scholarly journals Removal of Movement Artifact From High-Density EEG Recorded During Walking and Running

2010 ◽  
Vol 103 (6) ◽  
pp. 3526-3534 ◽  
Author(s):  
Joseph T. Gwin ◽  
Klaus Gramann ◽  
Scott Makeig ◽  
Daniel P. Ferris
Keyword(s):  
Event Related Potentials ◽  
High Density ◽  
Human Cognition ◽  
Whole Body ◽  
Brain Dynamics ◽  
Eeg Signals ◽  
Regression Procedure ◽  
Movement Artifact ◽  
Related Potentials ◽  
Visual Oddball

Although human cognition often occurs during dynamic motor actions, most studies of human brain dynamics examine subjects in static seated or prone conditions. EEG signals have historically been considered to be too noise prone to allow recording of brain dynamics during human locomotion. Here we applied a channel-based artifact template regression procedure and a subsequent spatial filtering approach to remove gait-related movement artifact from EEG signals recorded during walking and running. We first used stride time warping to remove gait artifact from high-density EEG recorded during a visual oddball discrimination task performed while walking and running. Next, we applied infomax independent component analysis (ICA) to parse the channel-based noise reduced EEG signals into maximally independent components (ICs) and then performed component-based template regression. Applying channel-based or channel-based plus component-based artifact rejection significantly reduced EEG spectral power in the 1.5- to 8.5-Hz frequency range during walking and running. In walking conditions, gait-related artifact was insubstantial: event-related potentials (ERPs), which were nearly identical to visual oddball discrimination events while standing, were visible before and after applying noise reduction. In the running condition, gait-related artifact severely compromised the EEG signals: stable average ERP time-courses of IC processes were only detectable after artifact removal. These findings show that high-density EEG can be used to study brain dynamics during whole body movements and that mechanical artifact from rhythmic gait events may be minimized using a template regression procedure.

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.

scholarly journals Event-related potentials following exercise bouts of different intensity

2007 ◽  
Vol 60 (11-12) ◽  
pp. 531-535 ◽  
Author(s):  
Otto Barak ◽  
Vesna Ivetic ◽  
Danka Filipovic ◽  
Nada Naumovic ◽  
Damir Lukac ◽  
...  
Keyword(s):  
Physical Activity ◽  
Positive Impact ◽  
Event Related Potentials ◽  
Bicycle Ergometer ◽  
Whole Body ◽  
Functional Diagnostics ◽  
Ergometer Exercise ◽  
Related Potentials ◽  
The Impact ◽  
Bicycle Ergometer Exercise

Introduction. A number of articles on physical activity analyze the effects of acute bouts of physical exercise on the whole body. These experiments mainly include questionnaires and measurements of reaction time. The use of event-related potentials in laboratories for functional diagnostics is only of recent date. The aim of this experiment was to give insights into the impact of physical activity of different intensity on the amplitude and latency of P300 cognitive potentials. Material and methods. After recording cognitive event-related potentials in 17 young (21.6?1.07 yrs) healthy adults (at Fz and Cz), the participants underwent a controlled bicycle ergometer exercise. Each exercise lasted 10 minutes, with successive increase in the intensity to 60%, 75% and 90% of the maximum pulse rate and maintaining this level of intensity for six minutes. Immediately after each bout of exercise, event-related potentials were recorded. Results. The amplitude of the P300 wave, following exercise intensity at 75% of the maximum pulse (Pmax) (Fz 15.00?4.57; Cz 18.63?8.83 mV) was statistically higher (p<0.05) than the amplitude of the P300 at rest (Fz 11.21?4.15 mV; Cz 13.40?8.04 mV), at 60% (Fz 11.86?5.11 mV; Cz 14.54?8.06 mV) and at 90% of maximum pulse (Fz 13.26?4.73 mV; Cz 14.91?8.91 mV). There were no statistically significant differences (p>0.05) between amplitudes at 60% of Pmax and values obtained at rest and at 90% of Pmax. Also, no statistically significant differences were recorded (p>0.05) among the latencies of P300 recorded at rest (Fz 323.57?13.24 ms; Cz 323.57?13.24 ms) and at 60% of Pmax (Fz 321.14?22.38 ms; Cz 321.86?22.88 ms), at 75% of Pmax (Fz 321.50?16.67 ms; Cz 322.50?14.60 ms) and at 90% of Pmax (Fz 326.29?7.85 ms; Cz 325.43?7.63 ms). Discusssion and Conclusion. Physical activity has a positive impact on cognitive functions. At intermediate intensities, the amplitude of P300 increases, but at submaximal intensities it decreases to values obtained at rest. However, the latency of P300 did not show a statistically significant change after different intensities of exercise.

