Event-related Potential Signals Research Articles

Neural signatures of visual awareness independent of postperceptual processing.

What are the neural processes associated with perceptual awareness that are distinct from preconscious sensory encoding and postperceptual processes such as reporting an experience? Using electroencephalography and a no-report visual masking paradigm, we manipulated stimulus visibility by varying the time between stimuli and masks in linear steps (17, 33, 50, 67, and 83ms). Awareness increased nonlinearly, with stimuli never seen at the two shortest intervals, always seen at the two longest, and 50% seen at the intermediate interval. Separate report and no-report conditions were used to isolate awareness from task performance. Our results revealed a neural signal closely linked to perceptual awareness, independent of the task: a fronto-central event-related potential that we refer to as the N2 (~250 to 300ms). Earlier event-related potential signals reflected the linear manipulation of stimulus strength, while later signals like P3b and temporal generalization of decoding were tied to task performance, appearing only in the report condition. Taken together, these findings inform current debates regarding theories of consciousness and offer new avenues for exploring the neural mechanisms supporting conscious processing.

MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces.

Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex spatiotemporal feature information is governed by ongoing oscillatory activity within the brain. Deep learning methods have been increasingly adopted for ERP-based brain-computer interfaces (BCIs) due to their excellent feature representation abilities, which allow for deep analysis of oscillatory activity within the brain. Features with higher spatiotemporal frequencies usually represent detailed and localized information, while features with lower spatiotemporal frequencies usually represent global structures. Mining EEG features from multiple spatiotemporal frequencies is conducive to obtaining more discriminative information. A multiscale feature fusion octave convolution neural network (MOCNN) is proposed in this article. MOCNN divides the ERP signals into high-, medium- and low-frequency components corresponding to different resolutions and processes them in different branches. By adding mid- and low-frequency components, the feature information used by MOCNN can be enriched, and the required amount of calculations can be reduced. After successive feature mapping using temporal and spatial convolutions, MOCNN realizes interactive learning among different components through the exchange of feature information among branches. Classification is accomplished by feeding the fused deep spatiotemporal features from various components into a fully connected layer. The results, obtained on two public datasets and a self-collected ERP dataset, show that MOCNN can achieve state-of-the-art ERP classification performance. In this study, the generalized concept of octave convolution is introduced into the field of ERP-BCI research, which allows effective spatiotemporal features to be extracted from multiscale networks through branch width optimization and information interaction at various scales.

Attenuated conflict self-referential information facilitating conflict resolution

Self-referential information can reduce the congruency effect by acting as a signal to enhance cognitive control. However, it cannot be denied that self-referential information can attract and hold attention. To investigate this issue, the study used a revised Stroop task and recorded behavioral and electrophysiological data from thirty-three participants. We combined event-related potential (ERP) and multivariate pattern analysis (MVPA) to examine the neural correlates of self-referential processing and conflict processing. In the behavioral results, self-referential information reduced the congruency effect. Specifically, self-reference stimuli elicited smaller N2 amplitude than non-self-reference stimuli, indicating that self-referential information was promptly identified and reduced top-down cognitive resource consumption. Self-referential information could be reliably decoded from ERP signals in the early-to-mid stage. Moreover, self-reference conditions exhibited earlier congruency decoding than non-self-reference conditions, facilitating conflict monitoring. In the late stage, under the incongruent condition, self-reference stimuli elicited smaller sustained potential amplitude than non-self-reference stimuli, indicating that cognitive control in the self-reference condition required fewer cognitive resources for conflict resolution. Together, these findings revealed that self-referential information was identified and facilitated conflict monitoring, leading to more effective conflict resolution.

