Characteristics Of Event-related Potentials Research Articles

The effects of deep TMS on purpose in life, quantitative EEG and event-related potentials in major depressive disorder

Background: Perceived purpose in life (PIL) is linked with many vital health outcomes, including Major Depressive Disorder (MDD). Methods: In this study, biomarkers associated with depression and PIL were investigated using Brain Network Activation (BNA) based quantitative electroencephalography (QEEG) and event-related potential (ERP) measures in a sample of individuals with MDD. Data were analyzed before and after a 36-session, Deep transcranial magnetic stimulation (TMS) program. Results: At baseline, the study observed that increased slow-frequency resting-state activity in the frontal and temporal regions correlated with higher levels of depression and reduced PIL. Additionally, a reduced P3b amplitude was found to predict elevated depressive symptoms. However, with the application of Deep TMS treatment notable improvements were observed in both depression (p < .001. d = 2.15) and PIL (p < .001, d = 1.59) levels. The treatment also successfully regulated resting-state QEEG (delta/beta) and ERP characteristics (P200 latency), bringing them closer to healthy levels. Conclusions: This attenuation of brain activity patterns relating to depression is encouraging as it suggests that effects were robust and are more likely to be sustained over time. This study represents the first exploration of the effects of Deep TMS on PIL and relevant QEEG and ERP biomarkers. The initial evidence suggests that Deep TMS holds promise in enhancing both PIL and neurophysiological health. Future investigations should continue exploring the utility of Deep TMS in targeting a wide range of neuropsychological and physical health conditions, leveraging objective biomarkers such as QEEG and ERP.

Maternal oral probiotic use is associated with decreased breastmilk inflammatory markers, infant fecal microbiome variation, and altered recognition memory responses in infants-a pilot observational study.

Early life gut microbiomes are important for brain and immune system development in animal models. Probiotic use has been proposed as a strategy to promote health via modulation of microbiomes. In this observational study, we explore if early life exposure to probiotics via the mother during pregnancy and lactation, is associated with decreased inflammation in breastmilk, maternal and infant microbiome variation, and altered infant neurodevelopmental features. Exclusively breastfeeding mother-infant dyads were recruited as part of the "Mothers and Infants Linked for Healthy Growth (MILk) Study." Probiotic comparison groups were defined by exposure to maternal probiotics (NO/YES) and by timing of probiotic exposure (prenatal, postnatal, total). C-reactive protein (CRP) and IL-6 levels were determined in breastmilk by immunoassays, and microbiomes were characterized from 1-month milk and from 1- and 6-month infant feces by 16S rDNA sequencing. Infant brain function was profiled via electroencephalogram (EEG); we assessed recognition memory using event-related potential (ERP) responses to familiar and novel auditory (1 month) and visual (6 months) stimuli. Statistical comparisons of study outcomes between probiotic groups were performed using permutational analysis of variance (PERMANOVA) (microbiome) and linear models (all other study outcomes), including relevant covariables as indicated. We observed associations between probiotic exposure and lower breastmilk CRP and IL-6 levels, and infant gut microbiome variation at 1- and 6-months of age (including higher abundances of Bifidobacteria and Lactobacillus). In addition, maternal probiotic exposure was associated with differences in infant ERP features at 6-months of age. Specifically, infants who were exposed to postnatal maternal probiotics (between the 1- and 6-month study visits) via breastfeeding/breastmilk, had larger differential responses between familiar and novel visual stimuli with respect to the late slow wave component of the EEG, which may indicate greater memory updating potential. The milk of mothers of this subgroup of infants had lower IL-6 levels and infants had different 6-month fecal microbiomes as compared to those in the "NO" maternal probiotics group. These results support continued research into "Microbiota-Gut-Brain" connections during early life and the role of pre- and postnatal probiotics in mothers to promote healthy microbiome-associated outcomes in infants.

