scholarly journals Anti-Heartbeat-Evoked Potentials Performance in Event-Related Potentials-Based Mental Workload Assessment

2021 ◽  
Vol 12 ◽  
Author(s):  
Sangin Park ◽  
Jihyeon Ha ◽  
Laehyun Kim
Keyword(s):  
Evoked Potentials ◽  
Mental Arithmetic ◽  
Mental Workload ◽  
Event Related Potentials ◽  
Classification Performance ◽  
Event Related Potential ◽  
Support Vector ◽  
Arithmetic Task ◽  
Related Potentials ◽  
Mental Arithmetic Task

The aim of this study was to determine the effect of heartbeat-evoked potentials (HEPs) on the performance of an event-related potential (ERP)-based classification of mental workload (MWL). We produced low- and high-MWLs using a mental arithmetic task and measured the ERP response of 14 participants. ERP trials were divided into three conditions based on the effect of HEPs on ERPs: ERPHEP, containing the heartbeat in a period of 280–700ms in ERP epochs after the target; ERPA-HEP, not including the heartbeat within the same period; and ERPT, all trials including ERPA-HEP and ERPHEP. We then compared MWL classification performance using the amplitude and latency of the P600 ERP among the three conditions. The ERPA-HEP condition achieved an accuracy of 100% using a radial basis function-support vector machine (with 10-fold cross-validation), showing an increase of 14.3 and 28.6% in accuracy compared to ERPT (85.7%) and ERPHEP (71.4%), respectively. The results suggest that evoked potentials caused by heartbeat overlapped or interfered with the ERPs and weakened the ERP response to stimuli. This study reveals the effect of the evoked potentials induced by heartbeats on the performance of the MWL classification based on ERPs.

Estimation of Cognitive Distraction from Driver Gazing

2017 ◽  
Vol 9 (2) ◽  
pp. 50-60 ◽  
Author(s):  
Akira Yoshizama ◽  
Hiroyuki Nishiyama ◽  
Hirotoshi Iwasaki ◽  
Fumio Mizoguchi
Keyword(s):  
Qualitative Data ◽  
Mental Arithmetic ◽  
Support Vector ◽  
Simulation Environment ◽  
Time Intervals ◽  
Arithmetic Task ◽  
Task Load ◽  
Cognitive Distraction ◽  
Mental Arithmetic Task ◽  
Using Data

In their study, the authors sought to generate rules for cognitive distractions of car drivers using data from a driving simulation environment. They collected drivers' eye-movement and driving data from 18 research participants using a simulator. Each driver drove the same 15-minute course two times. The first drive was normal driving (no-load driving), and the second drive was driving with a mental arithmetic task (load driving), which the authors defined as cognitive-distraction driving. To generate rules of distraction driving using a machine-learning tool, they transformed the data at constant time intervals to generate qualitative data for learning. Finally, the authors generated rules using a Support Vector Machine (SVM).

MATHEMATICAL MODEL OF BRAIN-COMPUTER INTERFACE BASED ON THE ANALYSIS OF P300 EVENT RELATED POTENTIALS

2021 ◽  
pp. 48-67
Author(s):  
Sergey A. Obukhov ◽  
◽  
Valery P. Stepanov ◽  
Igor V. Rudakov ◽  
◽  
...  
Keyword(s):  
Mathematical Model ◽  
Evoked Potentials ◽  
Event Related Potentials ◽  
Support Vector ◽  
Human Machine Interaction ◽  
Vector Method ◽  
External Stimulation ◽  
Related Potentials ◽  
Eeg Recordings ◽  
Machine Interaction

The evoked potentials (EP) method consists in recording bioelectric reactions of the brain in response to external stimulation or while performing cognitive tasks. The goal of the work is to develop a mathematical model of the system for detection and classification of evoked potentials on the electroencephalogram (EEG). The main odd of the machine EP detection are artifacts from EEG recordings and the high variability of potentials. EP detection and classification algorithm includes three stages. At the preliminary stage, the frequency-time and spatial signal transformations – a set of Butterworth frequency filters, linear composition and averaging of the recorded signals from different sensors are used to remove noise and uninformative EEG components. The next step is the direct fixation and averaging of the evoked potentials. At the final stage, to reduce the dimension of the problem, the information features vector is formed. The parameterized image is used as input of the binary classifier. The support vector method is used to construct the classifier. During the study, the optimization of the regularization C parameter of the classifier was carried out using the estimation of sliding control. The proposed solution is useful for human-machine interaction and for medical procedures with biofeedback.