Unsupervised separation of nonlinearly mixed event-related potentials using manifold clustering and non-negative matrix factorization

Event-related potentials (ERPs) can quantify brain responses to reveal the neural mechanisms of sensory perception. However, ERPs often reflect nonlinear mixture responses to multiple sources of sensory stimuli, and an accurate separation of the response to each stimulus remains a challenge. This study aimed to separate the ERP into nonlinearly mixed source components specific to individual stimuli. We developed an unsupervised learning method based on clustering of manifold structures of mixture signals combined with channel optimization for signal source reconstruction using non-negative matrix factorization (NMF). Specifically, we first implemented manifold learning based on Local Tangent Space Alignment (LTSA) to extract the spatial manifold structure of multi-resolution sub-signals separated via wavelet packet transform. We then used fuzzy entropy to extract the dynamical process of the manifold structures and performed a k-means clustering to separate different sources. Lastly, we used NMF to obtain the optimal contributions of multiple channels to ensure accurate source reconstructions. We evaluated our developed approach using a simulated ERP dataset with known ground truth of two components of ERP mixture signals. Our results show that the correlation coefficient between the reconstructed source signal and the true source signal was 92.8 % and that the separation accuracy in ERP amplitude was 91.6 %. The results show that our unsupervised separation approach can accurately separate ERP signals from nonlinear mixture source components. The outcomes provide a promising way to isolate brain responses to multiple stimulus sources during multisensory perception.

Signal alignment for cross-datasets in P300 brain-computer interfaces

Objective. Transfer learning has become an important issue in the brain-computer interface (BCI) field, and studies on subject-to-subject transfer within the same dataset have been performed. However, few studies have been performed on dataset-to-dataset transfer, including paradigm-to-paradigm transfer. In this study, we propose a signal alignment (SA) for P300 event-related potential (ERP) signals that is intuitive, simple, computationally less expensive, and can be used for cross-dataset transfer learning. Approach. We proposed a linear SA that uses the P300’s latency, amplitude scale, and reverse factor to transform signals. For evaluation, four datasets were introduced (two from conventional P300 Speller BCIs, one from a P300 Speller with face stimuli, and the last from a standard auditory oddball paradigm). Results. Although the standard approach without SA had an average precision (AP) score of 25.5%, the approach demonstrated a 35.8% AP score, and we observed that the number of subjects showing improvement was 36.0% on average. Particularly, we confirmed that the Speller dataset with face stimuli was more comparable with other datasets. Significance. We proposed a simple and intuitive way to align ERP signals that uses the characteristics of ERP signals. The results demonstrated the feasibility of cross-dataset transfer learning even between datasets with different paradigms.

The impact of risk preference on decision-making during evacuation: Behavioral and physiological analysis using event-related potential signals

Individuals with different cognitive and personality traits may be required to serve as evacuees during a fire or other emergencies. We investigated how the personality trait of risk preference influences the decision-making strategy of fire evacuees, and classified evacuees by different risk preferences to reveal behavioral and neurophysiological differences associated with different preference categories. Event-related potentials and reaction times were recorded as 49 healthy participants performed an emergency decision-making task that required them to choose between two escape routes by considering route distance and fire severity. Our simulated evacuation experiment revealed the participants’ risk-taking preferences during emergency evacuation. All participants were classified into three statistically different groups (risk-averse group; risk-neutral group; risk-seeking group). We found that evacuees determined their risk preference early and preferred to maintain their original decision-making strategy rather than change it frequently. Our findings indicate that, when participants selected an option against their risk preference, they demonstrated longer reaction time and used more cognitive and attentional resources. This difference is shown by the significant difference in reaction times, P300 amplitudes, and activity on topographic scalp maps. Our study is the first to identify physiological changes that correspond with different risk preferences during evacuation decision-making and explain more fully how evacuees make emergency decisions in the context of fire. Future research should examine these findings in a larger sample size.