Social anxiety prediction based on ERP features: A deep learning approach

BackgroundSocial Anxiety Disorder is traditionally diagnosed using subjective scales that may lack accuracy. Recently, EEG technology has gained importance for anxiety detection due to its ability to capture stable and objective neurophysiological activities. However, existing methods mainly focus on extracting EEG features during resting states, with limited use of psychologically features like Event-Related Potential (ERP) in task-related states for anxiety detection in deep learning frameworks. MethodsWe collected EEG data from 63 participants exposed to four facial expressions and extracted task-relevant features. Using the EEGNet model, we predicted social anxiety and evaluated its performance using metrics such as accuracy, F1 score, sensitivity, and specificity. We compared EEGNet's performance with Deep Convolutional Neural Network (DeepConvNet), ShallowConvNet, Bi-directional Long Short-Term Memory (BiLSTM), and SVM. To assess the generalizability of the results, we carried out the same procedure on our prior dataset. ResultsEEGNet outperformed other models, achieving 99.16 % accuracy with Late Positive Potential (LPP). ERP components yielded higher accuracy than time-domain and frequency-domain features for social anxiety recognition. Accuracy was better for neutral and negative facial stimuli. Consistency across two datasets indicates stability of findings. LimitationsDue to limited publicly available task-state datasets, only our own were used. Future studies could assess generalizability on larger datasets from different sources. ConclusionsWe conducted the first test of ERP features in anxiety recognition tasks. Results show ERP features have greater potential in social anxiety recognition, with LPP exhibiting high stability and accuracy. Outcomes indicate recognizing social anxiety with negative or neutral facial stimuli is more useful.

Characterizing Autism Spectrum Disorder through Fusion of Local Cortical Activation and Global Functional Connectivity using Game-Based Stimuli and a Mobile EEG System.

The deficit in social interaction skills among individuals with autism spectrum disorder (ASD) is strongly influenced by personal experiences and social environments. Neuroimaging studies have previously highlighted the link between social impairment and brain activity in ASD. This study aims to develop a method for assessing and identifying ASD using a social cognitive game-based paradigm combined with electroencephalography (EEG) signaling features. Typically developing (TD) participants and autistic preadolescents and teenagers were recruited to participate in a social game while 12-channel EEG signals were recorded. The EEG signals underwent preprocessing to analyze local brain activities, including event-related potentials (ERPs) and time-frequency features. Additionally, the global brain network's functional connectivity between brain regions was evaluated using phase-lag indices (PLIs). Subsequently, machine learning models were employed to assess the neurophysiological features. Results indicated pronounced ERP components, particularly the late positive potential (LPP), in parietal regions during social training. Autistic preadolescents and teenagers exhibited lower LPP amplitudes and larger P200 amplitudes compared to TD participants. Reduced theta synchronization was also observed in the ASD group. Aberrant functional connectivity within certain time intervals was noted in the ASD group. Machine learning analysis revealed that support-vector machines achieved a sensitivity of 100%, specificity of 91.7%, and accuracy of 95.8% as part of the performance evaluation when utilizing ERP and brain oscillation features for ASD characterization. These findings suggest that social interaction difficulties in autism are linked to specific brain activation patterns. Traditional behavioral assessments face challenges of subjectivity and accuracy, indicating the potential use of social training interfaces and EEG features for cognitive assessment in ASD.

RSVP-based BCI for inconspicuous targets: detection, localization, and modulation of attention

Objective.While brain-computer interface (BCI) based on rapid serial visual presentation (RSVP) is widely used in target detection, patterns of event-related potential (ERP), as well as the performance on detecting inconspicuous targets remain unknown. Moreover, participant-screening methods to excluded 'BCI-blind' users are still lacking.Approach.A RSVP paradigm was designed with targets of varied concealment, size, and location. ERPs (e.g. P300 and N2pc) and target detection accuracy were compared among these conditions. The relationship between participants' attention scores and target detection accuracy was also analyzed to test attention level as a criterion for participant screening.Main results.Statistical analysis showed that the conditions of target concealment and size significantly influenced ERP. In particular, ERP for inconspicuous targets, such as concealed and small targets, exhibited lower amplitudes and longer latencies. In consistent, the accuracy of detection in inconspicuous condition was significantly lower than that of conspicuous condition. In addition, a significant association was found between attention scores and target detection accuracy for camouflaged targets.Significance.The study was the first to address ERP features among multiple dimensions of concealment, size, and location. The conclusion provided insights into the relationship between ERP decoding and properties of targets. In addition, the association between attention scores and detection accuracy implied a promising method in screening well-behaved participants for camouflaged target detection.

