P300 Detection Research Articles

Inter-participant transfer learning with attention based domain adversarial training for P300 detection

A Brain-computer interface (BCI) system establishes a novel communication channel between the human brain and a computer. Most event related potential-based BCI applications make use of decoding models, which requires training. This training process is often time-consuming and inconvenient for new users. In recent years, deep learning models, especially participant-independent models, have garnered significant attention in the domain of ERP classification. However, individual differences in EEG signals hamper model generalization, as the ERP component and other aspects of the EEG signal vary across participants, even when they are exposed to the same stimuli. This paper proposes a novel One-source domain transfer learning method based Attention Domain Adversarial Neural Network (OADANN) to mitigate data distribution discrepancies for cross-participant classification tasks. We train and validate our proposed model on both a publicly available OpenBMI dataset and a Self-collected dataset, employing a leave one participant out cross validation scheme. Experimental results demonstrate that the proposed OADANN method achieves the highest and most robust classification performance and exhibits significant improvements when compared to baseline methods (CNN, EEGNet, ShallowNet, DeepCovNet) and domain generalization methods (ERM, Mixup, and Groupdro). These findings underscore the efficacy of our proposed method.

From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual presentation-based brain-computer interfaces

Objective. Brain-computer interfaces (BCI) have been extensively researched in controlled lab settings where the P300 event-related potential (ERP), elicited in the rapid serial visual presentation (RSVP) paradigm, has shown promising potential. However, deploying BCIs outside of laboratory settings is challenging due to the presence of contaminating artifacts that often occur as a result of activities such as talking, head movements, and body movements. These artifacts can severely contaminate the measured EEG signals and consequently impede detection of the P300 ERP. Our goal is to assess the impact of these real-world noise factors on the performance of a RSVP-BCI, specifically focusing on single-trial P300 detection. Approach. In this study, we examine the impact of movement activity on the performance of a P300-based RSVP-BCI application designed to allow users to search images at high speed. Using machine learning, we assessed P300 detection performance using both EEG data captured in optimal recording conditions (e.g. where participants were instructed to refrain from moving) and a variety of conditions where the participant intentionally produced movements to contaminate the EEG recording. Main results. The results, presented as area under the receiver operating characteristic curve (ROC-AUC) scores, provide insight into the significant impact of noise on single-trial P300 detection. Notably, there is a reduction in classifier detection accuracy when intentionally contaminated RSVP trials are used for training and testing, when compared to using non-intentionally contaminated RSVP trials. Significance. Our findings underscore the necessity of addressing and mitigating noise in EEG recordings to facilitate the use of BCIs in real-world settings, thus extending the reach of EEG technology beyond the confines of the laboratory.

Best performance with fewest resources: Unveiling the most resource-efficient Convolutional Neural Network for P300 detection with the aid of Explainable AI

Convolutional Neural Networks (CNNs) have shown remarkable prowess in detecting P300, an Event-Related Potential (ERP) crucial in Brain–Computer Interfaces (BCIs). Researchers persistently seek simple and efficient CNNs for P300 detection, exemplified by models like DeepConvNet, EEGNet, and SepConv1D. Noteworthy progress has been made, manifesting in reducing parameters from millions to hundreds while sustaining state-of-the-art performance. However, achieving further simplification or performance improvement beyond SepConv1D appears challenging due to inherent oversimplification. This study explores landmark CNNs and P300 data with the aid of Explainable AI, proposing a simpler yet superior-performing CNN architecture which incorporates (1) precise separable convolution for feature extraction of P300 data, (2) adaptive activation function tailored for P300 data, and (3) customized large learning rate schedules for training P300 data. Termed the Minimalist CNN for P300 detection (P300MCNN), this novel model is characterized by its requirement of the fewest filters and epochs to date, concurrently achieving best performance in cross-subject P300 detection. P300MCNN not only introduces groundbreaking concepts for CNN architectures in P300 detection but also showcases the importance of Explainable AI in demystifying the “black box” design of CNNs.

Optimization of transfer learning based on source sample selection in Euclidean space for P300-based brain-computer interfaces.

