P300 Signals Research Articles

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 Comparative Study of Classification Techniques for P300 Speller

P300 speller in Brain Computer Interface (BCI) allows locked-in or completely paralyzed patients to communicate with humans. To achieve the performance of characterization and increase accuracy, machine learning techniques are used. The study is about an event related potential (ERP) P300 signal detection and classification using various machine learning algorithms. Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) are used to classify P300 and Non-P300 signal from Electroencephalography (EEG) signal. The performance of the system is evaluated based on f1-score using BCI competition III dataset II. In our system, we used LDA and SVM classification algorithms. Both the classifiers gave 91.0% classification accuracy.

MULTIPLE CLASSIFICATION TECHNIQUES TOWARD A ROBUST AND RELIABLE P300 BCI SYSTEM

A brain–computer interface (BCI) can be used for people with severe physical disabilities such as ALS or amyotrophic lateral sclerosis. BCI can allow these individuals to communicate again by creating a new communication channel directly from the brain to an output device. BCI technology can allow paralyzed people to share their intent with others, and thereby demonstrate that direct communication from the brain to the external world is possible and that it might serve useful functions. BCI systems include machine learning algorithms (MLAs). Their performance depends on the feature extraction and classification techniques employed. In this paper, we propose a system to exploit the P300 signal in the brain, a positive deflection in event-related potentials. The P300 signal can be incorporated into a spelling device. There are two benefits behind this kind of research. First of all, this work presents the research status and the advantages of communication via a BCI system, especially the P300 BCI system for disordered people, and the related literature review is presented. Secondly, the paper discusses the performance of different machine learning algorithms. Two different datasets are presented: the first dataset 2004 and the second dataset 2019. A preprocessing step is introduced to the subjects in both datasets first to extract the important features before applying the proposed machine learning methods: linear discriminant analysis (LDA I and LDA II), support vector machine (SVM I, SVM II, SVM III, and SVM IV), linear regression (LREG), Bayesian linear discriminant analysis (BLDA), and twin support vector machine (TSVM). By comparing the performance of the different machine learning systems, in the first dataset it is found that BLDA and SVMIV classifiers yield the highest performance for both subjects “A” and “B”. BLDA yields 98% and 66% for 15th and 5th sequences, respectively, whereas SVMIV yields 98% and 54.4% for 15th and 5th sequences, respectively. While in the second dataset, it is obvious that BLDA classifier yields the highest performance for both subjects “1” and “2”, it achieves 90.115%. The paper summarizes the P300 BCI system for the two introduced datasets. It discusses the proposed system, compares the classification methods performances, and considers some aspects for the future work to be handled. The results show high accuracy and less computational time which makes the system more applicable for online applications.

Improving the Performance of Brain-Computer Interface using Deep Learning Algorithms and Event-Related Spectral Perturbation

Brain Computer Interface allows disabled people to communicate with the external world by using their brain signals. The main goal of a BCI is to provide patients who suffer form any neuromuscular disorders whith a communication channel based on their brain signals. In this paper, the aim is to explore the effects of applying deep learning algorithms and Event Related Spectral Perturbation analyses on the performance of different EEG-based BCI paradigms. Two paradigms were investigated: one is based on the Matrix paradigm (known as oddball); and the other one utilizes the Rapid serial visual Presentation (RSVP) for presenting the stimuli. Deep learning algorithms of convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) were utilized to evaluate the two paradigms. Our findings showed that Matrix paradigm is more effective in detecting P300 signal. In terms of classification methods, deep learning of CNN algorithm has shown superiority performance in comparison with the other machine learning algorithms.