Bayesian estimation of group event-related potential components (BEGEP): testing a model for synthetic and real datasets

Objective. The spatial resolution of event-related potentials (ERPs) recorded on the head surface is quite low, since the sensors located on the scalp register mixtures of signals from several cortical sources. Bayesian models for multi-channel ERPs obtained from a group of subjects under multiple task conditions can aid in recovering signals from these sources. Approach. This study introduces a novel model that captures several important characteristics of ERP, including person-to-person variability in the magnitude and latency of source signals. Furthermore, the model takes into account that ERP noise, the main source of which is the background electroencephalogram, has the following properties: it is spatially correlated, spatially heterogeneous, and varies over time and from person to person. Bayesian inference algorithms have been developed to estimate the parameters of this model, and their performance has been evaluated through extensive experiments using synthetic data and real ERPs records in a large number of subjects (N = 351). Main results. The signal estimates obtained using these algorithms were compared with the results of the analysis of ERPs by conventional methods. This comparison showed that the use of this model is suitable for the analysis of ERPs and helps to reveal some features of source signals that are difficult to observe in their mixture signals recorded on the scalp. Significance. This study shown that the proposed method is a potentially useful tool for analyzing ERPs collected from groups of subjects in various cognitive neuroscience experiments.

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.

Transdiagnostic psychopathology in the light of robust single-trial event-related potentials.

Recent evidence indicates that event-related potentials (ERPs) as measured on the electroencephalogram (EEG) are more closely related to transdiagnostic, dimensional measures of psychopathology (TDP) than to diagnostic categories. A comprehensive examination of correlations between well-studied ERPs and measures of TDP is called for. In this study, we recruited 50 patients with emotional disorders undergoing 14 weeks of transdiagnostic group psychotherapy as well as 37 healthy comparison subjects (HC) matched in age and sex. HCs were assessed once and patients three times throughout treatment (N = 172 data sets) with a battery of well-studied ERPs and psychopathology measures consistent with the TDP framework The Hierarchical Taxonomy of Psychopathology (HiTOP). ERPs were quantified using robust single-trial analysis (RSTA) methods and TDP correlations with linear regression models as implemented in the EEGLAB toolbox LIMO EEG. We found correlations at several levels of the HiTOP hierarchy. Among these, a reduced P3b was associated with the general p-factor. A reduced error-related negativity correlated strongly with worse symptomatology across the Internalizing spectrum. Increases in the correct-related negativity correlated with symptoms loading unto the Distress subfactor in the HiTOP. The Flanker N2 was related to specific symptoms of Intrusive Cognitions and Traumatic Re-experiencing and the mismatch negativity to maladaptive personality traits at the lowest levels of the HiTOP hierarchy. Our study highlights the advantages of RSTA methods and of using validated TDP constructs within a consistent framework. Future studies could utilize machine learning methods to predict TDP from a set of ERP features at the subject level.

Auditory event-related potential differentiates girls with Rett syndrome from their typically-developing peers with high accuracy: Machine learning study