Event-related potentials (ERPs), such as P300, are widely utilized for non-invasive monitoring of brain activity in brain-computer interfaces (BCIs) via electroencephalogram (EEG). However, the non-stationary nature of EEG signals and different data distributions among subjects create significant challenges for implementing real-time P300-based BCIs. This requires time-consuming calibration and a large number of training samples. To address these challenges, this study proposes a transfer learning-based approach that uses a convolutional neural network for high-level feature extraction, followed by Euclidean space data alignment to ensure similar distributions of extracted features. Furthermore, a source selection technique based on the Euclidean distance metric was applied to measure the distance between each source feature sample and a reference point from the target domain. The samples with the lowest distance were then chosen to increase the similarity between source and target datasets. Finally, the transferred features are applied to a discriminative restricted Boltzmann machine classifier for P300 detection. The proposed method was evaluated on the state-of-the-art BCI Competition III dataset II and rapid serial visual presentation dataset. The results demonstrate that the proposed technique achieves an average accuracy of 97% for both online and offline after 15 repetitions, which is comparable to the state-of-the-art methods. Notably, the proposed approach requires <½ of the training samples needed by previous studies. Therefore, this technique offers an efficient solution for developing ERP-based BCIs with robust performance against reduced a number of training data.

A Hybrid Brain–Computer Interface Combining P300 Potentials and Emotion Patterns for Detecting Awareness in Patients With Disorders of Consciousness

In this study, a hybrid brain-computer interface (BCI) system combining P300 potential and emotion patterns was proposed to improve the performance of awareness detection. Two video clips were flashed randomly to evoke the P300 potential, while a laughing or crying video clip was used to induce the corresponding emotion pattern. The subjects were asked to concentrate on the laughing or crying video clip cued by the instruction and to count the flashes of the corresponding video clip. Two layers of classification were developed. In the first layer, P300 detection and emotion recognition were performed separately using two support vector machine (SVM) classifiers. Specifically, the activation, spatial and connection patterns were fused in emotion recognition. In the second layer, the SVM scores of P300 detection and emotion recognition were fed into another SVM classifier to determine which video clip the subjects responded to. Six healthy subjects and eight patients with disorders of consciousness (DOC) were involved in the command-following experiment. The results showed that the accuracy of the hybrid BCI system was better than those of the single-modality systems. Furthermore, three patients were able to perform tasks (66%-72%) using our hybrid BCI, which indicated their residual awareness and emotion-related abilities.

Enhancing P300-Based Brain-Computer Interfaces with Hybrid Transfer Learning: A Data Alignment and Fine-Tuning Approach

A new approach is introduced to address the subject dependency problem in P300-based brain-computer interfaces (BCI) by using transfer learning. The occurrence of P300, an event-related potential, is primarily associated with changes in natural neuron activity and elicited in response to infrequent stimuli, which can be monitored non-invasively through an electroencephalogram. However, implementing P300-based BCI in real-time requires many training samples and time-consuming calibration, making it challenging to use in practical applications. To tackle these challenges, the proposed approach harnesses the high-level feature extraction capability of a deep neural network, achieved through fine-tuning. To ensure similar distributions of feature extraction data, the approach of aligning data in Euclidean space is employed, which is then applied to a discriminatively restricted Boltzmann machine with a single layer for P300 detection. The performance of the proposed method on the BCI Competition III dataset II and the BCI competition II dataset II, the state-of-the-art dataset, was evaluated and compared with previous studies. The results showed that robust performance could be achieved using a small number of training samples, demonstrating the effectiveness of the transfer learning approach in P300-based BCI applications.

