P300 Paradigm Research Articles

A review on electroencephalogram based channel selection

The electroencephalogram (EEG) signal is the key signal carrier of the brain-computer interface (BCI) system. The EEG data collected by the whole-brain electrode arrangement is conducive to obtaining higher information representation. Personalized electrode layout, while ensuring the accuracy of EEG signal decoding, can also shorten the calibration time of BCI and has become an important research direction. This paper reviews the EEG signal channel selection methods in recent years, conducts a comparative analysis of the combined effects of different channel selection methods and different classification algorithms, obtains the commonly used channel combinations in motor imagery, P300 and other paradigms in BCI, and explains the application scenarios of the channel selection method in different paradigms are discussed, in order to provide stronger support for a more accurate and portable BCI system.

Evaluating the association between steroid hormones and filtering of sensory information in healthy women

Sensori (motor) gating is an integral part of information processing. Stress may affect information processing and is a potent risk factor for the onset and worsening of mental disorders. Sensori (motor) gating is affected by sex hormone rhythms, e.g. changes over the menstrual cycle. Here, we investigated if steroid hormones, i.e. cortisol and ovarian sex hormones, influence sensori (motor) gating. Data from 53 naturally cycling healthy women were analyzed in a cross-sectional design. Associations between prepulse inhibition (PPI) and P50 suppression variables and endogenous estradiol, progesterone, testosterone, total daily cortisol, and the cortisol awakening response were evaluated using multiple linear regression and Spearman's correlation. Total daily cortisol was negatively associated with PPI. Estradiol was positively associated with the acoustic startle response in the PPI paradigm and the P50 amplitude to the conditioning stimulus in the P50 paradigm. Steroid hormone levels were not associated with P50 suppression. In conclusion, estrogen and cortisol levels in healthy, naturally cycling women are associated with key brain measures of basic information processing. This finding may stimulate further studies into the role of stress hormones and estrogen transitions in modulating information filtering, which may be critical for risk and resilience to mental disorders.

Classification of Visually Evoked Potential EEG Using Hybrid Anchoring-based Particle Swarm Optimized Scaled Conjugate Gradient Multi-Layer Perceptron Classifier

Brain-Computer Interface is an emerging field that focuses on transforming brain data into machine commands. EEG-based BCI is widely used due to the non-invasive nature of Electroencephalogram. Classification of EEG signals is one of the primary components in BCI applications. Steady-State Visually Evoked Potential (SSVEP) paradigms have gained importance because of lesser training time, higher precision, and improved information transfer rate compared to P300 and motor imagery paradigms. In this paper, a novel hybrid Anchoring-based Particle Swarm Optimized Scaled Conjugate Gradient Multi-Layer Perceptron classifier (APS-MLP) is proposed to improve the classification accuracy of SSVEP five classes viz. 6.66, 7.5, 8.57, 10 and 12 Hz, signals. Scaled Conjugate Gradient descent anchors the initial position of Particle Swarm Optimization. The best position, Pbest, of each particle initializes an SCG-MLP, the accuracy of APS-MLP is obtained by averaging the accuracies of each SCG-MLP. The proposed method is compared with standard classifiers namely, k-NN, SVM, LDA and MLP. In which, the proposed algorithm achieves improved training and testing accuracies of 88.69% and 95.4% respectively, which is 12–15% higher than the standard EEG-based BCI classifiers. The proposed algorithm is robust, with a Cohen’s kappa coefficient of 0.96, and will be used in applications such as motion control and improving the quality of life for people with disabilities.

Immediate effects and duration of a short and single application of transcutaneous auricular vagus nerve stimulation on P300 event related potential.