Rett Syndrome (RTT) is a rare neurodevelopmental disorder caused by mutation in the MECP2 gene. No cures are still available, but several clinical trials are ongoing. Here we examine neurophysiological correlates of auditory processing for ability to differentiate patients with RTT from typically developing (TD) peers applying standard machine learning (ML) methods and pipelines. Capitalized on the available event-related potential (ERP) data recorded in response to tone presented at different rates (stimulus onset asynchrony 900, 1800 and 3600 ms) from 24 patients with RTT and 27 their TD peer. We considered the most common ML models that are widely used for classification tasks. These include both linear models (logistic regression, support-vector machine with linear kernel) and tree-based nonlinear models (random forest, gradient boosting). Based on these methods we were able to differentiate RTT from TD children with high accuracy (with up to 0.94 ROC-AUC score), which was evidently higher at the fastest presentation rate. Importance analysis and perturbation importance pointed out that the most important feature for classification is P2-N2 peak-to-peak amplitude, consistently across the approaches and blocks with different presentation rate. The results suggest the unique pattern of ERP characteristics for RTT and points to features of importance. The results might be relevant for establishing outcome measures for clinical trials.

Personalized motor imagery prediction model based on individual difference of ERP

Objective. Motor imagery-based brain–computer interaction (MI-BCI) is a novel method of achieving human and external environment interaction that can assist individuals with motor disorders to rehabilitate. However, individual differences limit the utility of the MI-BCI. In this study, a personalized MI prediction model based on the individual difference of event-related potential (ERP) is proposed to solve the MI individual difference. Approach. A novel paradigm named action observation-based multi-delayed matching posture task evokes ERP during a delayed matching posture task phase by retrieving picture stimuli and videos, and generates MI electroencephalogram through action observation and autonomous imagery in an action observation-based motor imagery phase. Based on the correlation between the ERP and MI, a logistic regression-based personalized MI prediction model is built to predict each individual’s suitable MI action. 32 subjects conducted the MI task with or without the help of the prediction model to select the MI action. Then classification accuracy of the MI task is used to evaluate the proposed model and three traditional MI methods. Main results. The personalized MI prediction model successfully predicts suitable action among 3 sets of daily actions. Under suitable MI action, the individual’s ERP amplitude and event-related desynchronization (ERD) intensity are the largest, which helps to improve the accuracy by 14.25%. Significance. The personalized MI prediction model that uses the temporal ERP features to predict the classification accuracy of MI is feasible for improving the individual’s MI-BCI performance, providing a new personalized solution for the individual difference and practical BCI application.

Automatic sensory change processing in adults with attention deficit and hyperactivity disorder: a visual mismatch negativity study.

In addition to higher-order executive functions, underlying sensory processing ability is also thought to play an important role in Attention-Deficit/Hyperactivity Disorder (AD/HD). An event-related potential feature, the mismatch negativity, reflects the ability of automatic sensory change processing and may be correlated with AD/HD symptoms and executive functions. This study aims to investigate the characteristics of visual mismatch negativity (vMMN) in adults with AD/HD. Twenty eight adults with AD/HD and 31 healthy controls were included in this study. These two groups were matched in age, IQ and sex. In addition, both groups completed psychiatric evaluations, a visual ERP task used to elicit vMMN, and psychological measures about AD/HD symptoms and day-to-day executive functions. Compared to trols, the late vMMN (230-330 ms) was significantly reduced in the AD/HD group. Correlation analyses showed that late vMMN was correlated with executive functions but not AD/HD symptoms. However, further mediation analyses showed that different executive functions had mediated the relationships between late vMMN and AD/HD symptoms. Our findings indicate that the late vMMN, reflecting automatic sensory change processing ability, was impaired in adults with AD/HD. This impairment could have negative impact on AD/HD symptoms via affecting day-to-day executive functions.

Features of event-related potentials during retrieval of episodic memory in patients with mild cognitive impairment due to Alzheimer's disease.