A novel multiclass-based framework for P300 detection in BCI matrix speller: Temporal EEG patterns of non-target trials vary based on their position to previous target stimuli

Brain–computer interface (BCI) provides a new communication pathway for severely disabled people and enables them to communicate with external world using only their brain activity. P300-based BCI speller helps patients spell words using their brain signals.Until now, binary-classification-based approaches have been used for P300 detection. This study demonstrates that binary-classification-based approaches may not be appropriate for P300 detection. We proved that temporal EEG patterns of non-target trials are different based on their position to previous target stimuli. Therefore, considering all non-target trials in only one group makes distinguishing target from non-target components difficult for machine learning algorithms and, consequently, deteriorate character recognition accuracy. This study introduced a novel approach for P300 detection in BCI spellers. In this study, we first divided non-target trials into several groups according to their temporal patterns in training stage. Then, we introduced a multiclass-based framework for P300 detection.Proposed approach is evaluated with three public datasets, BCI competition II, BCI competition III and the BNCI Horizon. In all three datasets, proposed multi-class approach outperformed common binary-classification-based approach in the same conditions. In addition, multiclass-based approach reached the highest accuracy (100%) in the 3th sequence for BCI competition II, for BCI competition III achieved an average accuracy of 74% and 98% in 5th and 15th sequences and, for the BNCI Horizon dataset achieved an average accuracy of 77.50% and 97.49% in 5th and 10th sequences, respectively. That means our proposed approach, regardless of its simplicity, achieved state-of-the-art character recognition performance in the existing methods. The results confirmed that binary-classification based methods is not appropriate for P300 detection in BCI spellers.

Compact temporal dilated convolution with Channel-wise attention and cost sensitive learning for Single trial P300 detection

The P300 speller is a challenging task due to various factors, such as morphological and temporal variabilities, noisy channels, imbalanced P300/non-P300 data, numerous trainable parameters, and lengthy spelling cycles. To overcome these issues, we propose a novel solution that incorporates temporal dilated convolution with channel-wise attention into a lightweight base classifier and employs cost-sensitive learning to handle data imbalance. We evaluate our system through extensive subject-dependent and cross-subject experiments on two standard datasets: Amyotrophic Lateral Sclerosis (ALS) and Devanagari Script (DS).Our results demonstrate a significant improvement in P300 classification with a 2–3 times reduction in trainable parameters compared to single-trial experiments. Our proposed model outperforms state-of-the-art compact models and offers better trade-off between computational complexity and classification performance.Our approach offers new insights into the challenges of P300 classification and provides inspiration for future research in this field. We are confident that our proposed model will contribute to the advancement of P300 classification and benefit the scientific community.

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 hybrid P300-SSVEP brain-computer interface speller with a frequency enhanced row and column paradigm.

This study proposes a new hybrid brain-computer interface (BCI) system to improve spelling accuracy and speed by stimulating P300 and steady-state visually evoked potential (SSVEP) in electroencephalography (EEG) signals. A frequency enhanced row and column (FERC) paradigm is proposed to incorporate the frequency coding into the row and column (RC) paradigm so that the P300 and SSVEP signals can be evoked simultaneously. A flicker (white-black) with a specific frequency from 6.0 to 11.5 Hz with an interval of 0.5 Hz is assigned to one row or column of a 6 × 6 layout, and the row/column flashes are carried out in a pseudorandom sequence. A wavelet and support vector machine (SVM) combination is adopted for P300 detection, an ensemble task-related component analysis (TRCA) method is used for SSVEP detection, and the two detection possibilities are fused using a weight control approach. The implemented BCI speller achieved an accuracy of 94.29% and an information transfer rate (ITR) of 28.64 bit/min averaged across 10 subjects during the online tests. An accuracy of 96.86% is obtained during the offline calibration tests, higher than that of only using P300 (75.29%) or SSVEP (89.13%). The SVM in P300 outperformed the previous linear discrimination classifier and its variants (61.90-72.22%), and the ensemble TRCA in SSVEP outperformed the canonical correlation analysis method (73.33%). The proposed hybrid FERC stimulus paradigm can improve the performance of the speller compared with the classical single stimulus paradigm. The implemented speller can achieve comparable accuracy and ITR to its state-of-the-art counterparts with advanced detection algorithms.