Transcutaneous auricular vagus nerve stimulation (taVNS) is a neuromodulatory technique that stimulates the auricular branch of the vagus nerve. The modulation of the locus coeruleus-norepinephrine (LC-NE) network is one of the potential working mechanisms of this method. Our aims were 1-to investigate if short and single applications of taVNS can modulate the P300 cognitive event-related potential (ERP) as an indirect marker that reflects NE brain activation under control of the LC, and 2-to evaluate the duration of these changes. 20 healthy volunteers executed an auditory oddball paradigm to obtain P300 and reaction time (RT) values. Then a 7 min active or sham taVNS period was initiated and simultaneously a new P300 paradigm was performed. We successively repeated the paradigm on 4 occasions with different time intervals up to 56 min after the stimulation onset. During active taVNS an immediate and significant effect of increasing the amplitude and reducing the latency of P300, as well as a shortening in the RT was observed. This effect was prolonged in time up to 28 min. The values then returned to pre-stimulation levels. Sham stimulation did not generate changes. Our results, demonstrate differential facilitating effects in a concrete time window after taVNS. Literature about the modulatory effect of taVNS over P300 ERP shows a wide spread of results. There is not a standardized system for taVNS and currently the great heterogeneity of stimulation approaches concerning targets and parameters, make it difficult to obtain conclusions about this relationship. Our study was designed optimizing several stimulation settings, such as a customized earbud stimulator, enlarged stimulating surface, simultaneous stimulation over the cymba and cavum conchae, a Delayed Biphasic Pulse Burst and current controlled stimulation that adjusted the output voltage and guaranteed the administration of a preset electrical dose. Under our stimulation conditions, targeting vagal nerve fibers via taVNS modulates the P300 in healthy participants. The optimal settings of modulatory function of taVNS on P300, and their interdependency is insufficiently studied in the literature, but our data provides several easily optimizable parameters, that will produce more robust results in future.

Electrophysiological and Neuropsychological Indices of Cognitive Dysfunction in Patients with Chronic Insomnia and Severe Benzodiazepine Use Disorder.

Benzodiazepine (BDZ) misuse is a growing health problem, with 1-2% of patients under BDZ treatment meeting the criteria for use disorder or dependence. Although BDZ addiction potential has been known for decades, much remains unknown its effects on brain functions. The aim of this study was to assess the neuropsychological and neurophysiological profile of a group of chronic insomniacs taking long-term high doses of benzodiazepine. We recruited 17 consecutive patients admitted to our third-level Sleep Medicine Unit for drug discontinuation (7 males, mean age 49.2 ± 11.2 years, mean education 13.7 ± 3.9 years, mean daily diazepam-equivalent BDZ: 238.1 ± 84.5 mg) and 17 gender/age-matched healthy controls (7 males, mean age 46.8 ± 14.1 years, mean education 13.5 ± 4.5 years). We performed a full neuropsychological evaluation of all subjects and recorded their scalp event-related potentials (Mismatch-Passive Oddball-Paradigm and Active Oddball P300 Paradigm). Patients with chronic insomnia and BDZ use disorder showed a profound frontal lobe executive dysfunction with significant impairment in the cognitive flexibility domain, in face of a preserved working, short and long-term memory. In patients, P300 amplitude tended to be smaller, mainly over the frontal regions, compared to controls. BDZ use disorder has a severe cognitive impact on chronic insomnia patients. Long-term high-dose BDZ intake should be carefully evaluated and managed by clinicians in this specific patient population, especially in relation to risky activities.

Using Mental Shadowing Tasks to Improve the Sound-Evoked Potential of EEG in the Design of an Auditory Brain–Computer Interface

This study proposed an auditory stimulation protocol based on Shadowing Tasks to improve the sound-evoked potential in an EEG and the efficiency of an auditory brain–computer interface system. We use stories as auditory stimulation to enhance users’ motivation and presented the sound stimuli via headphones to enable the user to concentrate better on the keywords in the stories. The protocol presents target stimuli with an oddball P300 paradigm. To decline mental workload, we shift the usual Shadowing Tasks paradigm: Instead of loudly repeating the auditory target stimuli, we ask subjects to echo the target stimuli mentally as it occurs. Twenty-four healthy participants, not one of whom underwent a BCI use or training phase before the experimental procedure, ran twenty trials each. We analyzed the effect of the auditory stimulation based on the Shadowing Tasks theory with the performance of the auditory BCI system. We also evaluated the judgment effectiveness of the three ERPs components (N2P3, P300, and N200) from five chosen electrodes. The best average accuracy of post-analysis was 78.96%. Using component N2P3 to distinguish between target and non-target can improve the efficiency of the auditory BCI system and give it good practicality. We intend to persist in this study and involve the protocol in an aBCI-based home care system (HCS) for target patients to provide daily assistance.