Spark and Rule-KNN based scalable machine learning framework for EEG deceit identification

Brain computer interface (BCI) provides communication between the computer and the brain. It is the combination of hardware and software which provides non-muscular channel to send the various messages to control the computer. BCI is useful in various medical applications such as patients with neuromuscular injuries, locked-in syndrome (LiS) etc. BCI is not only useful in medical applications, but also useful in lie detection, entertainment, etc. In this paper, spark and rule-KNN based scalable framework has been presented using BCI with the EEG data collected on 20 subjects in which 10 are acted as innocent and 10 are acted as guilty. Using BCI P300, Deceit identification Test (DIT) is performed. To perform DIT, we classify the P300 signals which have a positive peak of 300 ms–1000 ms in one stimulus start. Data processing is performed with band pass filter to cut the frequency ranges and features are extracted using non-parametric weighted feature extraction followed by rule based discriminant classification. For training and testing, the data ratio selected as 80:20 and achieved the accuracy 92.46 %. Proposed framework provides better results in comparison with existing models presented in literature. Hence this model is accurate, scalable and fault tolerant.

3D Input Convolutional Neural Networks for P300 Signal Detection

P300 signal is an endogenous event related potential component. It is mostly elicited from the frontal to parietal brain lobes. Electroencephalography is used for acquiring P300 signal from scalp. P300 signal is used for brain-computer interface systems. P300 based brain-computer interface systems are preferable since they have high overall performance. The most significant overall performance indicator is information transfer rate for P300 based brain-computer interface systems. P300 signal detection accuracy and P300 detection time are using for information transfer rate calculation. Hence, P300 signal classification accuracy is important for getting higher information transfer rate. In this study, it is aimed to investigate P300 detection model for higher classification accuracy. Thus, it is proposed 3-dimensional input convolutional neural network model for P300 detection. Moreover, the proposed model was applied with region based P300 speller which constituted audio and visual stimuli. In experiments, the participants were asked to spell desired words in two sessions which were offline and online session. Linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and the proposed method were compared in both online and offline sessions. It is reached highest average classification accuracy rate with the proposed method in both sessions. According to the online session result, average classification accuracy was 94.22% in 3-dimensional input convolutional neural network model. Furthermore, average information transfer rate was 5.53 bit/min in 3-dimensional input convolutional neural network model. We have also applied methods on BCI competition III-dataset II for 2 participants “A” and “B” for evaluating performance of algorithms. The proposed method had higher classification accuracy rate than linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and multi-classifier convolutional neural network which was used in other study on same dataset.

P300 characterization using empirical mode decomposition

As the communication between brain and computer becomes more accessible the extraction of important features of electrophysiological signals is an essential step in artificial communication systems. This paper proposes the usage of the Empirical Mode Decomposition to identify characteristics of the P300 signal and classify target and non-target signals using a feedforward neural network. The results show that through the usage of EMD method it is possible to identify the P300 signal using low volume of data.

P300 ve DHGUP Tabanlı Hibrid BBA Heceleyicisi için Yeni bir Yaklaşım

P300 and steady state visual evoked potential (SSVEP) are type of electroencephalography (EEG) phenomena that widely used in brain computer interface (BCI) systems since both of them have high signal response and signal noise ratio. Classification accuracy rate of signal, and signal detection time affect overall performance of BCI systems. These both values are used for calculation information transfer rate (ITR) that is a key performance indicator for a BCI system. A P300 based BCI or a SSVEP based BCI have higher ITR values than other type of BCI systems. Thus, in this study our aim was to use together these both P300 and SSVEP phenomena in a BCI speller. We proposed a hybrid BCI speller based on P300 and SSVEP. Moreover, our proposed BCI speller interface allows to use only P300 stimuli, only SSVEP stimuli, or hybrid stimuli. In this BCI speller, there are numbers in 3 × 3 matrix form for elicitind P300 signal and also 9 white square flickering objects were placed near numbers for eliciting SSVEP. In this research, experiments were performed in two stage (training and online stages) with three sessions (only SSVEP stimuli session, only P300 stimuli session, and hybrid session). Five subjects participated experiments. We used support vector machine method for detection of P300 signal and SSVEP. According to experiment results, average classification accuracy values were 83.78%, 84.67%, and 90.89% with using only SSVEP stimuli, only P300 stimuli, and hybrid stimuli, respectively. Furhermore, average information transfer rate values were 6.81, 6.97, and, 8.19 bit/min with using only SSVEP stimuli, only P300 stimuli, and hybrid stimuli, respectively. Results showed that the proposed hybrid BCI speller based on P300 and SSVEP reached higher classification accuracy and ITR values than using only SSVEP stimuli or only P300 stimuli based BCI spellers.

