Detection Of Event-related Potentials Research Articles

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

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

Event-related potentials indicate differential neural reactivity to species and valence information in vocal stimuli in sleeping dogs

Dogs live in a complex social environment where they regularly interact with conspecific and heterospecific partners. Awake dogs are able to process a variety of information based on vocalisations emitted by dogs and humans. Whether dogs are also able to process such information while asleep, is unknown. In the current explorative study, we investigated in N = 13 family dogs, neural response to conspecific and human emotional vocalisations. Data were recorded while dogs were asleep, using a fully non-invasive event-related potential (ERP) paradigm. A species (between 250–450 and 600–800 ms after stimulus onset) and a species by valence interaction (between 550 to 650 ms after stimulus onset) effect was observed during drowsiness. A valence (750–850 ms after stimulus onset) and a species x valence interaction (between 200 to 300 ms and 450 to 650 ms after stimulus onset) effect was also observed during non-REM specific at the Cz electrode. Although further research is needed, these results not only suggest that dogs neurally differentiate between differently valenced con- and heterospecific vocalisations, but they also provide the first evidence of complex vocal processing during sleep in dogs. Assessment and detection of ERPs during sleep in dogs appear feasible.

The effect of neural pre-stimulus oscillations on post-stimulus somatosensory event-related potentials in disorders of consciousness.

Brain activity of people in a disorder of consciousness (DoC) is diffuse and different from healthy people. In order to get a better understanding of their cognitive processes and functions, electroencephalographic activity has often been examined in patients with DoC, including detection of event-related potentials (ERPs) and spectral power analysis. However, the relationship between pre-stimulus oscillations and post-stimulus ERPs has rarely been explored in DoC, although it is known from healthy participants that pre-stimulus oscillations predispose subsequent stimulus detection. Here, we examine to what extent pre-stimulus electroencephalography band power in DoC relates to post-stimulus ERPs in a similar way as previously documented in healthy people. 14 DoC patients in an unresponsive wakefulness syndrome (UWS, N = 2) or a minimally conscious state (MCS, N = 12) participated in this study. In an active oddball paradigm patients received vibrotactile stimuli. Significant post-stimulus differences between brain responses to deviant and standard stimulation could be found in six MCS patients (42.86%). Regarding relative pre-stimulus frequency bands, delta oscillations predominated in most patients, followed by theta and alpha, although two patients showed a relatively normal power spectrum. The statistical analysis of the relationship between pre-stimulus power and post-stimulus event-related brain response showed multiple significant correlations in five out of the six patients. Individual results sometimes showed similar correlation patterns as in healthy subjects primarily between the relative pre-stimulus alpha power and post-stimulus variables in later time-intervals. However, opposite effects were also found, indicating high inter-individual variability in DoC patients´ functional brain activity. Future studies should determine on an individual level to what extent the relationship between pre- and post-stimulus brain activity could relate to the course of the disorder.

A novel semi-supervised meta learning method for subject-transfer brain–computer interface

The brain–computer interface (BCI) provides a direct communication pathway between the human brain and external devices. However, the models trained for existing subjects perform poorly on new subjects, which is termed the subject calibration problem. In this paper, we propose a semi-supervised meta learning (SSML) method for subject-transfer calibration. The proposed SSML learns a model-agnostic meta learner with existing subjects and then fine-tunes the meta learner in a semi-supervised learning manner, i.e. using a few labelled samples and many unlabelled samples of the target subject for calibration. It is significant for BCI applications in which labelled data are scarce or expensive while unlabelled data are readily available. Three different BCI paradigms are tested: event-related potential detection, emotion recognition and sleep staging. The SSML achieved classification accuracies of 0.95, 0.89 and 0.83 in the benchmark datasets of three paradigms. The runtime complexity of SSML grows linearly as the number of samples of target subject increases so that is possible to apply it in real-time systems. This study is the first attempt to apply semi-supervised model-agnostic meta learning methodology for subject calibration. The experimental results demonstrated the effectiveness and potential of the SSML method for subject-transfer BCI applications.

ERP Detection Based on Smoothness Priors.

