scholarly journals Classifying EEG Signals during Stereoscopic Visualization to Estimate Visual Comfort

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Jérémy Frey ◽  
Aurélien Appriou ◽  
Fabien Lotte ◽  
Martin Hachet
Keyword(s):  
Adaptive Systems ◽  
Event Related Potentials ◽  
Stereoscopic Vision ◽  
Computer Interface ◽  
Visual Comfort ◽  
Eeg Signals ◽  
Stereoscopic Displays ◽  
Related Potentials ◽  
Left And Right ◽  
Stereoscopic Visualization

With stereoscopic displays a sensation of depth that is too strong could impede visual comfort and may result in fatigue or pain. We used Electroencephalography (EEG) to develop a novel brain-computer interface that monitors users’ states in order to reduce visual strain. We present the first system that discriminates comfortable conditions from uncomfortable ones during stereoscopic vision using EEG. In particular, we show that either changes in event-related potentials’ (ERPs) amplitudes or changes in EEG oscillations power following stereoscopic objects presentation can be used to estimate visual comfort. Our system reacts within 1 s to depth variations, achieving 63% accuracy on average (up to 76%) and 74% on average when 7 consecutive variations are measured (up to 93%). Performances are stable (≈62.5%) when a simplified signal processing is used to simulate online analyses or when the number of EEG channels is lessened. This study could lead to adaptive systems that automatically suit stereoscopic displays to users and viewing conditions. For example, it could be possible to match the stereoscopic effect with users’ state by modifying the overlap of left and right images according to the classifier output.

scholarly journals Design and Verification of a Dry Sensor-Based Multi-Channel Digital Active Circuit for Human Brain Electroencephalography Signal Acquisition Systems

Micromachines ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 720
Author(s):  
Chin-Teng Lin ◽  
Chi-Hsien Liu ◽  
Po-Sheng Wang ◽  
Jung-Tai King ◽  
Lun-De Liao
Keyword(s):  
Daily Life ◽  
Event Related Potentials ◽  
Computer Interface ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Preparation Time ◽  
Active Circuit ◽  
Related Potentials ◽  
Common Application

A brain–computer interface (BCI) is a type of interface/communication system that can help users interact with their environments. Electroencephalography (EEG) has become the most common application of BCIs and provides a way for disabled individuals to communicate. While wet sensors are the most commonly used sensors for traditional EEG measurements, they require considerable preparation time, including the time needed to prepare the skin and to use the conductive gel. Additionally, the conductive gel dries over time, leading to degraded performance. Furthermore, requiring patients to wear wet sensors to record EEG signals is considered highly inconvenient. Here, we report a wireless 8-channel digital active-circuit EEG signal acquisition system that uses dry sensors. Active-circuit systems for EEG measurement allow people to engage in daily life while using these systems, and the advantages of these systems can be further improved by utilizing dry sensors. Moreover, the use of dry sensors can help both disabled and healthy people enjoy the convenience of BCIs in daily life. To verify the reliability of the proposed system, we designed three experiments in which we evaluated eye blinking and teeth gritting, measured alpha waves, and recorded event-related potentials (ERPs) to compare our developed system with a standard Neuroscan EEG system.

ASSESSING THE IMPACT OF CONGRUENT AND INCONGRUENT STIMULUS IN STROOP TASK, USING EVENT-RELATED POTENTIALS (ERP) IN PATIENTS WITH DEPRESSION

2018 ◽  
Vol 30 (05) ◽  
pp. 1850034
Author(s):  
Yeganeh Shahsavar ◽  
Majid Ghoshuni
Keyword(s):  
Stroop Task ◽  
Time Window ◽  
Event Related Potentials ◽  
Peak Time ◽  
Eeg Signals ◽  
Incongruent Stimulus ◽  
Related Potentials ◽  
Recording Channel ◽  
The Mean ◽  
The Impact

The main goal of this event-related potentials (ERPs) study was to assess the effects of stimulations in Stroop task in brain activities of patients with different degrees of depression. Eighteen patients (10 males, with the mean age [Formula: see text]) were asked to fill out Beck’s depression questionnaire. Electroencephalographic (EEG) signals of subjects were recorded in three channels (Pz, Cz, and Fz) during Stroop test. This test entailed 360 stimulations, which included 120 congruent, 120 incongruent, and 120 neutral stimulations. To analyze the data, 18 time features in each type of stimulus were extracted from the ERP components and the optimal features were selected. The correlation between the subjects’ scores in Beck’s depression questionnaires and the extracted time features in each recording channel was calculated in order to select the best features. Total area, and peak-to-peak time window in the Cz channel in both the congruent and incongruent stimulus showed significant correlation with Beck scores, with [Formula: see text], [Formula: see text] and [Formula: see text], [Formula: see text], respectively. Consequently, given the correlation between time features and the subjects’ Beck scores with different degrees of depression, it can be interpreted that in case of growth in degrees of depression, stimulations involving congruent images would produce more challenging interferences for the patients compared to incongruent stimulations which can be more effective in diagnosing the level of disorder.

