EEG Based BCI Using Visual Imagery Task for Robot Control

2013 ◽  
Vol 61 (2) ◽  
Author(s):  
Husnaini Azmy ◽  
Norlaili Mat Safri
Keyword(s):  
Spectral Power ◽  
Normal Subjects ◽  
Imagery Task ◽  
Non Invasive ◽  
Cortex Area ◽  
Wireless Robot ◽  
Scalp Location ◽  
Task Conditions ◽  
Electroencephalogram Eeg ◽  
The Brain

The aim of this study is to detect the brain activation on scalp by Electroencephalogram (EEG) task–based for brain computer interface (BCI) using wirelessly control robot. EEG was measured in 8 normal subjects for control and task conditions. The objective is to determine one scalp location which will give signals that can be used to control the wireless robot using BCI and EEG, using non invasive and without subject training. In control condition subjects were ask to relax but in task condition, subjects were asked to imagine a star rotating clockwise at position 45 degrees direction pointed by the wireless robot where at this angle the target is located. At position 0 and 90 degree angle subjects were asked to relax since there is no target on that direction. Using EEG spectral power analysis and normalization, the optimum location for this task has been detected at position F8 which is in frontal cortex area and the rhythm happened at alpha frequency band. At this position, the signals from the brain should be able to drive the robot to the required direction by giving correct and accurate signals to robot moving towards target.

scholarly journals Electrophysiological Correlates of Shyness Affected by Facial Attractiveness

2022 ◽  
Vol 12 ◽  
Author(s):  
Xiaofan Xu ◽  
Bingbing Li ◽  
Ping Liu ◽  
Dan Li
Keyword(s):  
Hemispheric Asymmetry ◽  
Public Speaking ◽  
Spectral Power ◽  
Facial Attractiveness ◽  
Neural Basis ◽  
Baseline Phase ◽  
The Public ◽  
Significant Difference ◽  
Electroencephalogram Eeg ◽  
The Brain

Previous neurological studies of shyness have focused on the hemispheric asymmetry of alpha spectral power. To the best of our knowledge, few studies have focused on the interaction between different frequencies bands in the brain of shyness. Additionally, shy individuals are even shyer when confronted with a group of people they consider superior to them. This study aimed to reveal the neural basis of shy individuals using the delta-beta correlation. Further, it aimed to investigate the effect of evaluators’ facial attractiveness on the delta-beta correlation of shyness during the speech anticipation phase. We recorded electroencephalogram (EEG) activity of 94 participants during rest and anticipation of the public speaking phase. Moreover, during the speech anticipation phase, participants were presented with high or low facial attractiveness. The results showed that, as predicted, the delta-beta correlation in the frontal region was more robust for high shyness than for low shyness during the speech anticipation phase. However, no significant differences were observed in the delta-beta correlation during the baseline phase. Further exploration found that the delta-beta correlation was more robust for high facial attractiveness than low facial attractiveness in the high shyness group. However, no significant difference was found in the low-shyness group. This study suggests that a stronger delta-beta correlation might be the neural basis for shy individuals. Moreover, high facial attractiveness might enhance the delta-beta correlation of high shyness in anticipation of public speaking.

scholarly journals Introduction to the identification of brain waves based on their frequency

2018 ◽  
Vol 210 ◽  
pp. 05012 ◽  
Author(s):  
Zuzana Koudelková ◽  
Martin Strmiska
Keyword(s):  
Brain Activity ◽  
Computer Interface ◽  
Brain Wave ◽  
Non Invasive ◽  
Brain Waves ◽  
Analytical Reasoning ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Research Type

A Brain Computer Interface (BCI) enables to get electrical signals from the brain. In this paper, the research type of BCI was non-invasive, which capture the brain signals using electroencephalogram (EEG). EEG senses the signals from the surface of the head, where one of the important criteria is the brain wave frequency. This paper provides the measurement of EEG using the Emotiv EPOC headset and applications developed by Emotiv System. Two types of the measurements were taken to describe brain waves by their frequency. The first type of the measurements was based on logical and analytical reasoning, which was captured during solving mathematical exercise. The second type was based on relax mind during listening three types of relaxing music. The results of the measurements were displayed as a visualization of a brain activity.