Detection of event-related potentials (ERPs) in electroencephalography (EEG) is of great interest in the study of brain responses to various stimuli. This is challenging due to the low signal-to-noise ratio of these deflections. To address this problem, a new scheme to detect the ERPs based on smoothness priors is proposed. The problem is considered as a binary hypothesis test and solved using a smooth version of the generalized likelihood ratio test (SGLRT). First, we estimate the parameters of probability density functions from the training data under the Gaussian assumption. Then, these parameters are treated as known values and the unknown ERPs are estimated under the smoothness constraint. The performance of the proposed SGLRT is assessed for ERP detection in post-stimuli EEG recordings of two oddball settings. We compared our method with several powerful methods regarding ERP detection. The presented method performs better than the competing algorithms and improves the classification accuracy. SGLRT can be employed as a powerful means for different ERP detection schemes. The proposed scheme is opening a new direction in ERP identification which provides better classification results compared to several popular ERP detection methods.

MCGRAM: Linking Multi-Scale CNN With a Graph-Based Recurrent Attention Model for Subject-Independent ERP Detection

Electroencephalogram (EEG)-based event-related potential (ERP) detection is expected to play an important role in many applications. However, limited by the low signal-to-noise ratio and large inter-subject variability of EEG signals, it is still challenging to design a high-precision ERP detection method in subject-independent scenarios. In this brief, a novel graph-based multi-scale convolutional recurrent attention model (MCGRAM) is proposed to extract the underlying invariant features of EEG signals for subject-independent ERP detection. Specifically, considering the frequency factors of ERP signals, the multi-scale convolution module is designed for learning the frequency representations. Then, the graph convolution module is developed by constructing the links between each EEG node and designing trainable weights of the links to extract the discriminative spatial features. Besides, two-layer LSTM combined with a self-attention model is utilized to extract temporal representations of different EEG slices. Compared to the existing state-of-the-art methods, MCGRAM achieves the best performance in terms of AUC and F1-Score on a rapid serial visual presentation benchmark dataset. Lastly, the ablation studies demonstrate the superiority of each component in MCGRAM.

Phase Analysis of Event-Related Potentials Based on Dynamic Mode Decomposition

Real-time detection of event-related potentials (ERPs) and exploration of ERPs generation mechanisms are vital to practical application of brain–computer interfaces (BCI). Traditional methods for ERPs analysis often fall into time domain, time–frequency domain, or spatial domain. Methods which can reveal spatiotemporal interactions by simultaneously analyzing multi-channel EEG signals may provide new insights into ERP research and is highly desired. Additionally, although phase information has been investigated to describe the phase consistency of a certain frequency component across different ERP trials, it is of research significance to analyze the phase reorganization across different frequency components that constitute a single-trial ERP signal. To address these problems, dynamic mode decomposition (DMD) was applied to decompose multi-channel EEG into a series of spatial–temporal coherent DMD modes, and a new metric, called phase variance distribution (PVD) is proposed as an index of the phase reorganization of DMD modes during the ERP in a single trial. Based on the PVD, a new error-related potential (ErrP) detection method based on symmetric positive defined in Riemann manifold is proposed to demonstrate the significant PVD differences between correct and error trials. By including the phase reorganization index, the 10-fold cross-validation results of an ErrP detection task showed that the proposed method is 4.98%, 27.99% and 7.98% higher than the counterpart waveform-based ErrP detection method in the terms of weighted accuracy rate, precision and recall of the ErrP class, respectively. The resulting PVD curve shows that with the occurrence of ERP peaks, the phases of different frequency rhythms are getting to aligned and yields a significant smaller PVD. Since the DMD modes of different frequencies characterize spatiotemporal coherence of multi-channel EEG at different functional regions, the new phase reorganization index, PVD, may indicate the instantaneous phase alignment of different functional networks and sheds light on a new interpretation of ERP generation mechanism.

BCI Accuracy Using Classifier-Based Latency Estimation and the Optimal Interstimulus Interval.

Detection of event-related potentials (ERPs) in brain-computer interfaces (BCIs) allow for communication by individuals with neuromuscular disorders. Enhancing BCI accuracy may be possible through the exploration of the optimal interstimulus interval (ISI). Our objective is to investigate the relationship between BCI accuracy and the optimal ISI value for an individual. Using the previously developed classifier-based latency estimation (CBLE) [1], we investigated the relationship between the interstimulus interval (ISI) and P3 Speller BCI accuracy. Participants underwent two consecutive sessions in one day. The first session had a default ISI value of 120ms. An optimal ISI value calculated from the first session was used in the second. Ten subjects participated in the study. Of the ten, half received an optimal ISI value of 120ms and half 160ms. Accuracy differences after implementing the adjusted ISI ranged from -26.1 percent to 4.35 percent. Suggestions for additional experimental design adjustments are highlighted under the discussion portion of this manuscript.