Mental Strategies in a P300-BCI: Visuomotor Transformation Is an Option.

Currently, P300-BCIs are mostly used for spelling tasks, where the number of commands is equal to the number of stimuli that evoke event-related potentials (ERPs). Increasing this number slows down the BCI operation because each stimulus has to be presented several times for better classification. Furthermore, P300 spellers typically do not utilize potentially useful imagery-based approaches, such as the motor imagery successfully practiced in motor rehabilitation. Here, we tested a P300-BCI with a motor-imagery component. In this BCI, the number of commands was increased by adding mental strategies instead of increasing the number of targets. Our BCI had only two stimuli and four commands. The subjects either counted target appearances mentally or imagined hand movements toward the targets. In this design, the motor-imagery paradigm enacted a visuomotor transformation known to engage cortical and subcortical networks participating in motor control. The operation of these networks suffers in neurological conditions such as stroke, so we view this BCI as a potential tool for the rehabilitation of patients. As an initial step toward the development of this clinical method, sixteen healthy participants were tested. Consistent with our expectation that mental strategies would result in distinct EEG activities, ERPs were different depending on whether subjects counted stimuli or imagined movements. These differences were especially clear in the late ERP components localized in the frontal and centro-parietal regions. We conclude that (1) the P300 paradigm is suitable for enacting visuomotor transformations and (2) P300-based BCIs with multiple mental strategies could be used in applications where the number of possible outputs needs to be increased while keeping the number of targets constant. As such, our approach adds to both the development of versatile BCIs and clinical approaches to rehabilitation.

P551. Modelling of P300 and P50 Paradigms in the BSNIP Dataset Indicate Loss of NMDAR Function in Psychosis

Modelling of EEG during mismatch negativity, 40 Hz auditory steady state response and rest indicated consistently reduced excitability of pyramidal cells in schizophrenia (Adams et al, 2021, Biol Psych). Two potential explanations are i) hypofunction of pyramidal NMDA receptors, and ii) greater pyramidal inhibition by inhibitory interneurons. We disambiguated these hypotheses using a dynamic causal model (DCM) in which NMDA receptors and interneuronal inhibition are explicitly parameterised, and P300 and P50 EEG paradigms from the BSNIP dataset.

Abstracts Presented at the 2nd Annual Advancing Healthcare Innovation Summit: Nov 11, 2022, Cincinnati, OH

Abstracts Presented at the 2nd Annual Advancing Healthcare Innovation Summit: Nov 11, 2022, Cincinnati, OH

High Intellectual Potential and High Functioning Autism: Clinical and Neurophysiological Features in a Pediatric Sample.

High Intellectual Potential (HIP) and High Functioning Autism (HFA) are two different conditions sharing some clinical and neurobiological features. The aim of the present study was to characterize a sample of HIP children (n: 16; M/F: 14/2; median age: 10 years) in comparison to those with HFA (n: 17; M/F: 16/1; median age: 13 years) and to neurotypically developed (NTD) children (n: 10; M/F: 4/6; median age: 11 years) from a clinical and neurophysiological perspective. Specifically, a standardized clinical assessment of cognitive and adaptive skills, autistic symptoms, executive functions and behavioral features was performed. Moreover, event-related potentials (ERPs) were recorded, referring specifically to the mismatch negativity (MMN) and P300 paradigm. Our data highlighted the presence of similarities between the intellectually gifted individuals and the ones with autism (i.e., a nonhomogeneous intellective profile, an adaptive skills impairment, subthreshold autistic symptoms and increased perfectionism). Interestingly, a distinct neurophysiological characterization between groups came out, with evidence of a reduced MMN amplitude only in the HFA group. Furthermore, no differences within groups in the P300 component emerged. Therefore, our results start to provide a more informative characterization of the HIP phenotype in comparison to those of HFA and NTD, highlighting the potential role of the MMN amplitude index in helping clinicians and researchers to distinguish between HIP and HFA. Nevertheless, further research on the topic is strongly needed.