Analysis of Speech Imagery using Functional and Effective EEG based Brain Connectivity Parameters

2015 ◽  
Vol 9 (4) ◽  
pp. 33-48 ◽  
Author(s):  
Sandhya Chengaiyan ◽  
Kavitha Anandhan
Keyword(s):  
Brain Connectivity ◽  
Effective Connectivity ◽  
Imagery Task ◽  
Left Frontal Lobe ◽  
Directed Transfer Function ◽  
Invasive Approach ◽  
Non Invasive ◽  
Wernicke’S Area ◽  
The Given ◽  
The Brain

Speech imagery is a form of mental imagery which refers to the activity of talking to oneself in silence. In this paper, EEG coherence, a functional connectivity parameter is calculated to analyze the concurrence of the different regions of the brain and Effective connectivity parameters such as Partial Directed Coherence (PDC), Directed Transfer Function (DTF) and Information theory based parameter Transfer Entropy (TE) are estimated to find the direction and strength of the connectivity patterns of the given speech imagery task. It has been observed from the results that by using functional and effective connectivity parameters the left frontal lobe electrodes was found to be high during speech production and left temporal lobe electrodes was found to be high while imagining the word silently in the brain due to the proximity of the electrodes to the Broca's and Wernicke's area respectively. The results suggest that the proposed methodology is a promising non-invasive approach to study directional connectivity in the brain between mutually interconnected neural populations.

scholarly journals How Memory Switches Brain Responses of Patients with Posttraumatic Stress Disorder

2021 ◽  
Author(s):  
Jun Inoue ◽  
Kayako Matsuo ◽  
Toshiki Iwabuchi ◽  
Yasuo Takehara ◽  
Hidenori Yamasue
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Posttraumatic Stress ◽  
Stress Disorder ◽  
Imagery Task ◽  
Traumatic Memory ◽  
Traumatic Memories ◽  
Brain Responses ◽  
And Gender ◽  
Task Conditions ◽  
The Brain

Abstract To characterize the brain responses to traumatic memories in posttraumatic stress disorder (PTSD), we conducted task-employed functional magnetic resonance imaging and, in the process, devised a simple but innovative approach—correlation computation between task conditions. A script-driven imagery task was used to compare the responses to a script of the patients’ own traumatic memories and that of tooth brushing as a daily activity and to evaluate how eye movement desensitization and reprocessing (EMDR), an established therapy for PTSD, resolved the alterations in patients. Nine patients with PTSD (7 females, aged 27–50 years) and nine age- and gender-matched healthy controls participated in this study. Six patients underwent the second scan under the same paradigm after EMDR. We discovered intense negative correlations between daily and traumatic memory conditions in broad areas, including the hippocampus; patients who had an intense suppression of activation during daily recognition showed an intense activation while remembering a traumatic memory, whereas patients who had a hyperarousal in daily recognition showed an intense suppression while remembering a traumatic memory as a form of “shut-down.” Moreover, the magnitude of the discrepancy was reduced in patients who remitted after EMDR, which might predict an improved prognosis of PTSD.

scholarly journals Prediction of REM (Rapid Eye Movement) Sleep Behaviour Disorder using EEG Signal applied EMG1 and EMG2 Channel

2021 ◽  
Vol 14 (01) ◽  
pp. 519-524
Author(s):  
Mohd. Maroof Siddiqui ◽  
Ruchin Jain
Keyword(s):  
Sleep Disorder ◽  
Normal Subjects ◽  
Time Frequency ◽  
Brain Signals ◽  
Eeg Data ◽  
Spectrum Density ◽  
Short Time ◽  
Electroencephalogram Eeg ◽  
Electrical Activities ◽  
The Brain

This sleep disorder is reflected as the changes in the electrical activities and chemical activities in the brain that can be observed by capturing the brain signals and the images. In this research, Short Time-frequency analysis of Power Spectrum Density (STFAPSD) approach applied on Electroencephalogram (EEG) Signals for prediction of RBD sleep disorder. Collection of Electroencephalogram (EEG) of normal subjects & different type of sleep disordered subjects & application of signal processing on EEG data for development the algorithm for detection of sleep disorder and implementation in MATLAB.