Channel Automatic Selection Algorithm for P300 Signal with Group Sparsity Bayesian Logistic Regression

Channel Automatic Selection Algorithm for P300 Signal with Group Sparsity Bayesian Logistic Regression

A Deep Learning Architecture for P300 Detection with Brain-Computer Interface Application

In this paper, a brain-computer interface (BCI) system for character recognition is proposed based on the P300 signal. A P300 speller is used to spell the word or character without any muscle movement. P300 detection is the first step to detect the character from the electroencephalogram (EEG) signal. The character is recognized from the detected P300 signal. In this paper, sparse autoencoder (SAE) and stacked sparse autoencoder (SSAE) based feature extraction methods are proposed for P300 detection. This work also proposes a fusion of deep-features with the temporal features for P300 detection. A SSAE technique extracts high-level information about input data. The combination of SSAE features with the temporal features provides abstract and temporal information about the signal. An ensemble of weighted artificial neural network (EWANN) is proposed for P300 detection to minimize the variation among different classifiers. To provide more importance to the good classifier for final classification, a higher weightage is assigned to the better performing classifier. These weights are calculated from the cross-validation test. The model is tested on two different publicly available datasets, and the proposed method provides better or comparable character recognition performance than the state-of-the-art methods.

Evaluation of emotional and neutral pictures as flashing stimuli using a P300 brain–computer interface speller

Objective. Previous works have reported that complex emotional and visual stimuli can increase the amplitude of the P300 brain potential. Thus, the aim of the present work is to assess these kinds of images in a P300 brain–computer interface (BCI) speller as flashing stimuli. Approach. Twenty-three volunteers controlled four spellers with different sets of flashing stimuli: flashing letters, neutral pictures (NP), emotional pleasant pictures (EPP) and emotional unpleasant pictures (EUP). Main results. The sets of pictures showed a higher performance than the letters in accuracy and information transfer rate. These results were supported by the analysis of the P300 signal, where the picture sets offered the greatest amplitudes. The NP and EPP sets were the best evaluated in the subjective questionnaire. Significance. In short, despite the fact that the effect of emotional stimuli could not be observed in the performance metrics, picture sets have offered a high performance and should be considered in future proposals for visual P300-based BCI applications.

P300 based character recognition using convolutional neural network and support vector machine

In this work, a brain–computer interface (BCI) system for character recognition has been proposed based on the P300 signal. P300 signal classification is the most challenging task in electroencephalography signal processing as it is affected by the surrounding noise and low signal-to-noise ratio (SNR). Feature extraction and feature selection are essential steps for any classification task. Most of the earlier techniques reported hand-crafted features for detection of P300 signal. However, the hand-crafted features are not efficient to represent the signal properly due to surrounding environment and subject variability. Motivated by this, convolutional neural network (CNN) has been proposed for automatic high-level feature extraction to detect P300 signal. In general, CNN model consists of convolutional and fully-connected layers followed by a softmax layer. In the developed system, two different convolutional layers are used to extract the spatial and temporal features from the dataset. Also, a 2D convolutional layer based CNN architecture has been proposed where spatio-temporal feature is extracted in a single layer. To mitigate the over-fitting problem, dropout is employed in CNN architecture, which improves the network performance. After extracting high-level features, Fisher ratio (F-ratio) based feature selection is proposed to find the optimal features. The optimal features are used in the ensemble of support vector machine (ESVM) classifier for P300 detection. ESVM has been adopted in this work to minimize the classifier variability. The models are tested on two widely used datasets, and the experimental results show better or comparable performance compared to the earlier reported techniques.

Statistical context dictates the relationship between feedback-related EEG signals and learning.