Enhancement for P300-speller classification using multi-window discriminative canonical pattern matching

Objective. P300s are one of the most studied event-related potentials (ERPs), which have been widely used for brain–computer interfaces (BCIs). Thus, fast and accurate recognition of P300s is an important issue for BCI study. Recently, there emerges a lot of novel classification algorithms for P300-speller. Among them, discriminative canonical pattern matching (DCPM) has been proven to work effectively, in which discriminative spatial pattern (DSP) filter can significantly enhance the spatial features of P300s. However, the pattern of ERPs in space varies with time, which was not taken into consideration in the traditional DCPM algorithm. Approach. In this study, we developed an advanced version of DCPM, i.e. multi-window DCPM, which contained a series of time-dependent DSP filters to fine-tune the extraction of spatial ERP features. To verify its effectiveness, 25 subjects were recruited and they were asked to conduct the typical P300-speller experiment. Main results. As a result, multi-window DCPM achieved the character recognition accuracy of 91.84% with only five training characters, which was significantly better than the traditional DCPM algorithm. Furthermore, it was also compared with eight other popular methods, including SWLDA, SKLDA, STDA, BLDA, xDAWN, HDCA, sHDCA and EEGNet. The results showed multi-window DCPM preformed the best, especially using a small calibration dataset. The proposed algorithm was applied to the BCI Controlled Robot Contest of P300 paradigm in 2019 World Robot Conference, and won the first place. Significance. These results demonstrate that multi-window DCPM is a promising method for improving the performance and enhancing the practicability of P300-speller.

A P300 Brain-Computer Interface Paradigm Based on Electric and Vibration Simple Command Tactile Stimulation.

This paper proposed a novel tactile-stimuli P300 paradigm for Brain-Computer Interface (BCI), which potentially targeted at people with less learning ability or difficulty in maintaining attention. The new paradigm using only two types of stimuli was designed, and different targets were distinguished by frequency and spatial information. The classification algorithm was developed by introducing filters for frequency bands selection and conducting optimization with common spatial pattern (CSP) on the tactile evoked EEG signals. It features a combination of spatial and frequency information, with the spatial information distinguishing the sites of stimuli and frequency information identifying target stimuli and disturbances. We investigated both electrical stimuli and vibration stimuli, in which only one target site was stimulated in each block. The results demonstrated an average accuracy of 94.88% for electrical stimuli and 95.21% for vibration stimuli, respectively.

Prediction of Navigational Decisions in the Real-World: A Visual P300 Event-Related Potentials Brain-Computer Interface

ABSTRACT Despite the widespread availability of portable neuroimaging systems, current applications of brain-computer interfaces (BCI) have largely remained confined to laboratory and clinical settings. In order to develop BCI that will assist individuals across a wide range of everyday life behaviors, it is critical to test paradigms that have been successful in highly controlled setups within the frame of real-world environments. In the present study, a visual P300 paradigm was used to elicit neural responses reflective of decision making within the frame of real-world navigation. Participants (n = 8) were equipped with a tablet and a mobile EEG system while navigating through university corridors. Upon reaching an intersection, participants were presented with left and right arrows flashing on the tablet (and additional non-directional stimuli in the distractor condition). Neural responses elicited by the presentation of such stimuli (Event-Related Potentials) were recorded along with the participant’s navigational decisions. The participants completed two separate sessions to collect data for both the training and testing of an offline BCI system aimed at predicting their navigational decisions based on the extraction of event-related potentials features. Single-trial classification accuracy reached 59.6% (up to 72.3% when classifying group averages of six trials). Individual classification results were contrasted with single-trial analysis. Results are discussed in terms of their implications for the design of real-world BCI applications, and several recommendations to improve experimental protocols are proposed.

Single-Option P300-BCI Performance Is Affected by Visual Stimulation Conditions.

The P300 paradigm is one of the most promising techniques for its robustness and reliability in Brain-Computer Interface (BCI) applications, but it is not exempt from shortcomings. The present work studied single-trial classification effectiveness in distinguishing between target and non-target responses considering two conditions of visual stimulation and the variation of the number of symbols presented to the user in a single-option visual frame. In addition, we also investigated the relationship between the classification results of target and non-target events when training and testing the machine-learning model with datasets containing different stimulation conditions and different number of symbols. To this end, we designed a P300 experimental protocol considering, as conditions of stimulation: the color highlighting or the superimposing of a cartoon face and from four to nine options. These experiments were carried out with 19 healthy subjects in 3 sessions. The results showed that the Event-Related Potentials (ERP) responses and the classification accuracy are stronger with cartoon faces as stimulus type and similar irrespective of the amount of options. In addition, the classification performance is reduced when using datasets with different type of stimulus, but it is similar when using datasets with different the number of symbols. These results have a special connotation for the design of systems, in which it is intended to elicit higher levels of evoked potentials and, at the same time, optimize training time.