An EEG Classification-Based Method for Single-Trial N170 Latency Detection and Estimation.

Event-related potentials (ERPs) can reflect the high-level thinking activities of the brain. In ERP analysis, the superposition and averaging method is often used to estimate ERPs. However, the single-trial ERP estimation can provide researchers with more information on cognitive activities. In recent years, more and more researchers try to find an effective method to extract single-trial ERPs, because most of the existing methods have poor generalization ability or suffer from strong assumptions about the characteristics of ERPs, resulting in unsatisfactory results under the condition of a very low signal-to-noise ratio. In this paper, an EEG classification-based method for single-trial ERP detection and estimation was proposed. This study used a linear generated EEG model containing templates of ERP local descriptors which include amplitude and latency, and this model can avoid the invalid assumption about ERPs taken by other methods. The purpose of this method is not to recover the whole ERP waveform but to model the amplitude and latency of ERP components. This method afterwards examined the three machine learning models including logistic regression, neural network, and support vector machine in the EEG signal classification for ERP detection and selected the best performed MLPNN model for detection. To get the utmost out of information produced in the classification process, this study also used extra information to propose a new optimization model, with which outperformed detection results were obtained. Performance of the proposed method is evaluated on simulated N170 and real P50 data sets, and the results show that the model is more effective than the Woody filter and the SingleTrialEM algorithm. These results are also consistent with the conclusion of sensory gating, which demonstrated good generalization ability.

Single-Trial Detection of Event-Related Potentials with Integral Shape Averaging: An Application to the Elusive N400.

The estimation of Event-Related Potentials (ERPs) from the ambient EEG is a difficult task, usually achieved through the synchronous averaging of an extensive series of trials. However, this technique has some caveats: the ERPs have to be strictly time-locked with similar shape, i.e. emitted with the same latency and the same profile, with minor fluctuations of their amplitudes. Also, the method requires a huge number of valid trials (~100) to efficiently raise the ERPs from the EEG trials. In the case of cognitive ERPs, as with the N400, the delivered stimulus has to be different for each trial, the latencies are varying, and the number of available trials is usually low. In this paper, an alternative method, coined Integral Shape Averaging (ISA) and its derivatives are detailed. ISA is robust to varying latencies and affine transforms of shape. Furthermore, a new method coined ISAD can be derived to extract ERPs even from a single trial experiment. The aim here is to illustrate the potential of ISAD for N400 component extraction on real EEG data, with emphasis on its general applicability for ERPs computation and its major assets like reduced experimental protocol. Some insights are also given on its potential use to study ERP variability, through shape and latency.Clinical Relevance- The proposed algorithm aims to be a helpful tool in clinical practice to analyze and interpret evoked responses in real experimental settings, especially for particularities in neurology.

Event-related components are structurally represented by intrinsic event-related potentials

The detection of event-related potentials (ERPs) through electroencephalogram (EEG) analysis is a well-established method for understanding brain functions during a cognitive process. To increase the signal-to-noise ratio (SNR) and stationarity of the data, ERPs are often filtered to a wideband frequency range, such as 0.05–30 Hz. Alternatively, a natural-filtering procedure can be performed through empirical mode decomposition (EMD), which yields intrinsic mode functions (IMFs) for each trial of the EEG data, followed by averaging over trials to generate the event-related modes. However, although the EMD-based filtering procedure has advantages such as a high SNR, suitable waveform shape, and high statistical power, one fundamental drawback of the procedure is that it requires the selection of an IMF (or a partial sum of a range of IMFs) to determine an ERP component effectively. Therefore, in this study, we propose an intrinsic ERP (iERP) method to overcome the drawbacks and retain the advantages of event-related mode analysis for investigating ERP components. The iERP method can reveal multiple ERP components at their characteristic time scales and suitably cluster statistical effects among modes by using a tailored definition of each mode’s neighbors. We validated the iERP method by using realistic EEG data sets acquired from a face perception task and visual working memory task. By using these two data sets, we demonstrated how to apply the iERP method to a cognitive task and incorporate existing cluster-based tests into iERP analysis. Moreover, iERP analysis revealed the statistical effects between (or among) experimental conditions more effectively than the conventional ERP method did.