Sensory Attenuation Deficit and Auditory Hallucinations in Schizophrenia: A Causal Mechanism or a Risk Factor? Evidence From Meta-Analyses on the N1 Event-Related Potential Component

BackgroundSensory attenuation (SA), the dampened perception of self-generated sensory information, is typically associated with reduced event-related potential signals, such as for the N1 component of auditory event-related potentials. SA, together with efficient monitoring of intentions and actions, should facilitate the distinction between self-generated and externally generated sensory events, thereby optimizing interaction with the world. According to many, SA is deficient in schizophrenia. The question arises whether altered SA reflects a sufficient mechanism to explain positive symptoms such as auditory hallucinations. A systematic association of reduced auditory SA in hallucinating patients would support this hypothesis. MethodsWe conducted a series of meta-analyses on 15 studies on auditory SA in which the N1 component of event-related potential–electroencephalogram signals was measured during talking (self-generated sensory signals condition) or when listening to prerecorded vocalizations (externally generated sensory signals condition). ResultsWe found that individuals with schizophrenia did show some auditory SA because their N1 signal was significantly attenuated in talking conditions compared with listening conditions. However, the magnitude of such attenuation was reduced in individuals with schizophrenia compared to healthy control participants. This phenomenon generalizes independently from the stage of the disease, the severity of positive symptoms, and whether patients have auditory hallucinations or not. ConclusionsThese findings suggest that reduced SA cannot be a sufficient mechanism for explaining positive symptoms such as auditory hallucinations in schizophrenia. Because reduced SA was also present in participants at risk of schizophrenia, reduced SA may represent a risk factor for the disorder. We discuss the implications of these results for clinical-cognitive models of schizophrenia.

Conservative method for vertical electrooculogram attenuation based on local suppression of ongoing EEG artifact templates.

Eye movement during blinking can be a significant artifact in Event-Related Potentials (ERP) analysis. Blinks produce a positive potential in the vertical electrooculogram (VEOG), spreading towards the posterior direction. Two methods are frequently used to suppress VEOGs: linear regression to subtract the VEOG signal from the electroencephalogram (EEG) and Independent Component Analysis (ICA). However, some information is lost in both. The present algorithm (1) statistically identifies the position of VEOGs in the frontopolar channels; (2) performs EEG averaging for each channel, which results in 'blink templates'; (3) subtracts each template from the respective EEG at each VEOG position, only when the linear correlation index between the template and the segment is greater than a chosen threshold L. The signals from twenty subjects were acquired using a behavioral test and were treated using FilterBlink for subsequent ERP analysis. A model was designed to test the method for each subject using twenty copies of the EEG signal from the subject's mid-central channel (with nearly no VEOG) representing the EEG channels and their respective blink templates. At the same 200 equidistant time points (marks), a signal (2.5 sinusoidal cycles at 1050 ms emulating an ERP) was mixed with each model channel and the respective blink template of that channel, between 500 to 1200 ms after each mark. According to the model, VEOGs interfered with both ERPs and the ongoing EEG, mainly on the anterior medial leads, and no significant effect was observed on the mid-central channel (Cz). FilterBlink recovered approximately 90% (Fp1) to 98% (Fz) of the original ERP and EEG signals for L = 0.1. The method reduced the VEOG effect on the EEG after ERP and blink-artifact averaging in analyzing real signals. The method is straightforward and effective for VEOG attenuation without significant distortion in the EEG signal and embedded ERPs.

Connectivity changes in two-channel prefrontal ERP associated with early cognitive decline in the elderly population: beta band responses to the auditory oddball stimuli.

