestoration.

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

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. 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. 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. 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.

Transfer Learning for P300 Brain-Computer Interfaces by Joint Alignment of Feature Vectors.

This article presents a new transfer learning method named group learning, that jointly aligns multiple domains (many-to-many) and an extension named fast alignment that aligns any further domain to previously aligned group of domains (many-to-one). The proposed group alignment algorithm (GALIA) is evaluated on brain-computer interface (BCI) data and optimal hyper-parameter values of the algorithm are studied for classification performance and computational cost. Six publicly available P300 databases comprising 333 sessions from 177 subjects are used. As compared to the conventional subject-specific train/test pipeline, both group learning and fast alignment significantly improve the classification accuracy except for the database with clinical subjects (average improvement: 2.12±1.88%). GALIA utilizes cyclic approximate joint diagonalization (AJD) to find a set of linear transformations, one for each domain, jointly aligning the feature vectors of all domains. Group learning achieves a many-to-many transfer learning without compromising the classification performance on non-clinical BCI data. Fast alignment further extends the group learning for any unseen domains, allowing a many-to-one transfer learning with the same properties. The former method creates a single machine learning model using data from previous subjects and/or sessions, whereas the latter exploits the trained model for an unseen domain requiring no further training of the classifier.

A design and evaluation of wearable p300 brain-computer interface system based on Hololens2

Patients with amyotrophic lateral sclerosis ( ALS ) often have difficulty in expressing their intentions through language and behavior, which prevents them from communicating properly with the outside world and seriously affects their quality of life. The brain-computer interface (BCI) has received much attention as an aid for ALS patients to communicate with the outside world, but the heavy device causes inconvenience to patients in the application process. To improve the portability of the BCI system, this paper proposed a wearable P300-speller brain-computer interface system based on the augmented reality (MR-BCI). This system used Hololens2 augmented reality device to present the paradigm, an OpenBCI device to capture EEG signals, and Jetson Nano embedded computer to process the data. Meanwhile, to optimize the system's performance for character recognition, this paper proposed a convolutional neural network classification method with low computational complexity applied to the embedded system for real-time classification. The results showed that compared with the P300-speller brain-computer interface system based on the computer screen (CS-BCI), MR-BCI induced an increase in the amplitude of the P300 component, an increase in accuracy of 1.7% and 1.4% in offline and online experiments, respectively, and an increase in the information transfer rate of 0.7 bit/min. The MR-BCI proposed in this paper achieves a wearable BCI system based on guaranteed system performance. It has a positive effect on the realization of the clinical application of BCI.

Window-Adjusted Common Spatial Pattern for Detecting Error-Related Potentials in P300 BCI

Under certain task conditions, error-related potential (ErrP) will be elicited, meaning that the subject is perceiving an error, responding to an external error, or engaging in a cognitive process of reinforcement learning. The detection of ErrP on a single trial basis has been studied and applied to improve all kinds of brain–computer interfaces (BCIs). However, the performance of this kind of detection is not currently good enough. In the paper, we proposed a novel method, called window-adjusted common spatial pattern (WACSP), for detecting ErrP in P300 BCI. In this method, the coefficient of determination was introduced to measure the difference of Electroencephalogram (EEG) signals on a channel at a moment and to guide the search of time windows in which EEG differences are significant, and common spatial pattern (CSP) was further used to capture the stable spatial patterns of EEG differences between correct and incorrect responses in each time window. WACSP and the commonly used methods were tested on the data sets that were built using the EEG signals acquired during the P300 BCI experiments with different feedback. The comparisons of accuracy, area under receiver operating characteristics curve (AUC) and F-measure show that WACSP significantly outperforms the commonly used methods. The proposed method can improve ErrP detection based on a single trial.

A spatial-temporal linear feature learning algorithm for P300-based brain-computer interfaces

Speller brain-computer interface (BCI) systems can help neuromuscular disorders patients write their thoughts by using the electroencephalogram (EEG) signals by just focusing on the speller tasks. For practical speller-based BCI systems, the P300 event-related brain potential is measured by using the EEG signal. In this paper, we design a robust machine-learning algorithm for P300 target detection. The novel spatial-temporal linear feature learning (STLFL) algorithm is proposed to extract high-level P300 features. The STLFL method is a modified linear discriminant analysis technique focusing on the spatial-temporal aspects of information extraction. A new P300 detection structure is then proposed based on the combination of the novel STLFL feature extraction and discriminative restricted Boltzmann machine (DRBM) for the classification approach (STLFL + DRBM). The effectiveness of the proposed technique is evaluated using two state-of-the-art P300 BCI datasets. Across the two available databases, we show that in terms of average target recognition accuracy and standard deviation values, the proposed STLFL + DRBM method outperforms traditional methods by 33.5, 78.5, 93.5, and 98.5% for 1, 5, 10, and 15 repetitions, respectively, in BCI competition III datasets II and by 71.3, 100, 100, and 100% for 1, 5, 10, and 15 repetitions, respectively, in BCI competition II datasets II and by 67.5 ± 4, 84.2 ± 2.5, 93.5 ± 1, 96.3 ± 1, and 98.4 ± 0.5% for rapid serial visual presentation (RSVP) based dataset in repetitions 1–5. The method has some advantages over the existing variants including its efficiency, robustness with a small number of training samples, and a high ability to create discriminative features between classes.