A new benchmark dataset for P300 ERP-based BCI applications

Because of its non-invasive nature, one of the most commonly used event-related potentials in brain-computer interface (BCI) system designs is the P300 electroencephalogram (EEG) signal. The fact that the P300 response can easily be stimulated and measured is particularly important for participants with severe motor disabilities. In order to train and test P300-based BCI speller systems in more realistic high-speed settings, there is a pressing need for a large and challenging benchmark dataset. Various datasets already exist in the literature but most of them are not publicly available, and they either have a limited number of participants or utilize relatively long stimulus duration (SD) and inter-stimulus intervals (ISI). They are also typically based on a 36 target (6×6) character matrix. The use of long ISI, in particular, not only reduces the speed and the information transfer rates (ITRs) but also oversimplifies the P300 detection. This leaves a limited challenge to state-of-the-art machine learning and signal processing algorithms. In fact, near-perfect P300 classification accuracies are reported with the existing datasets. Therefore, one certainly needs a large-scale dataset with challenging settings to fully exploit the recent advancements in algorithm design (machine learning and signal processing) and achieve high-performance speller results. To this end, in this article we introduce a new freely- and publicly-accessible P300 dataset obtained using 32-channel EEG, in the hope that it will lead to new research findings and eventually more efficient BCI designs. The introduced dataset comprises 18 participants performing a 40-target (5×8) cued-spelling task, with reduced SD (66.6 ms) and ISI (33.3 ms) for fast spelling. We have also processed, analyzed, and character-classified the introduced dataset and we presented the accuracy and ITR results as a benchmark. The introduced dataset and the codes of our experiments are publicly accessible at https://data.mendeley.com/datasets/vyczny2r4w.

Bayesian Uncertainty Modeling for P300-Based Brain-Computer Interface.

P300 potential is important to cognitive neuroscience research, and has also been widely applied in brain-computer interfaces (BCIs). To detect P300, many neural network models, including convolutional neural networks (CNNs), have achieved outstanding results. However, EEG signals are usually high-dimensional. Moreover, since collecting EEG signals is time-consuming and expensive, EEG datasets are typically small. Therefore, data-sparse regions usually exist within EEG dataset. However, most existing models compute predictions based on point-estimate. They cannot evaluate prediction uncertainty and tend to make overconfident decisions on samples located in data-sparse regions. Hence, their predictions are unreliable. To solve this problem, we propose a Bayesian convolutional neural network (BCNN) for P300 detection. The network places probability distributions over weights to capture model uncertainty. In prediction phase, a set of neural networks can be obtained by Monte Carlo sampling. Integrating the predictions of these networks implies ensembling. Therefore, the reliability of prediction can be improved. Experimental results demonstrate that BCNN can achieve better P300 detection performance than point-estimate networks. In addition, placing a prior distribution over the weight acts as a regularization technique. Experimental results show that it improves the robustness of BCNN to overfitting on small dataset. More importantly, with BCNN, both weight uncertainty and prediction uncertainty can be obtained. The weight uncertainty is then used to optimize the network through pruning, and the prediction uncertainty is applied to reject unreliable decisions so as to reduce detection error. Therefore, uncertainty modeling provides important information to further improve BCI systems.

ST-CapsNet: Linking Spatial and Temporal Attention with Capsule Network for P300 Detection Improvement.

A brain-computer interface (BCI), which provides an advanced direct human-machine interaction, has gained substantial research interest in the last decade for its great potential in various applications including rehabilitation and communication. Among them, the P300-based BCI speller is a typical application that is capable of identifying the expected stimulated characters. However, the applicability of the P300 speller is hampered for the low recognition rate partially attributed to the complex spatio-temporal characteristics of the EEG signals. Here, we developed a deep-learning analysis framework named ST-CapsNet to overcome the challenges regarding better P300 detection using a capsule network with both spatial and temporal attention modules. Specifically, we first employed spatial and temporal attention modules to obtain refined EEG signals by capturing event-related information. Then the obtained signals were fed into the capsule network for discriminative feature extraction and P300 detection. In order to quantitatively assess the performance of the proposed ST-CapsNet, two publicly-available datasets (i.e., Dataset IIb of BCI Competition 2003 and Dataset II of BCI Competition III) were applied. A new metric of averaged symbols under repetitions (ASUR) was adopted to evaluate the cumulative effect of symbol recognition under different repetitions. In comparison with several widely-used methods (i.e., LDA, ERP-CapsNet, CNN, MCNN, SWFP, and MsCNN-TL-ESVM), the proposed ST-CapsNet framework significantly outperformed the state-of-the-art methods in terms of ASUR. More interestingly, the absolute values of the spatial filters learned by ST-CapsNet are higher in the parietal lobe and occipital region, which is consistent with the generation mechanism of P300.