This study utilized recent advancements in electroencephalography (EEG) technology that enable the measurement of prefrontal event-related potentials (ERPs) to facilitate the early detection of mild cognitive impairment (MCI). We investigated two-channel prefrontal ERP signals obtained from a large cohort of elderly participants and compare among cognitively normal (CN), subjective cognitive decline (SCD), amnestic MCI (aMCI), and nonamnestic MCI (naMCI) groups. Signal processing and ERP component analyses, specifically adapted for two-channel prefrontal ERP signals evoked by the auditory oddball task, were performed on a total of 1,754 elderly participants. Connectivity analyses were conducted to assess brain synchronization, especially in the beta band involving the phase locking value (PLV) and coherence (COH). Time-frequency, time-trial, grand average, and further statistical analyses of the standard and target epochs were also conducted to explore differences among the cognition groups. The MCI group's response to target stimuli was characterized by greater response time variability (p < 0.001) and greater variability in the P300 latency (p < 0.05), leading to less consistent responses than those of the healthy control (HC) group (CN+SCD subgroups). In the connectivity analyses of PLV and COH waveforms, significant differences were observed, indicating a loss of synchronization in the beta band in response to standard stimuli in the MCI group. In addition, the absence of event-related desynchronization (ERD) indicated that information processing related to readiness and task performance in the beta band was not efficient in the MCI group. Furthermore, the observed decline in the P200 amplitude as the standard trials progressed suggests the impaired attention and inhibitory processes in the MCI group compared to the HC group. The aMCI subgroup showed high variability in COH values, while the naMCI subgroup showed impairments in their overall behavioral performance. These findings highlight the variability and connectivity measures can be used as markers of early cognitive decline; such measures can be assessed with simple and fast two-channel prefrontal ERP signals evoked by both standard and target stimuli. Our study provides deeper insight of cognitive impairment and the potential use of the prefrontal ERP connectivity measures to assess early cognitive decline.

Opinion on the event-related potential signature of automatic detection of violated regularity (visual mismatch negativity): non-perceptual but predictive.

Opinion on the event-related potential signature of automatic detection of violated regularity (visual mismatch negativity): non-perceptual but predictive.

Multilevel State-Space Models Enable High Precision Event Related Potential Analysis.

During cognitive tasks, the elicited brain responses that are time-locked to the stimulus presentation are manifested in electroencephalogram (EEG) as Event Related Potentials (ERPs). In general, ERPs are signals embedded in the background of much stronger neural oscillations, and thus they are traditionally extracted by averaging hundreds of trial responses so that the neural oscillations can cancel out each other. However, often in cognitive science experiments, it is difficult to administer large number of trials due to physical constraints. Additionally, these excessive averaging can also blur fine structures of the ERPs signals, which might otherwise be indicative of various intrinsic factors. Here we propose to model the background oscillations using a novel oscillation state-space representation and identify their time-traces in a data-driven way. This allows us to effectively separate the oscillations from the response signals of interest, thus improving the signal-to-noise of the evoked response, and eventually increasing trial fidelity. We also consider a random-walk like continuity constraint for the ERP waveforms to recover smooth, de-noised estimates. We employ a generalized expectation maximization algorithm for estimating the model parameters, and then infer the approximate posterior distribution of ERP waveforms. We demonstrate the reduced reliance of our proposed ERP extraction technique via a simulation study. Finally, we showcase how the extracted ERPs using our method can be more informative than the traditional average-based ERPs when analyzing EEG data in cognitive task settings with fewer trials.

Event-related potentials reveal visual episodic memory deficits in patients with temporal lobe epilepsy