Learning should be adjusted according to the surprise associated with observed outcomes but calibrated according to statistical context. For example, when occasional changepoints are expected, surprising outcomes should be weighted heavily to speed learning. In contrast, when uninformative outliers are expected to occur occasionally, surprising outcomes should be less influential. Here we dissociate surprising outcomes from the degree to which they demand learning using a predictive inference task and computational modeling. We show that the P300, a stimulus-locked electrophysiological response previously associated with adjustments in learning behavior, does so conditionally on the source of surprise. Larger P300 signals predicted greater learning in a changing context, but less learning in a context where surprise was indicative of a one-off outlier (oddball). Our results suggest that the P300 provides a surprise signal that is interpreted by downstream learning processes differentially according to statistical context in order to appropriately calibrate learning across complex environments.

P300 based character recognition using sparse autoencoder with ensemble of SVMs

In this study, a brain–computer interface (BCI) system known as P300 speller is used to spell the word or character without any muscle activity. For P300 signal classification, feature extraction is an important step. In this work, deep feature learning techniques based on sparse autoencoder (SAE) and stacked sparse autoencoder (SSAE) are proposed for feature extraction. Deep feature provides the abstract information about the signal. This work proposes fusion of deep features with the temporal features, which provides abstract and temporal information about the EEG signal. These deep feature and temporal feature are partially complement of each other to represent the EEG signal. For classification of the EEG signal, an ensemble of support vector machines (ESVM) is adopted as it helps to reduce the classifiers variability. In classifier ensemble system, the score of individual classifier is not at the same level. To transform these scores into a common level, min–max normalization is proposed prior to combining them. Min-max normalization scales the classifiers’ score between 0 and 1. The experiments are conducted on three standard public datasets, dataset IIb of BCI Competition II, dataset II of the BCI Competition III and BNCI Horizon dataset. The experimental results show that the proposed method yields better or comparable performance compared to earlier reported techniques.

2 Stages-region-based P300 Speller in Brain–Computer Interface

ABSTRACTBrain–computer interface (BCI) applications present communication model without using peripheral nerves and neuromuscular systems. The P300 waves are used in BCI applications. Signal classification accuracy is a significant parameter for P300 BCI application. In this study, our goal is to investigate P300 speller structure for higher classification accuracy. There are a lot studies about P300 speller variations of stimulus model. This models mostly includes about row–column-based visual or audio stimuli model P300 spellers. But there is not enough study about region-based P300 spellers. This study contributes to region-based P300 spellers researches. Our new paradigm 2-stages region-based P300 speller has different amount of regions and stimulus position. During the experiment also, row–column-based P300 speller was used for comparing accuracy rates with our unique design 2-stages region-based P300 speller. The subject focused on the desired character stimulus. We used the stepwise linear discriminant analysis (SWLDA) method for classification that either included the desired P300 signal or not. According to SWLDA, the maximum mean classification accuracy value of the experiment was 83.33% with 2-stages region-based P300 speller. With this new paradigm, the classification accuracy was improved by 23.89% according to the most commonly used row–column-based P300 speller.

A brain interface to capture your attention: An EEG headpiece for children with ADHD is now maker friendly - [Resources_Hands on

I AM A FASHION-TECH DESIGNER: I COMBINE FASHION WITH engineering, science, and interactive user-experience technologies. When worn, many of my designs monitor physiological indicators—such as heart rate—and react to this information in some way, communicating the wearer's internal state. In 2016, I was in Linz, Austria, as an Ars Electronica Futurelab artist-in-residence, and I decided to take the opportunity to see if I could apply my techniques therapeutically. • The result was a headpiece that helps children with attention-deficit hyperactivity disorder (ADHD) and their caregivers better understand what environmental cues are associated with symptomatic problems. In April, the underlying technology became commercially available for makers interested in brain-computer interfaces (BCIs). • When I arrived in Linz, I was already interested in electroencephalography (EEG) devices, which measure the brain's electrical activity, as I saw them as an extension of my earlier work. Through Ars Electronica I met Dominik Laister at the nearby Barmherzige Brüder Hospital, who became a valuable advisor. After consulting with him, I decided to focus on what is known as the P300 event-related brain potential signal. P300 is a frequent focus of clinical exams and BCI research. It is a voltage pulse, often thought to be connected to attention and decision making, that occurs a few hundred milliseconds after an external stimulus. The P300 signal is often measured when diagnosing children with ADHD because the signal takes longer to manifest and isn't as strong as it is in children without ADHD.