P300 event-related potential detection using one-dimensional convolutional capsule networks

The main challenge in creating a brain-computer interface (BCI) is establishing an effective brain signal recognition model suitable for achieving direct communication between humans and computers. Recently, various deep learning-based methods have been proposed to improve the performance of P300 event-related potentials (ERPs) for BCI. However, during the detection of P300 ERP signals, even electroencephalogram (EEG) signals from the same person are inconsistent and may be significantly distorted, resulting in impaired classification accuracy in many deep learning methods. Here, we propose a machine learning model based on a one-dimensional convolutional capsule network (1D-CapsNet). This network topology can effectively detect P300 ERP signals in the time domain, thereby achieving a better detection performance than can the current convolutional neural network (CNN)-based methods. Two classifiers based on the 1D-CapsNet model are proposed, namely, 1D-CapsNet-64 and 1D-CapsNet-8, which are used for classifying EEG data with 64 and 8 electrodes, respectively. These two classifiers are tested and compared on dataset II of the third BCI competition. The results show that the 1D-CapsNet-64 classifier obtains the best character recognition rate result (96%). The proposed method is superior to both state-of-the-art CNN-based methods and various traditional machine learning methods. The experimental results reveal the feasibility of our proposed method for detecting P300 ERP signals. The proposed method is expected to expand the concept of EEG signal recognition pattern and improve BCI design and applications.

Capsule Network for ERP Detection in Brain-Computer Interface.

Event-related potential (ERP) is bioelectrical activity that occurs in the brain in response to specific events or stimuli, reflecting the electrophysiological changes in the brain during cognitive processes. ERP is important in cognitive neuroscience and has been applied to brain-computer interfaces (BCIs). However, because ERP signals collected on the scalp are weak, mixed with spontaneous electroencephalogram (EEG) signals, and their temporal and spatial features are complex, accurate ERP detection is challenging. Compared to traditional neural networks, the capsule network (CapsNet) replaces scalar-output neurons with vector-output capsules, allowing the various input information to be well preserved in the capsules. In this study, we expect to utilize CapsNet to extract the discriminative spatial-temporal features of ERP and encode them in capsules to reduce the loss of valuable information, thereby improving the ERP detection performance for BCI. Therefore, we propose ERP-CapsNet to perform ERP detection in a BCI speller application. The experimental results on BCI Competition datasets and the Akimpech dataset show that ERP-CapsNet achieves better classification performances than do the state-of-the-art techniques. We also use a decoder to investigate the attributes of ERPs encoded in capsules. The results show that ERP-CapsNet relies on the P300 and P100 components to detect ERP. Therefore, ERP-CapsNet not only acts as an outstanding method for ERP detection, but also provides useful insights into the ERP detection mechanism.

EEG-Inception: A Novel Deep Convolutional Neural Network for Assistive ERP-Based Brain-Computer Interfaces.

In recent years, deep-learning models gained attention for electroencephalography (EEG) classification tasks due to their excellent performance and ability to extract complex features from raw data. In particular, convolutional neural networks (CNN) showed adequate results in brain-computer interfaces (BCI) based on different control signals, including event-related potentials (ERP). In this study, we propose a novel CNN, called EEG-Inception, that improves the accuracy and calibration time of assistive ERP-based BCIs. To the best of our knowledge, EEG-Inception is the first model to integrate Inception modules for ERP detection, which combined efficiently with other structures in a light architecture, improved the performance of our approach. The model was validated in a population of 73 subjects, of which 31 present motor disabilities. Results show that EEG-Inception outperforms 5 previous approaches, yielding significant improvements for command decoding accuracy up to 16.0%, 10.7%, 7.2%, 5.7% and 5.1% in comparison to rLDA, xDAWN + Riemannian geometry, CNN-BLSTM, DeepConvNet and EEGNet, respectively. Moreover, EEG-Inception requires very few calibration trials to achieve state-of-the-art performances taking advantage of a novel training strategy that combines cross-subject transfer learning and fine-tuning to increase the feasibility of this approach for practical use in assistive applications.