A cross-subject decoding algorithm for patients with disorder of consciousness based on P300 brain computer interface.

Brain computer interface (BCI) technology may provide a new way of communication for some patients with disorder of consciousness (DOC), which can directly connect the brain and external devices. However, the DOC patients' EEG differ significantly from that of the normal person and are difficult to collected, the decoding algorithm currently only is trained based on a small amount of the patient's own data and performs poorly. In this study, a decoding algorithm called WD-ADSTCN based on domain adaptation is proposed to improve the DOC patients' P300 signal detection. We used the Wasserstein distance to filter the normal population data to increase the training data. Furthermore, an adversarial approach is adopted to resolve the differences between the normal and patient data. The results showed that in the cross-subject P300 detection of DOC patients, 7 of 11 patients achieved an average accuracy of over 70%. Furthermore, their clinical diagnosis changed and CRS-R scores improved three months after the experiment. These results demonstrated that the proposed method could be employed in the P300 BCI system for the DOC patients, which has important implications for the clinical diagnosis and prognosis of these patients.

Eliminating or Shortening the Calibration for a P300 Brain-Computer Interface Based on a Convolutional Neural Network and Big Electroencephalography Data: An Online Study.

A brain-computer interface (BCI) measures and analyzes brain activity and converts it into computer commands to control external devices. Traditional BCIs usually require full calibration, which is time-consuming and makes BCI systems inconvenient to use. In this study, we propose an online P300 BCI spelling system with zero or shortened calibration based on a convolutional neural network (CNN) and big electroencephalography (EEG) data. Specifically, three methods are proposed to train CNNs for the online detection of P300 potentials: (i) training a subject-independent CNN with data collected from 150 subjects; (ii) adapting the CNN online via a semisupervised learning/self-training method based on unlabeled data collected during the user's online operation; and (iii) fine-tuning the CNN with a transfer learning method based on a small quantity of labeled data collected before the user's online operation. Note that the calibration process is eliminated in the first two methods and dramatically shortened in the third method. Based on these methods, an online P300 spelling system is developed. Twenty subjects participated in our online experiments. Average accuracies of 89.38%, 94.00% and 93.50% were obtained by the subject-independent CNN, the self-training-based CNN and the transfer learning-based CNN, respectively. These results demonstrate the effectiveness of our methods, and thus, the convenience of the online P300-based BCI system is substantially improved.

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.

A P300 Brain-Computer Interface for Lower Limb Robot Control Based on Tactile Stimulation

A P300 Brain-Computer Interface for Lower Limb Robot Control Based on Tactile Stimulation

A Potential Prognosis Indicator Based on P300 Brain-Computer Interface for Patients with Disorder of Consciousness.

For patients with disorders of consciousness, such as unresponsive wakefulness syndrome (UWS) patients and minimally conscious state (MCS) patients, their long treatment cycle and high cost commonly put a heavy burden on the patient's family and society. Therefore, it is vital to accurately diagnose and predict consciousness recovery for such patients. In this paper, we explored the role of the P300 signal based on an audiovisual BCI in the classification and prognosis prediction of patients with disorders of consciousness. This experiment included 18 patients: 10 UWS patients and 8 MCS- patients. At the three-month follow-up, we defined patients with an improved prognosis (from UWS to MCS-, from UWS to MCS+, or from MCS- to MCS+) as "improved patients" and those who stayed in UWS/MCS as "not improved patients". First, we compared and analyzed different types of patients, and the results showed that the P300 detection accuracy rate of "improved" patients was significantly higher than that of "not improved" patients. Furthermore, the P300 detection accuracy of traumatic brain injury (TBI) patients was significantly higher than that of non-traumatic brain injury (NTBI, including acquired brain injury and cerebrovascular disease) patients. We also found that there was a positive linear correlation between P300 detection accuracy and CRS-R score, and patients with higher P300 detection accuracy were likely to achieve higher CRS-R scores. In addition, we found that the patients with higher P300 detection accuracies tend to have better prognosis in this audiovisual BCI. These findings indicate that the detection accuracy of P300 is significantly correlated with the level of consciousness, etiology, and prognosis of patients. P300 can be used to represent the preservation level of consciousness in clinical neurophysiology and predict the possibility of recovery in patients with disorders of consciousness.

End-to-end P300 BCI using Bayesian accumulation of Riemannian probabilities

ABSTRACT In brain–computer interfaces (BCI), most of the approaches based on event-related potential (ERP) focus on the detection of P300, aiming for single trial classification for a speller task, but leaving the character classification out of the scope. To reduce the number of repetitions while maintaining a good character classification, it is critical to embrace the full classification problem. We introduce an end-to-end pipeline, starting from feature extraction, and composed of an ERP-level classification using probabilistic Riemannian MDM which feeds a character-level classification using Bayesian accumulation of confidence across trials. Whereas existing approaches only increase the confidence of a character when it is flashed, our new pipeline, called Bayesian accumulation of Riemannian probabilities (ASAP), update the confidence of each character after each flash. We demonstrate that this seamless processing of information from signal to BCI characters performs significantly better than standard methods on public P300 datasets.