How Visual Stimuli Evoked P300 is Transforming the Brain-Computer Interface Landscape: A PRISMA Compliant Systematic Review.

Non-invasive Visual Stimuli evoked-EEG-based P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics, and education. The current article systematically reviews 147 articles that were published between 2006-2021*. Articles that pass the pre-defined criteria are included in the study. Further, classification based on their primary focus, including article orientation, participants' age groups, tasks given, databases, the EEG devices used in the studies, classification models, and application domain, is performed. The application-based classification considers a vast horizon, including medical assessment, assistance, diagnosis, applications, robotics, entertainment, etc. The analysis highlights an increasing potential for P300 detection using visual stimuli as a prominent and legitimate research area and demonstrates a significant growth in the research interest in the field of BCI spellers utilizing P300. This expansion was largely driven by the spread of wireless EEG devices, advances in computational intelligence methods, machine learning, neural networks and deep learning.

An efficient deep learning framework for P300 evoked related potential detection in EEG signal

BackgroundIncorporating the time-frequency localization properties of Gabor transform (GT), the complexity understandings of convolutional neural network (CNN), and histogram of oriented gradients (HOG) efficacy in distinguishing positive peaks can exhibit their characteristics to reveal an effective solution in the detection of P300 evoked related potential (ERP). By applying a drastic number of convolutional layers, the majority of deep networks elicit sufficient properties for the output determination, leading to gigantic and time-consuming structures. In this paper, we propose a novel deep learning framework by the combination of tuned GT, and modified HOG with the CNN as “TGT-MHOG-CNN” for detection of P300 ERP in EEG signal. MethodIn the proposed method, GT is tuned based on triangular function for EEG signals, and then spectrograms including time-frequency information are captured. The function's parameters are justified to differentiate the signals with the P300 component. Furthermore, HOG is modified (MHOG) for the 2-D EEG signal, and consequently, gradients patterns are extracted for the target potentials. MHOG is potent in distinguishing the positive peak in the general waveform; however, GT unravels time-frequency information, which is ignored in the gradient histogram. These outputs of GT and MHOG do not share the same nature in the images nor overlap. Therefore, more extensive information is reached without redundancy or excessive information by fusing them. Combining GT and MHOG provides different patterns which benefit CNN for more precise detection. Consequently, TGT-MHOG-CNN ends in a more straightforward structure than other networks, and therefore, the whole performance is acceptable with faster rates and very high accuracy. ResultsBCI Competition II and III datasets are used to evaluate the performance of the proposed method. These datasets include a complete record for P300 ERP with BCI2000 using a paradigm, and it has numerous noises, including power and muscle-based noises. The objective is to predict the correct character in each provided character selection epochs. Compared to state-of-the-art methods, simulation results indicate striking abilities of the proposed framework for P300 ERP detection. Our best record reached the P300 ERP classification rates of over 98.7% accuracy and 98.7% precision for BCI Competition II and 99% accuracy and 100% precision for BCI Competition III datasets, with superiority in execution time for the mentioned datasets.

Event-related brain potential markers of visual and auditory perception: A useful tool for brain computer interface systems.