A few filters are enough: Convolutional neural network for P300 detection

Over the past decade convolutional neural networks (CNNs) have become the driving force of an ever-increasing set of applications, achieving state-of-the-art performance. Modern CNN architectures are often composed of many convolutional and some fully connected layers, and have thousands or millions of parameters. CNNs have shown to be effective in the detection of Event-Related Potentials from electroencephalogram (EEG) signals, notably the P300 component which is frequently employed in Brain-Computer Interfaces (BCIs). However, for this task, the increase in detection rates compared to approaches based on human-engineered features has not been as impressive as in other areas and might not justify such a large number of parameters. In this paper, we study the performance of existing CNN architectures with diverse complexities for single-trial within-subject and cross-subject P300 detection on four different datasets. We also proposed SepConv1D, a very simple CNN architecture consisting of a single depthwise separable 1D convolutional layer followed by a fully connected Sigmoid classification neuron. We found that with as few as four filters in its convolutional layer and an overall small number of parameters, SepConv1D obtained competitive performances in the four datasets. We believe these results may represent an important step towards building simpler, cheaper, faster, and more portable BCIs.

A Novel Method based on Regularized Logistic Regression and CCA for P300 Detection using a Reduced Number of EEG Trials

The Event-Related Potential (ERP) P300 is an Electroencephalography (EEG) signal evoked by external stimuli, characterised by a positive deflection at the 300 ms after presentation of an interesting stimulus for the user. In literature, P300-based Brain-Computer Interface (BCI) has been implemented to translate the subjects intent for restoring communication and control functions. In order to detect the ERP of the EEG signals and taking into account low signal-to-noise ratio (SNR) of the P300 signal, multiple trial averaging has been widely used, which permit diminishing the random noise. However, the signal averaging technique requires processing multiple EEG trials, thus the ITR (Information Transfer Rate) of the P300-based BCI is significantly reduced. An open challenge of P300-based BCI systems focuses on recognizing ERP signals (as target and no-target) using a reduced number of trials with an enough classification rate. In this work, we propose a novel method based on Regularized Logistic Regression (RLR) using as features the coefficients obtained from a Canonical Correlation Analysis (CCA), as method for detection of visual P300 ERPs using a reduced number of EEG trials (1, 2, 3 and 4 trials). The proposed method was evaluated with a freely available EEG dataset and was compared with two widely used methods: Mean-Amplitude LDA and Step-Wise LDA. Finally, the proposed method outperforms standard algorithms for P300 detection, providing a maximum accuracy of 86.3% (p=0.27 to Mean Amplitude LDA and p less than 0.05 to Stepwise-LDA) and maximum Area Under Curve (AUC) of the Receiver Operating characteristic (ROC) of 0.753 (p=0.715 to Mean Amplitude LDA and p=0.228 to Stepwise LDA) on the recognition. Despite the performance of some metrics of proposed method are not significant, the results allow concluding that this one have an acceptable performance for the recognition of P300, using a reduced number of trials.

Adaptive Time Segment Analysis for Steady-State Visual Evoked Potential Based Brain-Computer Interfaces.

The research in non-invasive Brain-Computer Interface (BCI) has led to significant improvements in the recent years for potential end users. However, the user experience and the BCI illiteracy problem remains challenging areas to address for obtaining robust and resilient clinical applications. In this study, we address the choice of the time segment for the detection of steady state visual evoked potential (SSVEP) detection. This problem has been widely addressed for the detection of event-related potentials compared to SSVEP based BCIs. The choice of this parameter is typically fixed and has a direct influence on both the detection accuracy and the information transfer rate. We propose to shift the problem of the time segment to the choice of the threshold for determining if a response has been properly detected. We consider two open-datasets for benchmarking the rationale of the approach. The results support the conclusion that an adaptive time segment for each trial, based on the selection of a threshold, can lead to a substantial higher ITR (86.92 bits/min), compared to the time segment chosen at the user (79.56 bits/min) or group level (73.78 bits/min). Finally, the results suggest that the threshold could be determined automatically in relation to the number of classes. Such an approach can leverage the literacy of SSVEP based BCI.