ObjectiveTemporal lobe epilepsy (TLE) patients usually suffer from impaired episodic memory (EM), but its underlying electrophysiologic mechanism and impacted cognitive performance are unclear. We aim to investigate the association between episodic memory reserve and physiological measures of memory workload in TLE patients using Event-related potentials (ERP). MethodsA change detection task with image stimuli assesses visual episodic memory. During the memory encoding and decoding phases, the ERP signals were analyzed from twenty-nine TLE patients (twelve with left TLE patients, seventeen with TLE), and thirty healthy controls. Given that EM is a complex process involving many fundamental cognitive processes, the amplitudes and latencies of EM-related ERP (FN400, late positive potential (LPC), and late posterior negativity (LPN)), and the ERP reflecting the fundamental processes (P100, N100, P200, and P300) were calculated. Then we used a three-by-two factorial design on the ERP metrics for interaction and main effects. The correlation analysis among Wechsler Memory Scales-Chinese Revision (WMS-RC) results, behavioral data, and the ERPs was carried out. ResultsThe TLE patients performed worse in WMS-RC and the memory task. The increased P200 and decreased P300 amplitudes were observed in the TLE patients, and LPN was abnormal in only LTLE patients. For EM-related components, differences were observed in both the LTLE and RTLE patients: the lack of the FN400 effect, the lack of the reversed LPC effect, and the reduced FN400. No significant inter-group difference was detected for the latencies of all the ERPs. Additionally, there were significant correlations among WMS-RC scores, behaviors, and some ERP amplitudes. ConclusionsThe impaired EM is linked to the increased P200 and decreased P300 amplitudes. LPN seems to be sensitive to left temporal lobe dysfunction. More importantly, the abnormal old or new effects of the FN400 and LPC, and the reduced FN400 amplitude might be associated with the visual EM deficit in the TLE patients. These findings may assist in the deep understanding of the EM disorder and the evaluation of the side effects of antiepileptic drugs.

Low-rank Tensor Restoration for ERP extraction

Event-related potential (ERP) data is essentially multi-dimensional, with correlated data in some spaces. Therefore, matrix and vector analysis results in structural information loss. Tensor decomposition can be used to explore the shared structural information of ERP signal among related conditions. Perceiving the decaying trends of the singular value changes of unfolding matrices indicate that they are low-rank matrices. Based on this assumption, in this work, a low-rank tensor restoration (LTR) method is proposed. An operator splitting method known as the alternating direction method of multipliers (ADMM) is adapted to tackle the proposed optimization problem with orthogonality and sparsity constraints. Accordingly, the problem is solved in a sequential fashion by computing the unconstrained and orthogonality constrained quadratic sub-problems with closed-form solutions. The algorithm is examined under three application areas, namely, noise removal, feature extraction and subject-to-subject transfer learning. The empirical evaluations on real P300-based ERP dataset demonstrate the robustness and effectiveness of the proposed method.

Analysis and Classification of Event-Related Potentials During Image Observation.

In the field of cognitive neuroscience, researchers have conducted extensive studies on object categorization using Event-Related Potential (ERP) analysis, specifically by analyzing electroencephalographic (EEG) response signals triggered by visual stimuli. The most common approach for visual ERP analysis is to use a low presentation rate of images and an active task where participants actively discriminate between target and non-target images. However, researchers are also interested in understanding how the human brain processes visual information in real-world scenarios. To simulate real-life object recognition, this study proposes an analysis pipeline of visual ERPs evoked by images presented in a Rapid Serial Visual Presentation (RSVP) paradigm. Such an approach allows for the investigation of recurrent patterns of visual ERP signals across specific categories and subjects. The pipeline includes segmentation of the EEGs in epochs, and the use of the resulting features as inputs for Support Vector Machine (SVM) classification. Results demonstrate common ERP patterns across the selected categories and the ability to obtain discriminant information from single visual stimuli presented in the RSVP paradigm.

Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques

BackgroundPresentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task.MethodsEEG single trials are decomposed with discrete wavelet transform (DWT) up to the 4{th} level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects.ResultsThe proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60pm {6.5}, sensitivities 93.55pm {4.5}, specificities 94.85pm {4.2}, precisions 92.50pm {5.5}, and area under the curve (AUC) 0.93pm {0.3} using SVM and k-NN machine learning classifiers.ConclusionThe proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.

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

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

Multi-channel EEG signals classification via CNN and multi-head self-attention on evidence theory

Electroencephalography (EEG) provides valuable physiological information to identify human activities. However, it can be difficult to analyze the EEG data in human patterns identification, because both subjective and objective factors can easily affect sensitivity. In this study, a novel multi-head self-attention convolutional neural networks (CNN) framework based on Dempster-Shafer (D-S) evidence theory, called ETNN, is proposed to classify the EEG signal. The ETNN model considers the multi-type networks and fuses multi-output with D-S evidence theory, which can handle the EEG data more reasonably. In particular, a classification algorithm for EEG signals is derived with information fusion. Finally, an application for event-related potential signal classification and sensitivity analysis is used to demonstrate the effectiveness of the proposed ETNN model compared with existing classification techniques.