A majority of BCI systems, enabling communication with patients with locked-in syndrome, are based on electroencephalogram (EEG) frequency analysis (e.g., linked to motor imagery) or P300 detection. Only recently, the use of event-related brain potentials (ERPs) has received much attention, especially for face or music recognition, but neuro-engineering research into this new approach has not been carried out yet. The aim of this study was to provide a variety of reliable ERP markers of visual and auditory perception for the development of new and more complex mind-reading systems for reconstructing the mental content from brain activity. A total of 30 participants were shown 280 color pictures (adult, infant, and animal faces; human bodies; written words; checkerboards; and objects) and 120 auditory files (speech, music, and affective vocalizations). This paradigm did not involve target selection to avoid artifactual waves linked to decision-making and response preparation (e.g., P300 and motor potentials), masking the neural signature of semantic representation. Overall, 12,000 ERP waveforms × 126 electrode channels (1 million 512,000 ERP waveforms) were processed and artifact-rejected. Clear and distinct category-dependent markers of perceptual and cognitive processing were identified through statistical analyses, some of which were novel to the literature. Results are discussed from the view of current knowledge of ERP functional properties and with respect to machine learning classification methods previously applied to similar data. The data showed a high level of accuracy (p ≤ 0.01) in the discriminating the perceptual categories eliciting the various electrical potentials by statistical analyses. Therefore, the ERP markers identified in this study could be significant tools for optimizing BCI systems [pattern recognition or artificial intelligence (AI) algorithms] applied to EEG/ERP signals.

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.

A mink (Neovison vison) model of self-injury: Effects of CBP-CREB axis on neuronal damage and behavior.

Self-injurious behavior (SIB) is a clinically challenging problem in the general population and several clinical disorders. However, the precise molecular mechanism of SIB is still not clear. In this paper, the systematic investigation of the genesis and development of SIB is conducted based on behavioral and pathophysiology studies in mink (Neovison vison) models. The night-vision video was used to observe the mink behavior, and the duration was a month. HE stain was performed to characterize the pathology change in the brain of a mink. IHC assay was performed to conduct the protein level detection of Iba-1, p-CREB, CBP, and p300 in the brain tissues. Elisa assay was used to examine the levels of NfL and NfH in serum and CSF of mink. The qRT-PCR assay was used to detect the expression of Bcl-2, NOR1, FoxO4, c-FOS, CBP, and p300 in brain tissues. Western blot was used to detect the protein levels of p-CREB, CBP, and p300 in brain tissues. We also used Evans Blue as a tracer to detect whether the blood-brain barrier was impaired in the brain of mink. The behavioral test, histopathological and molecular biology experiments were combined in this paper, and the results showed that CBP was related to SIB. Mechanism analysis showed that the dysregulation of CBP in brain-activated CREB signaling will result in nerve damage of the brain and SIB symptoms in minks. More importantly, the CBP-CREB interaction inhibitor might help relieve SIB and nerve damage in brain tissues. Our results illustrate that the induction of CBP and the activation of CREB are novel mechanisms in the genesis of SIB. This finding indicates that the CBP-CREB axis is critical for SIB and demonstrates the efficacy of the CBP-CREB interaction inhibitor in treating these behaviors.

An optimized deep learning approach based on autoencoder network for P300 detection in brain computer interface systems

Background. Brain computer interface (BCI) systems by extracting knowledge from brain signals provide a connection channel to the outside world for disabled people, without physiological interfaces. Event-related potentials (ERPs) are a specific type of electroencephalography signals and P300 is one of the most important ERP components. The critical part of P300-based BCI systems is classification step. In this research, an approach is proposed for P300 classification based on novel machine learning methods using convolutional neural networks (CNN) and autoencoder networks. Methods. In the pre-processing step, channel selection, data augmentation (by ADASYN method), filtering and base-line drift were done. Then, in the classification step, four different CNN classifiers including CNN1D, CNN2D, CNN1D_Autoencoder, and CNN2D-Autoencoder were used for P300 classification. Results. After implementation and tuning the networks, 92% as a best accuracy was achieved by CNN2D_Autoencoder. This result was achieved with a considerable tradeoff between complexity and stability. Conclusion. The acquired results emphasize the ability of the deep learning methods in P300 classification and approve the advantage of using them in BCI systems. Furthermore, autoencoder versions of CNN networks are more stable and have a faster convergence. Meanwhile, ADASYN is a suitable method for augmentation of P300 data and even ERPs by sustaining the premier feature space without copying data. Practical Implications. Our results can increase the accuracy of P300 detection and simultaneously reduce the volume of data using the proposed model. Consequently, they can improve character recognition in P300-speller systems generally used by amyotrophic lateral sclerosis (ALS) patients.