A Low-Cost Computational Method for Characterizing Event-Related Potentials for BCI Applications and Beyond

Event-related potentials (ERPs) are important neurophysiological markers widely used in scientific, medical and engineering contexts. Proper ERP detection contributes to widening the scope of use and, in general, improving functionality. The morphology and latency of ERPs are variable among subject sessions, which complicates their detection. Although variability is an intrinsic feature of neuronal activity, it can be addressed with novel views on ERP detection techniques. In this paper, we propose an agile method for characterizing and thus detecting variable ERPs, which keeps track of their temporal and spatial information through the continuous measurement of the area under the curve in ERP components. We illustrate the usefulness of the proposed ERP characterization for electrode selection in brain-computer interfaces (BCIs) and compare the results with other standard methods. We assess ERP classification for BCI use with Bayesian linear discriminant analysis (BLDA) and cross-validation. We also evaluate performance with both the information transfer rate and BCI utility. The results of our validation tests show that this characterization helps to take advantage of the information on the evolution of positive and negative ERP components and, therefore, to efficiently select electrodes for optimized ERP detection. The proposed method improves the classification accuracy and bitrate of all sets of electrodes analyzed. Furthermore, the method is robust between different day sessions. Our work contributes to the efficient detection of ERPs, manages inter- and intrasubject variability, decreases the computational cost of classic detection methods and contributes to promoting low-cost personalized brain-computer interfaces.

Robust detection of event-related potentials in a user-voluntary short-term imagery task.

Event-related potentials (ERPs) represent neuronal activity in the brain elicited by external visual or auditory stimulation and are widely used in brain-computer interface (BCI) systems. The ERP responses are elicited a few milliseconds after attending to an oddball stimulus; target and non-target stimuli are repeatedly flashed, and the ERP trials are averaged over time in order to improve their decoding accuracy. To reduce this time-consuming process, previous studies have attempted to evoke stronger ERP responses by changing certain experimental parameters like color, size, or the use of a face image as a target symbol. Since these exogenous potentials can be naturally evoked by merely looking at a target symbol, the BCI system could generate unintended commands while subjects are gazing at one of the symbols in a non-intentional mental state. We approached this problem of unintended command generation by assuming that a greater effort by the user in a short-term imagery task would evoke a discriminative ERP response. Three tasks were defined: passive attention, counting, and pitch-imagery. Users were instructed to passively attend to a target symbol, or to perform a mental tally of the number of target presentations, or to perform the novel task of imagining a high-pitch tone when the target symbol was highlighted. The decoding accuracy were 71.4%, 83.5%, and 89.2% for passive attention, counting, and pitch-imagery, respectively, after the fourth averaging procedure. We found stronger deflections in the N500 component corresponding to the levels of mental effort (passive attention: -1.094 ±0.88 μV, counting: -2.226 ±0.97 μV, and pitch-imagery: -2.883 ±0.74 μV), which highly influenced the decoding accuracy. In addition, the rate of binary classification between passive attention and pitch-imagery tasks was 73.5%, which is an adequate classification rate that motivated us to propose a two-stage classification strategy wherein the target symbols are estimated in the first stage and the passive or active mental state is decoded in the second stage. In this study, we found that the ERP response and the decoding accuracy are highly influenced by the user’s voluntary mental tasks. This could lead to a useful approach in practical ERP systems in two respects. Firstly, the user-voluntary tasks can be easily utilized in many different types of BCI systems, and performance enhancement is less dependent on the manipulation of the system’s external, visual stimulus parameters. Secondly, we propose an ERP system that classifies the brain state as intended or unintended by considering the measurable differences between passively gazing and actively performing the pitch-imagery tasks in the EEG signal thus minimizing unintended commands to the BCI system.

The Spatio-Temporal Equalization for Evoked or Event-Related Potential Detection in Multichannel EEG Data.

Evoked or Event-Related Potential (EP/ERP) detection is a major area of interest within the domain of EEG (electroencephalography) signal processing. While traditional methods of EEG processing have mostly focused on enhancing signal components, few have explored background noise suppression techniques. Optimizing the suppression of background noise can play a critical role in improving the performance of EP/ERP detection. In this study, a spatio-temporal equalization (STE) method was proposed based on the Multivariate Autoregressive (MVAR) model, which has been shown to suppress the spatio-temporal correlation of background noise and improve the EEG signal detection performance. For practical applications, two optimization schemes based on the spatio-temporal equalization method were designed to solve two common challenges in EEG signal detection: P300 and steady state visual evoked potentials. Our results demonstrated that the STE method effectively improves recognition performance of evoked or event-related potential detection. Additionally, the STE method offers fewer parameters, lower computational complexity, and easier implementation. These attributes allow the STE approach to be extended as a preprocessing method which can be used in combination with existing techniques.