scholarly journals High-density EEG of auditory steady-state responses during stimulation of basal forebrain parvalbumin neurons

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Eunjin Hwang ◽  
Hio-Been Han ◽  
Jung Young Kim ◽  
Jee Hyun Choi
Keyword(s):  
Steady State ◽  
Basal Forebrain ◽  
Event Related Potentials ◽  
High Density ◽  
Time Stamps ◽  
Related Potentials ◽  
Steady State Responses ◽  
Auditory Steady State Responses ◽  
State Responses ◽  
Stimulation Of

Abstract We present high-density EEG datasets of auditory steady-state responses (ASSRs) recorded from the cortex of freely moving mice with or without optogenetic stimulation of basal forebrain parvalbumin (BF-PV) neurons, known as a subcortical hub circuit for the global workspace. The dataset of ASSRs without BF-PV stimulation (dataset 1) contains raw 36-channel EEG epochs of ASSRs elicited by 10, 20, 30, 40, and 50 Hz click trains and time stamps of stimulations. The dataset of ASSRs with BF-PV stimulation (dataset 2) contains raw 36-channel EEG epochs of 40-Hz ASSRs during BF-PV stimulation with latencies of 0, 6.25, 12.5, and 18.75 ms and time stamps of stimulations. We provide the datasets and step-by-step tutorial analysis scripts written in Python, allowing for descriptions of the event-related potentials, spectrograms, and the topography of power. We complement this experimental dataset with simulation results using a time-dependent perturbation on coupled oscillators. This publicly available dataset will be beneficial to the experimental and computational neuroscientists.

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.

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. 

Neurophysiological and Cognitive Correlates of Error Processing Deficits in Internet Gaming Disorder

2020 ◽  
Vol 30 (9) ◽  
pp. 4914-4921
Author(s):  
Minkyung Park ◽  
Myung Hun Jung ◽  
Jiyoon Lee ◽  
A Ruem Choi ◽  
Sun Ju Chung ◽  
...  
Keyword(s):  
Decision Making ◽  
Internet Gaming Disorder ◽  
Event Related Potentials ◽  
Anterior Cingulate ◽  
Human Cognition ◽  
Error Processing ◽  
Gaming Disorder ◽  
Addictive Disorders ◽  
Internet Gaming ◽  
Related Potentials

Abstract The ability to detect and correct errors is a critical aspect of human cognition. Neuronal dysfunction in error processing has been reported in addictive disorders. The aim of this study was to investigate neural systems underlying error processing using event-related potentials (ERPs) and current source localization as well as neurocognitive executive function tests in patients with Internet gaming disorder (IGD). A total of 68 individuals (34 patients with IGD and 34 healthy controls [HCs]) were included, and two ERP components, error-related negativity (ERN) and error positivity (Pe), were extracted during a GoNogo task. Patients with IGD exhibited significantly reduced ERN and Pe amplitudes compared with HCs. Standardized low-resolution brain electromagnetic tomography (sLORETA) in between-group comparisons revealed that patients with IGD had decreased source activations of the Pe component in the anterior cingulate cortex (ACC) under the Nogo condition. These ERP changes were associated with deficits in decision-making and response inhibition in IGD patients. The results suggest that IGD may be associated with functional abnormalities in the ACC and alterations in neural activity related to both the early unconscious and the later conscious stages of error processing, as well as deficits in area of decision-making.

Different Frequency Bands of Electromagnetic Wave on Age-Related Developmental Changes

2013 ◽  
Vol 479-480 ◽  
pp. 480-485
Author(s):  
Ming Chung Ho ◽  
Chin Fei Huang ◽  
Chia Yi Chou ◽  
Ming Chi Lu ◽  
Chen Hsieh ◽  
...  
Keyword(s):  
Spectral Power ◽  
Brain Activity ◽  
Phase Locking ◽  
Age Groups ◽  
Event Related Potentials ◽  
Brain Dynamics ◽  
Alpha Power ◽  
Time Frequency ◽  
Age Related ◽  
Related Potentials

Brain dynamics is an important issue in understanding child development. However, very little research of the event-related responses has been used to explore changes during childhood. The aim of this study was to investigate mature changes in spatiotemporal organization of brain dynamics. We hypothesized that oscillatory event-related brain activity were affected by age-related changes. The sample include three age groups, namely 7 years (N = 18), 11 years (N = 18), and adults (N = 18). The event-related spectral power (ERPSP), and inter-trial phase locking (ITPL) of the event-related potentials (ERPs) were obtained from the time-frequency analysis of the auditory oddball task. Results revealed that: (a) decreased theta power, but alpha power increased with age; (b) the values of ITPL in the theta and alpha bands increased with age. These suggest that ERPSP, and ITPL provide useful indicators of cognitive maturation processes in children aged 7 and 11 years.

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