Introduction to Electroencephalography

2019 ◽  
pp. 35-46 ◽  
Author(s):  
Michael Avidan ◽  
Jamie Sleigh
Keyword(s):  
Monitoring Method ◽  
Spectral Edge Frequency ◽  
Non Invasive ◽  
Eeg Changes ◽  
Frequency Coupling ◽  
Cross Frequency Coupling ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Nmda Blockers ◽  
Induction Agents

This chapter presents an introduction to electroencephalography, a non-invasive electrophysiological monitoring method to record electrical activity of the brain. It discusses waveforms in the electroencephalogram (EEG), and EEG changes with general anaesthesia. It covers EEG neurobiology, effects of different drugs (e.g., opioids, NMDA blockers, GABAergic intravenous induction agents, hydrocarbon-based volatile anaesthetic drugs), EEG changes as a function of patients’ age, and the titration of anaesthesia. It also covers special concepts in anaesthetic monitoring, including power spectrum, spectral edge frequency, spectrogram, cross frequency coupling and coherence, proprietary pEEG indices, EEG measures of connectivity, and artefacts. Finally, it discusses future directions within the specialty.

scholarly journals SEROTONIN LEVEL IN PERIPHERAL BLOOD AND THE BRAIN’S BIOELECTRICAL ACTIVITY IN YOUNG PEOPLE 15-17 YEARS

2019 ◽  
Vol 19 (1S) ◽  
pp. 137-138
Author(s):  
E V Krivonogova ◽  
L V Poskotinova ◽  
D B Demin ◽  
O A Stavinskaya
Keyword(s):  
Young People ◽  
Peripheral Blood ◽  
Spectral Power ◽  
Power Level ◽  
Bioelectrical Activity ◽  
Serotonin Level ◽  
Temporal Region ◽  
The Right ◽  
Electroencephalogram Eeg ◽  
The Brain

The purpose of the work is to evaluate the features of the organization of the bioelectrical activity of the brain with different levels of serotonin in the serum of peripheral blood in young people 15-17 years old. The study involved 93 healthy girls and boys (15-17 years) of the Arkhangelsk region and the Nenets autonomous okrug. A serotonin level is determined in serum by enzyme immunoassay using a set of “Serotonin ELISA”. The electroencephalogram (EEG) power spectrum (PS) in the alpha, beta and theta frequencies ranges was recorded using an electroencephalograph “Encephalan” (Medicom, Taganrog). Age-dependent electroencephalogram (EEG) patterns is associated with the level of serotonin in peripheral blood in adolescents. On the background of a higher level of serotonin in the blood, compared to girls, boys have localized associations of theta and beta1 activity of EEG and serotonin levels, mainly in the right frontal-temporal region. In girls, the spectral power level of the EEG theta activity is more dependent on the level of serotonin in the blood, and a greater number of brain areas are involved in correlation interactions in comparison with young men (temporal regions on the left and frontal, central, parietal regions of both hemispheres of the brain).

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

scholarly journals A Simple Method to Avoid Brain Shift during Neuronavigation: Technical Note

2020 ◽  
Author(s):  
Jair Leopoldo Raso
Keyword(s):  
Dura Mater ◽  
Brain Area ◽  
Conventional Technique ◽  
Technical Note ◽  
Cortical Surface ◽  
Brain Shift ◽  
Simple Method ◽  
Cortex Area ◽  
Neuronavigation System ◽  
The Brain

Abstract Introduction The precise identification of anatomical structures and lesions in the brain is the main objective of neuronavigation systems. Brain shift, displacement of the brain after opening the cisterns and draining cerebrospinal fluid, is one of the limitations of such systems. Objective To describe a simple method to avoid brain shift in craniotomies for subcortical lesions. Method We used the surgical technique hereby described in five patients with subcortical neoplasms. We performed the neuronavigation-guided craniotomies with the conventional technique. After opening the dura and exposing the cortical surface, we placed two or three arachnoid anchoring sutures to the dura mater, close to the edges of the exposed cortical surface. We placed these anchoring sutures under microscopy, using a 6–0 mononylon wire. With this technique, the cortex surface was kept close to the dura mater, minimizing its displacement during the approach to the subcortical lesion. In these five cases we operated, the cortical surface remained close to the dura, anchored by the arachnoid sutures. All the lesions were located with a good correlation between the handpiece tip inserted in the desired brain area and the display on the navigation system. Conclusion Arachnoid anchoring sutures to the dura mater on the edges of the cortex area exposed by craniotomy constitute a simple method to minimize brain displacement (brain-shift) in craniotomies for subcortical injuries, optimizing the use of the neuronavigation system.

scholarly journals fNIRS Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task

2021 ◽  
Vol 11 (11) ◽  
pp. 4922
Author(s):  
Tengfei Ma ◽  
Wentian Chen ◽  
Xin Li ◽  
Yuting Xia ◽  
Xinhua Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Total Duration ◽  
Signal Classification ◽  
Imagery Task ◽  
Neural Activities ◽  
Computer Interfaces ◽  
Acquisition Device ◽  
The Time Domain ◽  
The Brain

To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).

Sign in / Sign up

Export Citation Format

Share Document