A dual-arm mobile robot system performing assistive tasks operated via P300-based brain computer interface

PurposeIn the past decade, more than 700 million people are affected by some kind of disability or handicap. In this context, the research interest in assistive robotics is growing up. For people with mobility impairments, daily life operations, as dressing or feeding, require the assistance of dedicated people; thus, the use of devices providing independent mobility can have a large impact on improving their life quality. The purpose of this paper is to present the development of a robotic system aimed at assisting people with this kind of severe motion disabilities by providing a certain level of autonomy.Design/methodology/approachThe system is based on a hierarchical architecture where, at the top level, the user generates simple and high-level commands by resorting to a graphical user interface operated via a P300-based brain computer interface. These commands are ultimately converted into joint and Cartesian space tasks for the robotic system that are then handled by the robot motion control algorithm resorting to a set-based task priority inverse kinematic strategy. The overall architecture is realized by integrating control and perception software modules developed in the robots and systems environment with the BCI2000 framework, used to operate the brain–computer interfaces (BCI) device.FindingsThe effectiveness of the proposed architecture is validated through experiments where a user generates commands, via an Emotiv Epoc+ BCI, to perform assistive tasks that are executed by a Kinova MOVO robot, i.e. an omnidirectional mobile robotic platform equipped with two lightweight seven degrees of freedoms manipulators.Originality/valueThe P300 paradigm has been successfully integrated with a control architecture that allows us to command a complex robotic system to perform daily life operations. The user defines high-level commands via the BCI, letting all the low-level tasks, for example, safety-related tasks, to be handled by the system in a completely autonomous manner.

Performance improvement of P300-based home appliances control classification using convolution neural network

P300 speller-based brain-computer interface (BCI) is an immediate correspondence from a human brain to a computer that depends on the translation of mind reactions produced by stimulus of a subject utilizing a P300 speller. No muscle movements are required for this communication. The present study helps the disabled people (viz.patients with spinal cord injury, spastic cerebral palsy, locomotive diseases, etc.) ease their lives by accessing light, fan, mobile device, door, television, electric heater, etc. A novel 2 × 3 matrix consisting of home appliances visuals has been proposed as a P300 paradigm. Once the matrix visualization is completed, auditory feedback was provided and the chosen command was executed. It enables six menus to be navigated for hadling six electronic systems with up to 30 control commands. The objective of this research is to develop a P300-based BCI system for operating different electronic appliances at home. The existing BCI-based P300 spellers with standard machine learning methods suffers from low information transfer rate (ITR) and poor classification accuracy. With the application of convolution neural network (CNN) for classification, the proposed system improves the performance in terms of P300 classification & target appliances detection. The proposed system is designed to be user-convenient & cost-effective in terms of hardware design. The experiments have been performed on the dataset acquired from 30 target item selections from nine subjects using 16 channel actiCAP Xpress EEG recorders. The experimental result demonstrates that the proposed CNN model achieved 90% classification accuracy. It also offers ITR of 22.3 bits per minute which is substantially greater than the existing methods. The presented approach shows its novelty in the real-time applications.

Brain–computer interface‐based single trial P300 detection for home environment application

P300 speller-based brain–computer interface (BCI) is an immediate correspondence between the human brain and computer that depends on the translation of mind reactions produced by the stimulus of a subject utilising the P300 speller. No muscle movements are required for this communication. As a P300 paradigm, a novel 2 × 3 matrix consisting of visual home appliances is proposed, which helps disabled people ease their lives by accessing mobile, light, fan, door, television, electric heater etc. In most of the current P300-based BCIs, 5–15 trials work better and the low information transfer rate (ITR) is a major issue in its adaptation in real-time. The objective of this Letter is to improve accuracy as well as an ITR for real-time home appliance control applications. To address this, the authors proposed a single trial weighted ensemble of compact convolution neural network and obtained an ITR of 46.45 bits per minute and an average target appliance accuracy of 93.22% for the BCI-based home environment system. The experimental findings confirmed the feasibility of the proposed method and thus can provide guidance for future use of the system for paralysed patients.