Does MOED Rhyme with FRUIT? An event-related potential study of cross-language rhyming.

The study aimed to characterize the event-related potentials signature elicited by visual rhyme judgements across two alphabetic orthographies that differ in depth (shallow: Dutch; deep: English) and by spelling-sound consistency in a deep L2-English orthography. Twenty-four Dutch-English bilinguals who varied on measures of L2-English proficiency, made rhyme judgments of semantically unrelated Dutch-English word pairs presented sequentially in the visual modality, while behavioral and electrophysiological responses were recorded. The spelling-sound consistency of target words was varied systematically. Nonrhyming targets elicited a larger N450 amplitude than rhyming targets, indicating sensitivity to mismatching phonology across languages. English target words with consistent spelling-sound mappings elicited less negative N250 amplitudes when preceded by rhyming Dutch primes. Overall, event-related potentials revealed robust responses to phonological mismatch, but subtle responses to spelling-sound inconsistency in L2-English. Results suggest that bilingual readers of a shallow L1 orthography who are immersed in an L1-speaking environment may not tune into the degree of spelling-sound consistency of a deep L2 orthography.

Источники и значимость вариативности потенциалов мозга человека в интерфейсе мозг–компьютер

In the brain-computer interface based on the P300 wave (P300 BCI), the selection of the command by the user becomes possible due to focusing the user's attention on the external stimulus/command and extraction of the response to this stimulus in the form of the event-related potential (ERP) components from EEG. To obtain the ERP signal, stimuli should be repeated many times, however, in view of the existing variability in latency of the response to certain stimuli, the averaged ERPs may give a distorted view of the nature of such responses and reduce accuracy of the interface. The study was aimed to develop an effective method for identification of the effects of the ERP components' latency variability and for accounting these effects in the P300 BCI, as well as to identify the possible impact of psychophysiological factors on the nature of ERP variability. We have conducted a BCI-based study of 19 healthy subjects involving extraction and adjustment of latency in the N1 and P300 spatial components, which play a key role in the command classification in the P300 BCI, to explore the mechanisms underlying variability. Such an approach ensured higher accuracy compared to the use of conventional EEG leads, and the highest increase of 10% was observed when using the minimum number of the stimulus repetitions. Furthermore, modifications of the interface allowing one to ensure a higher level of the user's focus on the task and a more accurate visual fixation on the target objects contributed to the increase in the amplitude of the ERP components by reducing variability of the responses to single stimuli. The findings emphasize the important role of the processes underlying the ERP components' variability and provide an effective tool for scientific exploration of such processes and the development of advanced BCI systems.

Tactile perception of textile fabrics using event-related potentials and nonlinear methods

Tactile perception is processed in the somatosensory cortices of the brain. To study the electrical activity of the brain evoked by the tactile perceptions of fabric textures, electroencephalograph signals during tactile perception were evaluated using event-related potentials and recurrence quantification analysis methods. A support vector machine was used to realize the identification of electroencephalograph signals. The results showed that fabric surface with large surface roughness, critical buckling force, and fiber diameter, and low warp-weft density was preferentially perceived, need large attentional resources, and evoke strong nonstationary state of the electroencephalographic system during the tactile perception. The δ rhythm was the main rhythm of event-related potential signals evoked by tactile perception of fabrics and the main energy of the electroencephalograph rhythm was concentrated in the low frequency band. The electroencephalographic system was in a strong nonstationary and fluctuating state during the production stage of P200 and P300 components of the event-related potential wave. The combination of P200, P300, mean diagonal line length L, and trapping time TT can significantly improve the classification accuracy of the electroencephalograph signal of tactile perception to about 72%.