scholarly journals Improving Covariance Matrices Derived from Tiny Training Datasets for the Classification of Event-Related Potentials with Linear Discriminant Analysis

2020 ◽  
Author(s):  
Jan Sosulski ◽  
Jan-Philipp Kemmer ◽  
Michael Tangermann
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Covariance Matrix ◽  
Event Related Potentials ◽  
Classification Performance ◽  
Event Related Potential ◽  
Feature Representation ◽  
Training Data ◽  
Linear Discriminant ◽  
Related Potentials

AbstractElectroencephalogram data used in the domain of brain–computer interfaces typically has subpar signal-to-noise ratio and data acquisition is expensive. An effective and commonly used classifier to discriminate event-related potentials is the linear discriminant analysis which, however, requires an estimate of the feature distribution. While this information is provided by the feature covariance matrix its large number of free parameters calls for regularization approaches like Ledoit–Wolf shrinkage. Assuming that the noise of event-related potential recordings is not time-locked, we propose to decouple the time component from the covariance matrix of event-related potential data in order to further improve the estimates of the covariance matrix for linear discriminant analysis. We compare three regularized variants thereof and a feature representation based on Riemannian geometry against our proposed novel linear discriminant analysis with time-decoupled covariance estimates. Extensive evaluations on 14 electroencephalogram datasets reveal, that the novel approach increases the classification performance by up to four percentage points for small training datasets, and gracefully converges to the performance of standard shrinkage-regularized LDA for large training datasets. Given these results, practitioners in this field should consider using our proposed time-decoupled covariance estimation when they apply linear discriminant analysis to classify event-related potentials, especially when few training data points are available.

scholarly journals Decision Fusion of EEG and fNIRS Signals

2019 ◽  
Author(s):  
Fares Al-Shargie
Keyword(s):  
Mental Stress ◽  
Near Infrared ◽  
Time Pressure ◽  
Functional Neuroimaging ◽  
Mental Arithmetic ◽  
Decision Fusion ◽  
Support Vector ◽  
Functional Near Infrared Spectroscopy ◽  
Arithmetic Task ◽  
Mental Arithmetic Task

In this study, we investigated the use of multimodal functional neuroimaging in detecting mental stress on the prefrontal cortex (PFC). We recorded Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS) simultaneously from 20-subjects performing mental arithmetic task under control and stress conditions. Stress was induced in this study based on two established stressors – time pressure and negative feedback about peer performance. We explored decision fusion by using support vector machine classifier for each modality, and optimizing the classifiers based on Receiver Operating Characteristic (ROC) curve values. Experiment results revealed significant reduction in alpha rhythm and mean change in concentration of oxygenated hemoglobin at PFC when stressed, p<0.001 and 0.0001 respectively. The decision fusion improved significantly the detection rate of mental stress by +7.76% and +10.57%, when compared to sole modality of EEG and fNIRS, respectively.

scholarly journals The Decision Decoding ToolBOX (DDTBOX) – A multivariate pattern analysis toolbox for event-related potentials

2017 ◽  
Author(s):  
Stefan Bode ◽  
Daniel Feuerriegel ◽  
Daniel Bennett ◽  
Phillip M. Alday
Keyword(s):  
Open Source ◽  
Pattern Analysis ◽  
Event Related Potentials ◽  
Event Related Potential ◽  
Statistical Testing ◽  
Support Vector ◽  
Single Subject ◽  
Multivariate Pattern Analysis ◽  
Related Potentials ◽  
Multivariate Pattern