To provide a rigorous comparison between patients with mild cognitive impairment due to Alzheimer's disease (MCI-AD) and healthy elderly, as well as to assess the value of electroencephalography (EEG) in terms of early diagnosis, we conducted a neutral image recognition memory task involving individuals with positive biomarkers including β amyloid deposition, pathologic tau or neurodegeneration. The task involving study and test blocks was designed to evaluate participants' recognition memory. Electroencephalogram was recorded synchronously to elicit event-related potentials in patients with MCI-AD and healthy control subjects. We further analyzed differences between groups or conditions in terms of behavioral performance, time domain, and time-frequency domain. The MCI-AD cohort showed a slower response time to old/new images and had low accuracy regarding behavioral performance. The amplitude of the late positive complex for the old/new effects was significantly suppressed in the MCI-AD cohort when compared with that in the HC cohort. The amplitude of the late old/new effects was correlated with the Auditory Verbal Learning Test recognition score in all participants. The time-frequency domain analysis revealed that correct recognition of old items elicited a decrease in beta power, mainly limited to the HC cohort. Moreover, the combination of behavioral (processing speed and accuracy) and electrophysiological (average amplitude and relative power of delta band) measures contributes to classifying patients with MCI-AD from healthy elderly people. Changes of old/new effects, accuracy and response time are sensitive to the impairment of recognition memory in patients with MCI-AD and have moderate value in predicting the incipient stage of AD.

LDER: a classification framework based on ERP enhancement in RSVP task

Objective. Rapid serial visual presentation (RSVP) based on electroencephalography (EEG) has been widely used in the target detection field, which distinguishes target and non-target by detecting event-related potential (ERP) components. However, the classification performance of the RSVP task is limited by the variability of ERP components, which is a great challenge in developing RSVP for real-life applications. Approach. To tackle this issue, a classification framework based on the ERP feature enhancement to offset the negative impact of the variability of ERP components for RSVP task classification named latency detection and EEG reconstruction was proposed in this paper. First, a spatial-temporal similarity measurement approach was proposed for latency detection. Subsequently, we constructed a single-trial EEG signal model containing ERP latency information. Then, according to the latency information detected in the first step, the model can be solved to obtain the corrected ERP signal and realize the enhancement of ERP features. Finally, the EEG signal after ERP enhancement can be processed by most of the existing feature extraction and classification methods of the RSVP task in this framework. Main results. Nine subjects were recruited to participate in the RSVP experiment on vehicle detection. Four popular algorithms (spatially weighted Fisher linear discrimination-principal component analysis (PCA), hierarchical discriminant PCA, hierarchical discriminant component analysis, and spatial-temporal hybrid common spatial pattern-PCA) in RSVP-based brain–computer interface for feature extraction were selected to verify the performance of our proposed framework. Experimental results showed that our proposed framework significantly outperforms the conventional classification framework in terms of area under curve, balanced accuracy, true positive rate, and false positive rate in four feature extraction methods. Additionally, statistical results showed that our proposed framework enables better performance with fewer training samples, channel numbers, and shorter temporal window sizes. Significance. As a result, the classification performance of the RSVP task was significantly improved by using our proposed framework. Our proposed classification framework will significantly promote the practical application of the RSVP task.

Real‐time Neurofeedback Rewards Memory‐related Potentials in Older Brains

AbstractBackgroundElectroencephalogram (EEG)‐based neurofeedback (NF) allows reward of desired brain activity in milliseconds. Compared to frequency power‐based approach during resting state EEG, event‐related potentials (ERPs) based NF during a task is technically challenging because of the cost of computation over a time window. We have previously established that left‐frontal memory‐related potentials discriminate normal cognitive aging from age‐ and education‐ matched older adults with risk of mild cognitive impairment (MCI). To modulate brainwaves during working memory retrieval towards healthier patterns, we develop an ERP‐based neurofeedback protocol to reward better memory‐related potentials.MethodThe training protocol is developed based on a modified delayed match‐to‐sample task also known as Bluegrass working memory paradigm. During each memory trial, a participant holds the sample target image in his/her working memory and responds whether each subsequent image is a Match or Nonmatch to the memory target by pressing the button. Real‐time classification of extracted ERP features is analyzed after stimulus onset, the dynamic machine‐learning program then modifies the stimulus by turning it into vivid color (reward) or leaving it with black‐and‐white (unrewarded). The processing pipeline has been developed using ERPs of older adults with and without MCI. The left frontal sites (F3 and F7) were preprocessed by re‐referencing and filtering with a 0.05 – 40 Hz band‐pass filter. For each participant, the EEG of 5 offline base trials in a 300‐600ms time window was used as a baseline. The ERP difference of the baseline versus the subsequent single trial is calculated to determine whether the image will be colored or left at black‐and‐white.ResultOur newly developed ERP‐neurofeedback protocol has succeeded in providing visual feedback based on fluctuation of single trial ERP in real time. The program was also designed to fit in each individual due to varied sensibility to NF. Currently we are to examine the feasibility and effectiveness of the proposed protocol by measuring memory indicators before and after NF training sessions.ConclusionWe have developed a new protocol using ERP‐based neurofeedback to improve personalized memory retrieval. The program is the first step to intervention trials for enhancing successful memory by rewarding desired memory‐related potentials in older brains.