An Evaluation of the P300 Brain-Computer Interface, EyeLink Board, and Eye-Tracking Camera as Augmentative and Alternative Communication Devices.

Augmentative and alternative communication (AAC) systems are important to support communication for individuals with complex communication needs. A recent addition to AAC system options is the brain-computer interface (BCI). This study aimed to compare the clinical application of the P300 speller BCI with two more common AAC systems, the EyeLink board, and an eye-tracking camera. Ten participants without communication impairment (18-35 years of age) used each of the three AAC systems to spell three-letter words in one session. Accuracy and speed of letter selection were measured, and questionnaires were administered to evaluate usability, cognitive workload, and user preferences. The results showed that the BCI was significantly less accurate, slower, and with lower usability and higher cognitive workload compared to the eye-tracking camera and EyeLink board. Participants rated the eye-tracking camera as the most favorable AAC system on all measures. The results demonstrated that while the P300 speller BCI was usable by most participants, it did not function as well as the eye-tracking camera and EyeLink board. The clinical use of the BCI is, therefore, currently difficult to justify for most individuals, particularly when considering the substantial cost and setup resourcing needed. https://doi.org/10.23641/asha.21291384.

Applying Functional Animation to Pictorial Symbols for Supporting P300–Brain–Computer Interface Access to Augmentative and Alternative Communication Devices by Children

Motion techniques, such as highlighting communication items via flip and/or zoom motion, may support BCI-AAC success and comfort for those using P300 brain–computer interface techniques for augmentative and alternative communication device access (BCI-AAC). However, the utilization of motion in P300-BCI-AAC for children is largely unexplored. Therefore, the aims of this investigation are to provide initial insights into the impacts of functional pictorial symbol animation on (a) event-related potentials underlying P300-BCI-AAC performance and (b) children’s user experience. Participants completed two conditions: (1) a static item condition, and (2) a functional animation condition. Following completion, participants completed Likert scale ratings of factors like visual appeal. Animation significantly increased parieto-occipital P200/P300 amplitudes and delayed parieto-occipital N200, P200/P300, and N400 latencies. Further, animation raised ratings of visual appeal to a level approaching significance. Static highlighting significantly increased N400 amplitudes. Findings provide early insights into neural activity underlying P300-BCI-AAC highlighting of pictorial symbols via animation in children and justify further evaluation.

How brain-computer interface technology may improve the diagnosis of the disorders of consciousness: A comparative study.

ObjectiveClinical assessment of consciousness relies on behavioural assessments, which have several limitations. Hence, disorder of consciousness (DOC) patients are often misdiagnosed. In this work, we aimed to compare the repetitive assessment of consciousness performed with a clinical behavioural and a Brain-Computer Interface (BCI) approach.Materials and methodsFor 7 weeks, sixteen DOC patients participated in weekly evaluations using both the Coma Recovery Scale-Revised (CRS-R) and a vibrotactile P300 BCI paradigm. To use the BCI, patients had to perform an active mental task that required detecting specific stimuli while ignoring other stimuli. We analysed the reliability and the efficacy in the detection of command following resulting from the two methodologies.ResultsOver repetitive administrations, the BCI paradigm detected command following before the CRS-R in seven patients. Four clinically unresponsive patients consistently showed command following during the BCI assessments.ConclusionBrain-Computer Interface active paradigms might contribute to the evaluation of the level of consciousness, increasing the diagnostic precision of the clinical bedside approach.SignificanceThe integration of different diagnostic methods leads to a better knowledge and care for the DOC.

A Regional Smoothing Block Sparse Bayesian Learning Method With Temporal Correlation for Channel Selection in P300 Speller.

The P300-based brain–computer interfaces (BCIs) enable participants to communicate by decoding the electroencephalography (EEG) signal. Different regions of the brain correspond to various mental activities. Therefore, removing weak task-relevant and noisy channels through channel selection is necessary when decoding a specific type of activity from EEG. It can improve the recognition accuracy and reduce the training time of the subsequent models. This study proposes a novel block sparse Bayesian-based channel selection method for the P300 speller. In this method, we introduce block sparse Bayesian learning (BSBL) into the channel selection of P300 BCI for the first time and propose a regional smoothing BSBL (RSBSBL) by combining the spatial distribution properties of EEG. The RSBSBL can determine the number of channels adaptively. To ensure practicality, we design an automatic selection iteration strategy model to reduce the time cost caused by the inverse operation of the large-size matrix. We verified the proposed method on two public P300 datasets and on our collected datasets. The experimental results show that the proposed method can remove the inferior channels and work with the classifier to obtain high-classification accuracy. Hence, RSBSBL has tremendous potential for channel selection in P300 tasks.