An efficient EEG based deceit identification test using wavelet packet transform and linear discriminant analysis

BackgroundBrain–computer interface (BCI) is a combination of hardware and software that provides a non-muscular channel to send various messages and commands to the outside world and control external devices such as computers. BCI helps severely disabled patients having neuromuscular injuries, locked-in syndrome (LiS) to lead their life as a normal person to the best extent possible. There are various applications of BCI not only in the field of medicine but also in entertainment, lie detection, gaming, etc. MethodologyIn this work, using BCI a Deceit Identification Test (DIT) is performed based on P300, which has a positive peak from 300 ms to 1000 ms of stimulus onset. The goal is to recognize and classify P300 signals with excellent results. The pre-processing has been performed using the band-pass filter to eliminate the artifacts. Comparison with existing methodsWavelet packet transform (WPT) is applied for feature extraction whereas linear discriminant analysis (LDA) is used as a classifier. Comparison with the other existing methods namely BCD, BAD, BPNN etc has been performed. ResultsA novel experiment is conducted using EEG acquisition device for the collection of data set on 20 subjects, where 10 subjects acted as guilty and 10 subjects acted as innocent. Training and testing data are in the ratio of 90:10 and the accuracy obtained is up to 91.67%. The proposed approach that uses WPT and LDA results in high accuracy, sensitivity, and specificity. ConclusionThe method provided better results in comparison with the other existing methods. It is an efficient approach for deceit identification for EEG based BCI.

A New Paradigm for Region-Based P300 Speller in Brain Computer Interface

Electroencephalography-based brain computer interface systems could provide alternative communication methods for severely disabled people who cannot use their neuromuscular systems. The P300 signal is one of the event related potentials that are used for brain computer interface systems. The most important performance parameter of a P300 based brain computer interface system is information transfer rate that is calculated by using classification accuracy and P300 signal detection time. Moreover, P300 speller has a very critical role for classification accuracy and information transfer rate in a P300 based brain computer interface. Although most of studies are about row column based P300 speller in literature, region based P300 speller proved that has higher classification accuracy than row column based one. There are very few studies about region based P300 speller. This study aims to investigate methods for obtaining higher classification accuracy and information transfer rate with using region based P300 speller that constituted audio and visual stimulus. This is the first research that using audio and visual stimulus for a region based P300 speller in literature. Previous studies about region based P300 spellers focused on spellers with only visual stimulus types. Our new paradigm presents region based P300 spellers with only audio, only visual, and audio-visual stimuli. Audio-visual P300 speller structure is the newest model for region based spellers. The subject focused on the desired character stimulus. We used the stepwise linear discriminant analysis method for classification that either included the desired P300 signal or not. According to stepwise linear discriminant analysis, the mean classification accuracy value of the experiment was 90.31% with the audio-visual region based P300 speller. With this new paradigm, classification accuracy in the audio-visual P300 speller was improved 15.69% and 66,99% according to the visual only and audio only P300 speller that we used in the experiments, respectively.

Selection of effective probes for an individual to identify P300 signal generated from P300 BCI speller

P300 is a strong event related potential (ERP) generated in the brain and observed on the scalp when an unusual event happens. To decipher P300 signal, we have to use the property of P300 to distinguish P300 signal from non-P300 signal. In this work, we used data collected from P300 BCI Speller with 128 probes. Conventional BCI speller uses eight probes at pre-defined locations on the skull. Though P300 is strong in the parietal region of the brain, location of the strongest signal varies from person to person. The idea is that, if we optimise probe locations for an individual, we could reduce the number of probes required. In fact, the process mode for the raw brain wave signals also will affect the classification accuracy. We designed an algorithm to analyse the raw signals. We achieved over 81% classification accuracy on average with only three probes from only one target stimulus and one non-target stimulus.

Selection of effective probes for an individual to identify P300 signal generated from P300 BCI speller

Selection of effective probes for an individual to identify P300 signal generated from P300 BCI speller