Feature selection method based on Menger curvature and LDA theory for a P300 brain-computer interface

2021 ◽  
Author(s):  
ShuRui Li ◽  
Jing Jin ◽  
Ian Daly ◽  
Chang Liu ◽  
Andrzej Cichocki
Keyword(s):  
Feature Selection ◽  
Brain Computer Interface ◽  
Feature Selection Method ◽  
Event Related Potentials ◽  
Selection Method ◽  
Computer Interface ◽  
Feature Subset ◽  
Linear Discriminant ◽  
Related Potentials ◽  
Menger Curvature

Abstract Brain–computer interface (BCI) systems decode electroencephalogram signals to establish a channel for direct interaction between the human brain and the external world without the need for muscle or nerve control. The P300 speller, one of the most widely used BCI applications, presents a selection of characters to the user and performs character recognition by identifying P300 event-related potentials from the EEG. Such P300-based BCI systems can reach good levels of accuracy but are difficult to use in day-to-day life due to redundancy and noisy signal. A room for improvement should be considered. We propose a novel hybrid feature selection method for the P300-based BCI system to address the problem of feature redundancy, which combines the Menger curvature and linear discriminant analysis. First, selected strategies are applied separately to a given dataset to estimate the gain for application to each feature. Then, each generated value set is ranked in descending order and judged by a predefined criterion to be suitable in classification models. The intersection of the two approaches is then evaluated to identify an optimal feature subset. The proposed method is evaluated using three public datasets, i.e., BCI Competition III dataset II, BNCI Horizon dataset, and EPFL dataset. Experimental results indicate that compared with other typical feature selection and classification methods, our proposed method has better or comparable performance. Additionally, our proposed method can achieve the best classification accuracy after all epochs in three datasets. In summary, our proposed method provides a new way to enhance the performance of the P300-based BCI speller.

scholarly journals Towards an Accessible Use of a Brain-Computer Interfaces-Based Home Care System through a Smartphone

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Koun-Tem Sun ◽  
Kai-Lung Hsieh ◽  
Syuan-Rong Syu
Keyword(s):  
Home Care ◽  
Event Related Potentials ◽  
End Users ◽  
Disabled People ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Related Potentials ◽  
The One ◽  
The Brain ◽  
Care System

This study proposes a home care system (HCS) based on a brain-computer interface (BCI) with a smartphone. The HCS provides daily help to motor-disabled people when a caregiver is not present. The aim of the study is two-fold: (1) to develop a BCI-based home care system to help end-users control their household appliances, and (2) to assess whether the architecture of the HCS is easy for motor-disabled people to use. A motion-strip is used to evoke event-related potentials (ERPs) in the brain of the user, and the system immediately processes these potentials to decode the user’s intentions. The system, then, translates these intentions into application commands and sends them via Bluetooth to the user’s smartphone to make an emergency call or to execute the corresponding app to emit an infrared (IR) signal to control a household appliance. Fifteen healthy and seven motor-disabled subjects (including the one with ALS) participated in the experiment. The average online accuracy was 81.8% and 78.1%, respectively. Using component N2P3 to discriminate targets from nontargets can increase the efficiency of the system. Results showed that the system allows end-users to use smartphone apps as long as they are using their brain waves. More important, only one electrode O1 is required to measure EEG signals, giving the system good practical usability. The HCS can, thus, improve the autonomy and self-reliance of its end-users.

scholarly journals SCENE AND OBJECT CLASSIFICATION USING BRAIN WAVES SIGNAL

2017 ◽  
Vol 10 (13) ◽  
pp. 137
Author(s):  
Darshan A Khade ◽  
Ilakiyaselvan N
Keyword(s):  
Artificial Intelligence ◽  
Event Related Potentials ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Eeg Signals ◽  
Future Technology ◽  
Brain Waves ◽  
Brain Signals ◽  
Related Potentials ◽  
Signal Machine

This study aims to classify the scene and object using brain waves signal. The dataset captured by the electroencephalograph (EEG) device by placing the electrodes on scalp to measure brain signals are used. Using captured EEG dataset, classifying the scene and object by decoding the changes in the EEG signals. In this study, independent component analysis, event-related potentials, and grand mean are used to analyze the signal. Machine learning algorithms such as decision tree, random forest, and support vector machine are used to classify the data. This technique is useful in forensic as well as in artificial intelligence for developing future technology. 