Stylized faces enhance ERP features used for the detection of emotional responses.

For their ease of accessibility and low cost, current Brain-Computer Interfaces (BCI) used to detect subjective emotional and affective states rely largely on electroencephalographic (EEG) signals. Public datasets are available for researchers to design models for affect detection from EEG. However, few designs focus on optimally exploiting the nature of the stimulus elicitation to improve accuracy. The RSVP protocol is used in this experiment to present human faces of emotion to 28 participants while EEG was measured. We found that artificially enhanced human faces with exaggerated, cartoonish visual features significantly improve some commonly used neural correlates of emotion as measured by event-related potentials (ERPs). These images elicit an enhanced N170 component, well known to relate to the facial visual encoding process. Our findings suggest that the study of emotion elicitation could exploit consistent, high detail, AI generated stimuli transformations to study the characteristics of electrical brain activity related to visual affective stimuli. Furthermore, this specific result might be useful in the context of affective BCI design, where a higher accuracy in affect decoding from EEG can improve the experience of a user.

Hybrid Genetic Algorithm for Clustering IC Topographies of EEGs

Clustering of independent component (IC) topographies of Electroencephalograms (EEG) is an effective way to find brain-generated IC processes associated with a population of interest, particularly for those cases where event-related potential features are not available. This paper proposes a novel algorithm for the clustering of these IC topographies and compares its results with the most currently used clustering algorithms. In this study, 32-electrode EEG signals were recorded at a sampling rate of 500 Hz for 48 participants. EEG signals were pre-processed and IC topographies computed using the AMICA algorithm. The algorithm implements a hybrid approach where genetic algorithms are used to compute more accurate versions of the centroids and the final clusters after a pre-clustering phase based on spectral clustering. The algorithm automatically selects the optimum number of clusters by using a fitness function that involves local-density along with compactness and separation criteria. Specific internal validation metrics adapted to the use of the absolute correlation coefficient as the similarity measure are defined for the benchmarking process. Assessed results across different ICA decompositions and groups of subjects show that the proposed clustering algorithm significantly outperforms the (baseline) clustering algorithms provided by the software EEGLAB, including CORRMAP.

Improving Intention Detection in Single-Trial Classification Through Fusion of EEG and Eye-Tracker Data

Intention decoding is an indispensable procedure in hands-free human–computer interaction (HCI). A conventional eye-tracker system using a single-model fixation duration may issue commands that ignore users' real expectations. Here, an eye-brain hybrid brain–computer interface (BCI) interaction system was introduced for intention detection through the fusion of multimodal eye-tracker and event-related potential (ERP) [a measurement derived from electroencephalography (EEG)] features. Eye-tracking and EEG data were recorded from 64 healthy participants as they performed a 40-min customized free search task of a fixed target icon among 25 icons. The corresponding fixation duration of eye tracking and ERP were extracted. Five previously-validated linear discriminant analysis (LDA)-based classifiers [including regularized LDA, stepwise LDA, Bayesian LDA, shrinkage linear discriminant analysis (SKLDA), and spatial-temporal discriminant analysis] and the widely-used convolutional neural network (CNN) method were adopted to verify the efficacy of feature fusion from both offline and pseudo-online analysis, and the optimal approach was evaluated by modulating the training set and system response duration. Our study demonstrated that the input of multimodal eye tracking and ERP features achieved a superior performance of intention detection in the single-trial classification of active search tasks. Compared with the single-model ERP feature, this new strategy also induced congruent accuracy across classifiers. Moreover, in comparison with other classification methods, we found that SKLDA exhibited a superior performance when fusing features in offline tests (ACC = 0.8783, AUC = 0.9004) and online simulations with various sample amounts and duration lengths. In summary, this study revealed a novel and effective approach for intention classification using an eye-brain hybrid BCI and further supported the real-life application of hands-free HCI in a more precise and stable manner.