A Tactile-based Brain Computer Interface P300 Paradigm Using Vibration Frequency and Spatial Location

Visual based brain-computer interface (BCI) has been widely investigated for the severely disabled patients. However, this modality is not applicable to the persons who have lost their visual function. This study aims to develop an alternative BCI using tactile stimuli only. An event-related potential (ERP) based BCI applying vibrotactile stimuli was proposed. It featured a combination of frequency and spatial information as a cue for binary choices. Two identical mechanical vibrotactile tactors were tied to each index finger of the two hands of a subject, to provide vibrotactile stimuli at various frequencies and constitute a three-stimulus oddball paradigm with one attended stimulus, one ignored stimulus and one disturbance stimulus. Ten healthy subjects participated in the experiments, and the classification of P300 signals was conducted by a stepwise linear discriminate analysis algorithm. Significant P300 components were evoked at approximately 360 ms after the onset of the target stimuli for each subject, and an average classification accuracy of 79% was achieved. This work demonstrates the feasibility of employing vibrotactile stimuli to build an ERP based BCI. The patients without a functional visual system could benefit from the presented paradigm.

A novel hybrid paradigm based on steady state visually evoked potential & P300 to enhance information transfer rate

BackgroundSteady-State Visually Evoked Potential (SSVEP) has been one of the most common paradigms for Brain-Computer Interface (BCI) based applications with relatively good accuracy and Information Transfer Rate (ITR). However, limited decision options for a set number of paradigm frequencies, which is the main reason behind low ITR, is a huge hurdle the researchers have been facing in generalising SSVEP based BCI applications. New methodThis paper proposes a novel hybrid Brain-Computer Interface (hBCI) stimuli paradigm to improve ITR by increasing the number of decision options available for SSVEP BCI by introducing P300 as a Time Division Multiplexing (TDM) marker. One of the Hybrid BCI’s used distinct colours along with distinct flickering frequencies for targets, with an aim to improve the performance of the BCI based on the accuracy of classification, the elevation of ITR and reduction of FAR as compared to traditional BCIs. ResultsIt was established that the Novel SSVEP-P300 with distinct colours for target frequencies hybrid BCI had average parameters as following: classification accuracy of 92.30 %, ITR of 82.38 bits/min and FAR of 2.72 %. ComparisonA comparative study between the two novel paradigms, traditional SSVEP and P300 paradigms in the same environment was conducted. And a statistical inference was established sussing pairedt-tests. ConclusionThe results of the comparative study were conclusive that the hybrid BCI with distinct colours for each target frequency yielded best results and hence can be considered as a viable paradigm option for the development of an Assistive Device.

Monitoring the Clinical Evolution of A Psychotic Patient Presenting A First-Schizophrenic Episode Thanks to Bimodal Oddball-P300 Event-Related Potentials: First Evidence from A Single-Case Study

Objective: Previous studies showed that a bimodal oddball design allows to increase the sensitivity of the P300 in subjects with anxious-depressive tendencies and patients presenting an adjustment disorder. In this case report we illustrate the increased sensitivity of a bimodal oddball task in the clinical follow-up of a patient presenting a psychosis in the context of a first onset schizophrenia. Method: The patient was instructed to detect deviant stimuli in three separate oddball tasks by hitting a button. Tasks comprised an auditory, a visual and a synchronized and congruent audio-visual block of stimuli. The peak-latency and the amplitude of the P300 evoked response were compared and confronted to scores of the clinical assessment. Results: Only the bimodal task mirrored the clinical evolution of the patient, as the reduction of psychotic symptoms was accompanied by an increase of the bimodal P300 amplitude. However, this was not true for both unimodal tasks (auditory and visual). Conclusions: A bimodal oddball design combined with unimodal ones could be necessary to monitor the joined clinical/neurophysiological evolution of a patient presenting a first onset psychosis. Significance: Further longitudinal studies applying the bimodal P300 paradigm should be designed in order to verify whether the oddball-bimodal P300 component could be used as “state” and/ or “trait” biological marker of the disease.