scholarly journals A P300 Brain-Computer Interface With a Reduced Visual Field

2020 ◽  
Vol 14 ◽  
Author(s):  
Luiza Kirasirova ◽  
Vladimir Bulanov ◽  
Alexei Ossadtchi ◽  
Alexander Kolsanov ◽  
Vasily Pyatin ◽  
...  
Keyword(s):  
Visual Field ◽  
Visual Processing ◽  
Classification Accuracy ◽  
Peripheral Vision ◽  
Brain Computer Interface ◽  
Event Related Potentials ◽  
Computer Interface ◽  
Central Visual Field ◽  
P300 Speller ◽  
Related Potentials

A P300 brain-computer interface (BCI) is a paradigm, where text characters are decoded from event-related potentials (ERPs). In a popular implementation, called P300 speller, a subject looks at a display where characters are flashing and selects one character by attending to it. The selection is recognized as the item with the strongest ERP. The speller performs well when cortical responses to target and non-target stimuli are sufficiently different. Although many strategies have been proposed for improving the BCI spelling, a relatively simple one received insufficient attention in the literature: reduction of the visual field to diminish the contribution from non-target stimuli. Previously, this idea was implemented in a single-stimulus switch that issued an urgent command like stopping a robot. To tackle this approach further, we ran a pilot experiment where ten subjects operated a traditional P300 speller or wore a binocular aperture that confined their sight to the central visual field. As intended, visual field restriction resulted in a replacement of non-target ERPs with EEG rhythms asynchronous to stimulus periodicity. Changes in target ERPs were found in half of the subjects and were individually variable. While classification accuracy was slightly better for the aperture condition (84.3 ± 2.9%, mean ± standard error) than the no-aperture condition (81.0 ± 2.6%), this difference was not statistically significant for the entire sample of subjects (N = 10). For both the aperture and no-aperture conditions, classification accuracy improved over 4 days of training, more so for the aperture condition (from 72.0 ± 6.3% to 87.0 ± 3.9% and from 72.0 ± 5.6% to 97.0 ± 2.2% for the no-aperture and aperture conditions, respectively). Although in this study BCI performance was not substantially altered, we suggest that with further refinement this approach could speed up BCI operations and reduce user fatigue. Additionally, instead of wearing an aperture, non-targets could be removed algorithmically or with a hybrid interface that utilizes an eye tracker. We further discuss how a P300 speller could be improved by taking advantage of the different physiological properties of the central and peripheral vision. Finally, we suggest that the proposed experimental approach could be used in basic research on the mechanisms of visual processing.

scholarly journals Growing Shapes with a Generalised Model from Neural Correlates of Visual Discrimination

2021 ◽  
pp. 68-78
Author(s):  
Pierre Cutellic
Keyword(s):  
Design Process ◽  
Visual Discrimination ◽  
Event Related Potentials ◽  
Visual Cognition ◽  
Neural Correlates ◽  
Classification Models ◽  
Eeg Signals ◽  
Related Potentials ◽  
Design Solutions ◽  
Singular Individual

AbstractThis paper focuses on the application of visual Event-Related Potentials (ERP) in better generalisations for design and architectural modelling. It makes use of previously built techniques and trained models on EEG signals of a singular individual and observes the robustness of advanced classification models to initiate the development of presentation and classification techniques for enriched visual environments by developing an iterative and generative design process of growing shapes. The pursued interest is to observe if visual ERP as correlates of visual discrimination can hold in structurally similar, but semantically different, experiments and support the discrimination of meaningful design solutions. Following bayesian terms, we will coin this endeavour a Design Belief and elaborate a method to explore and exploit such features decoded from human visual cognition.

scholarly journals Physiological and Medico-Social Research Trends of the Wave P300 and More Late Components of Visual Event-Related Potentials

2020 ◽  
Author(s):  
Sergey Lytaev ◽  
Irina Vatamaniuk
Keyword(s):  
Social Research ◽  
Brain Computer Interface ◽  
Sensory System ◽  
Event Related Potentials ◽  
Computer Interface ◽  
Visual Images ◽  
Physiological Mechanisms ◽  
Multiple Modalities ◽  
Related Potentials ◽  
Sensory Processes

The objective of this study was aimed to study the sensory processes of the “human-computer interaction” model when classifying visual images with an incomplete set of signs based on the analysis of early, middle, late and slow components of event-related potentials (ERPs). 26 healthy subjects (men) aged 20-22 years were investigated. ERPs in 19 monopolar sites according to the 10/20 system were recorded. Discriminant and factor analysis were applied. The component N450 is the most specialized indicator of the perception of unrecognizable (oddball) visual images. The amplitude of the ultra-late components N750 and N900 is also higher under conditions of presentation of the oddball image, regardless of the location of the registration points. The results of the study are discussed in the light of the paradigm of the P300 wave application in brain-computer interface systems, as well as with the peculiarities in brain pathology. Promising directions for the development of studies of the “Brain Computer Interface” (BCI) P300 systems are to increase the throughput of information flows. To extend the application of the P300 ERPs to multiple modalities, the underlying physiological mechanisms and responses of the brain for a particular sensory system and mental function must be carefully examined.

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