On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features.

The utilization of a non-invasive electroencephalogram (EEG) as an input sensor is a common approach in the field of the brain-computer interfaces (BCI). However, the collected EEG data pose many challenges, one of which may be the age-related variability of event-related potentials (ERPs), which are often used as primary EEG BCI signal features. To assess the potential effects of aging, a sample of 27 young and 43 older healthy individuals participated in a visual oddball study, in which they passively viewed frequent stimuli among randomly occurring rare stimuli while being recorded with a 32-channel EEG set. Two types of EEG datasets were created to train the classifiers, one consisting of amplitude and spectral features in time and another with extracted time-independent statistical ERP features. Among the nine classifiers tested, linear classifiers performed best. Furthermore, we show that classification performance differs between dataset types. When temporal features were used, maximum individuals' performance scores were higher, had lower variance, and were less affected overall by within-class differences such as age. Finally, we found that the effect of aging on classification performance depends on the classifier and its internal feature ranking. Accordingly, performance will differ if the model favors features with large within-class differences. With this in mind, care must be taken in feature extraction and selection to find the correct features and consequently avoid potential age-related performance degradation in practice.

Characteristics of Event-related Potentials during a Target Selection Task with Fingerspelling Images

We have been developing a cognitive assessment system based on an electroencephalography (EEG)-based brain-machine interface (BMI) that uses event-related potentials (ERPs) as a virtual “Mind Switch.” ERPs reflect temporal changes in attention and are also known as potential biomarkers for assessing cognitive function. We recorded EEG data from 10 healthy subjects who performed target-selection tasks based upon the visual discrimination of fingerspelling images with or without motor responses (key release). We found differential responses between target and non-target images, which were quantitatively evaluated by the decoding accuracy of the target. We also found that ERPs were enhanced when accompanied by key releases, which might indicate that a more attentive state was involved. These results suggest that fingerspelling images are a feasible tool for cognitive assessment. Future studies should be conducted to demonstrate the applicability of our system in preventing frailty in older people.

Subject-Independent Classification of P300 Event-Related Potentials Using a Small Number of Training Subjects

The intersubject variability present in electroencephalographic (EEG) signals can affect the performance of the brain–computer interface (BCI) systems. Despite the significant progress in the field, the variability in neural data remains one of the most critical challenges in constructing accurate predictive models of human intention. As a result, the majority of the previous studies have focused either on devising subject-specific signal processing and machine learning algorithms, used some data from a target user to update and calibrate a pretrained classifier, or have used data collected from a relatively large number of training subjects to construct generic classifiers for new subjects. In this work, we investigate the feasibility of using a relatively small number of training subjects to achieve subject-independent classification of event-related potentials (ERPs) in P300-based BCIs. To this end, we employ convolutional neural networks (CNNs) and propose a leave-one-subject-out cross-validation (LOSO-CV) for model selection; that is to say, for tuning CNN hyperparameters including number of layers, filters, kernel size, and epoch. The utility of the proposed model selection is warranted because LOSO-CV simulates the effect of subject-independent classification within the training data. The entire process of training (including model selection) is validated by applying another LOSO-CV external to the training process. Our empirical results obtained on four publicly available datasets confirm the capability of LOSO-CV model selection with CNN to capture intrinsic ERP features from a small group of subjects to classify observations collected from unseen subjects.