AbstractIn recent years, neuroimaging research in cognitive neuroscience has increasingly used multivariate pattern analysis (MVPA) to investigate higher cognitive functions. Here we present DDTBOX, an open-source MVPA toolbox for electroencephalography (EEG) data. DDTBOX runs under MATLAB and is well integrated with the EEGLAB/ERPLAB and Fieldtrip toolboxes (Delorme and Makeig, 2004; Lopez-Calderon and Luck, 2014; Oostenveld et al. 2011). It trains support vector machines (SVMs) on patterns of event-related potential (ERP) amplitude data, following or preceding an event of interest, for classification or regression of experimental variables. These amplitude patterns can be extracted across space/electrodes (spatial decoding), time (temporal decoding), or both (spatiotemporal decoding). DDTBOX can also extract SVM feature weights, generate empirical chance distributions based on shuffled-labels decoding for group-level statistical testing, provide estimates of the prevalence of decodable information in the population, and perform a variety of corrections for multiple comparisons. It also includes plotting functions for single subject and group results. DDTBOX complements conventional analyses of ERP components, as subtle multivariate patterns can be detected that would be overlooked in standard analyses. It further allows for a more explorative search for information when no ERP component is known to be specifically linked to a cognitive process of interest. In summary, DDTBOX is an easy-to-use and open-source toolbox that allows for characterising the time-course of information related to various perceptual and cognitive processes. It can be applied to data from a large number of experimental paradigms and could therefore be a valuable tool for the neuroimaging community.

scholarly journals Mental Workload Estimation Using Wireless EEG Signals

2019 ◽  
Author(s):  
Quadri Adewale ◽  
George Panoutsos
Keyword(s):  
Real Time ◽  
Feature Matching ◽  
Mental Arithmetic ◽  
Mental Workload ◽  
Time Estimation ◽  
Arithmetic Task ◽  
Mental Arithmetic Task ◽  
Real Time Estimation ◽  
Wireless Eeg ◽  
Back Task

AbstractPrevious studies have shown that electroencephalogram (EEG) can be used in estimating mental workload. However, developing fast and reliable models for cross-task, cross-subject and cross-session classifications of workload remains a challenge. In this study, a wireless Emotiv EPOC headset was used to evaluate workload in two different mental tasks: n-back task and mental arithmetic task. 0-back task and 2-back task were employed as low and high workload in the n-back task while 1-digit and 3-digit addition were used as the two different workload levels in the arithmetic task. Using power spectral density as features, a fast signal processing and feature extraction framework was developed to facilitate real-time estimation of workload. Within-session accuracies of 98.5% and 95.5% were achieved in the n-back and arithmetic tasks respectively. Adaptive subspace feature matching (ASFM) was applied for cross-session, cross-task and cross-subject classifications. The feature adaptation provided average cross-session accuracies of 80.5% and 74.4% in the n-back and the arithmetic tasks respectively. An average cross-task accuracy of 68.6% was achieved while cross-subject accuracies were 74.4% and 64.1% in the n-back and arithmetic tasks respectively. The framework generalised well across subjects and tasks, and it provided a promising approach towards developing subject and task-independent models. This study also shows that a consumer-level wireless EEG headset can be applied in cognitive monitoring for real-time estimation of workload in practice.

Neural dynamics of trainee teachers' emotional conflict processing: An event-related potential study

2019 ◽  
Vol 47 (7) ◽  
pp. 1-9
Author(s):  
Li Jin ◽  
Xu Li ◽  
Jiamei Lu ◽  
Nianqu Chen ◽  
Lin Cheng ◽  
...  
Keyword(s):  
School Context ◽  
Event Related Potentials ◽  
Event Related Potential ◽  
The Body ◽  
Educational Context ◽  
Congruent Condition ◽  
Slow Potential ◽  
Emotional Conflict ◽  
Trainee Teachers ◽  
Related Potentials

We investigated emotional conflict in an educational context with an emotional body–word Stroop paradigm, examining whether the N450 (a late fronto-central phasic negative event-related potential signature) and slow potential (SP) effects could be evoked in trainee teachers. The N450 effect is characterized by topography and negative polarity of an incongruent minus congruent difference potential, and the SP effect has positive polarity (incongruent minus congruent difference potential). Positive and negative body language examples were obtained from pupils in an actual school context, and emotional words were selected. Compound stimuli were presented, each comprising a congruent or incongruent word displayed across a body image. Event-related potentials were recorded while participants judged body expression valence. Reaction times were longer and accuracies were lower for the incongruent compared to the congruent condition. The N450 component amplitude in the incongruent condition was more negative than in the congruent condition. Results showed a behavioral interference effect and an N450 effect for trainee teachers in this context, thus indicating that the body–word task was efficient in assessing emotional conflict in an educational context, and trainee teachers' perception of body expressions of students could be influenced by emotional signals. The findings further the understanding of emotional conflict in an educational context.

scholarly journals Eliciting and Recording Event Related Potentials (ERPs) in Behaviourally Unresponsive Populations: A Retrospective Commentary on Critical Factors

2021 ◽  
Vol 11 (7) ◽  
pp. 835
Author(s):  
Alexander Rokos ◽  
Richard Mah ◽  
Rober Boshra ◽  
Amabilis Harrison ◽  
Tsee Leng Choy ◽  
...  
Keyword(s):  
Event Related Potentials ◽  
Stimulus Presentation ◽  
Event Related Potential ◽  
Critical Factors ◽  
Healthy Elderly ◽  
States Of Consciousness ◽  
Long Latency ◽  
Eeg Data ◽  
Related Potentials ◽  
The Relationship

A consistent limitation when designing event-related potential paradigms and interpreting results is a lack of consideration of the multivariate factors that affect their elicitation and detection in behaviorally unresponsive individuals. This paper provides a retrospective commentary on three factors that influence the presence and morphology of long-latency event-related potentials—the P3b and N400. We analyze event-related potentials derived from electroencephalographic (EEG) data collected from small groups of healthy youth and healthy elderly to illustrate the effect of paradigm strength and subject age; we analyze ERPs collected from an individual with severe traumatic brain injury to illustrate the effect of stimulus presentation speed. Based on these critical factors, we support that: (1) the strongest paradigms should be used to elicit event-related potentials in unresponsive populations; (2) interpretation of event-related potential results should account for participant age; and (3) speed of stimulus presentation should be slower in unresponsive individuals. The application of these practices when eliciting and recording event-related potentials in unresponsive individuals will help to minimize result interpretation ambiguity, increase confidence in conclusions, and advance the understanding of the relationship between long-latency event-related potentials and states of consciousness.

scholarly journals Preparatory Experiments Regarding Human Brain Perception and Reasoning of Image Complexity for Synthetic Color Fractal and Natural Texture Images via EEG

2020 ◽  
Vol 11 (1) ◽  
pp. 164
Author(s):  
Irina E. Nicolae ◽  
Mihai Ivanovici
Keyword(s):  
Human Perception ◽  
Event Related Potentials ◽  
Classification Performance ◽  
Subjective Perception ◽  
Image Perception ◽  
Image Complexity ◽  
Temporal Area ◽  
Practical Applications ◽  
Related Potentials ◽  
Texture Complexity

Texture plays an important role in computer vision in expressing the characteristics of a surface. Texture complexity evaluation is important for relying not only on the mathematical properties of the digital image, but also on human perception. Human subjective perception verbally expressed is relative in time, since it can be influenced by a variety of internal or external factors, such as: Mood, tiredness, stress, noise surroundings, and so on, while closely capturing the thought processes would be more straightforward to human reasoning and perception. With the long-term goal of designing more reliable measures of perception which relate to the internal human neural processes taking place when an image is perceived, we firstly performed an electroencephalography experiment with eight healthy participants during color textural perception of natural and fractal images followed by reasoning on their complexity degree, against single color reference images. Aiming at more practical applications for easy use, we tested this entire setting with a WiFi 6 channels electroencephalography (EEG) system. The EEG responses are investigated in the temporal, spectral and spatial domains in order to assess human texture complexity perception, in comparison with both textural types. As an objective reference, the properties of the color textural images are expressed by two common image complexity metrics: Color entropy and color fractal dimension. We observed in the temporal domain, higher Event Related Potentials (ERPs) for fractal image perception, followed by the natural and one color images perception. We report good discriminations between perceptions in the parietal area over time and differences in the temporal area regarding the frequency domain, having good classification performance.

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