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.

scholarly journals Context-Based Filtering for Assisted Brain-Actuated Wheelchair Driving

2007 ◽  
Vol 2007 ◽  
pp. 1-12 ◽  
Author(s):  
Gerolf Vanacker ◽  
José del R. Millán ◽  
Eileen Lew ◽  
Pierre W. Ferrez ◽  
Ferran Galán Moles ◽  
...  
Keyword(s):  
Control System ◽  
Shared Control ◽  
Eeg Signals ◽  
Intelligent Processing ◽  
Intelligent Wheelchair ◽  
Brain Signals ◽  
Brain Interface ◽  
The Subject ◽  
Electroencephalogram Eeg ◽  
The Brain

Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subject's steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

Electroencephalogram activity in the anoxic turtle brain

1997 ◽  
Vol 273 (3) ◽  
pp. R911-R919 ◽  
Author(s):  
J. A. Fernandes ◽  
P. L. Lutz ◽  
A. Tannenbaum ◽  
A. T. Todorov ◽  
L. Liebovitch ◽  
...  
Keyword(s):  
Transition Period ◽  
Activity Level ◽  
Sharp Waves ◽  
Energy Demands ◽  
Order Of Magnitude ◽  
Activity State ◽  
Low Amplitude ◽  
Electroencephalogram Eeg ◽  
Electrical Activities ◽  
The Brain

The anoxia-tolerant turtle brain slowly undergoes a complex sequence of changes in electroencephalogram (EEG) activity as the brain systematically downregulates its energy demands. Following N2 respiration, the root mean square voltage rapidly fell, reaching approximately 20% of normoxic levels after approximately 100 min of anoxia. During the first 20- to 40-min transition period, the power of the EEG decreased substantially, particularly in the 12- to 24-Hz band, with low-amplitude slow wave activity predominating (3-12 Hz). Bursts of high voltage rhythmic slow (approximately 3-8 Hz) waves were seen during the 20- to 100-min period of anoxia, accompanied by large sharp waves. During the next 400 min of N2 respiration, two distinct patterns of electrical activity characterized the anoxic turtle brain: 1) a sustained but depressed activity level, with an EEG amplitude approximately 20% of the normoxic control and with total EEG power reduced by one order of magnitude at all frequencies, and 2) short (3-15 s) periodic (0.5-2/min) bursts of mixed-frequency activity that interrupted the depressed activity state. We speculate that the EEG patterns seen during sustained anoxia represent the minimal or basic electrical activities that are compatible with the survival of the anoxic turtle brain as an integrated unit, which allow the brain to return to normal functioning when air respiration resumed.

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 Statistical Method to Extract Evoked Potentials from Noise

2020 ◽  
Vol 170 ◽  
pp. 02005
Author(s):  
Besma Benchabane ◽  
Moncef Benkherrat ◽  
Salah Djelel
Keyword(s):  
Wavelet Transform ◽  
Evoked Potentials ◽  
A Priori ◽  
Brain Regions ◽  
Statistical Test ◽  
A Priori Information ◽  
Time Frequency ◽  
Priori Information ◽  
Electrical Activities ◽  
The Brain

Evoked Potentials are induced by visual or auditory stimulation. The Evoked Potentials represent transient electrical activities of some limited brain regions. The signal-noise ratio (SNR) of the EPs is typically around -10 dB. In order to study brain activities related to information processing in the brain, one has to “extract” the single EPs from the noise. We propose a method does not require a priori information concerning the characteristics (time, frequency) of the signal and does not use a template. The method proposed in this work use the wavelet transform associated with a statistical test.

BCI-Touch Based System: A Multimodal CAD Interface for Object Manipulation

2013 ◽  
Author(s):  
Rohit Bhat ◽  
Akshay Deshpande ◽  
Rahul Rai ◽  
Ehsan Tarkesh Esfahani
Keyword(s):  
Computer Aided Design ◽  
Brain Activity ◽  
Temporal Cortex ◽  
Target Object ◽  
Time Frequency ◽  
System A ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Aided Design ◽  
Electroencephalogram Eeg

The aim of this paper is to explore a new multimodal Computer Aided Design (CAD) platform based on brain-computer interfaces and touch based systems. The paper describes experiments and algorithms for manipulating geometrical objects in CAD systems using touch-based gestures and movement imagery detected though brain waves. Gestures associated with touch based systems are subjected to ambiguity since they are two dimensional in nature. Brain signals are considered here as the main source to resolve these ambiguities. The brainwaves are recorded in terms of electroencephalogram (EEG) signals. Users wear a neuroheadset and try to move and rotate a target object on a touch screen. As they perform these actions, the EEG headset collects brain activity from 14 locations on the scalp. The data is analyzed in the time-frequency domain to detect the desynchronizations of certain frequency bands (3–7Hz, 8–13 Hz, 14–20Hz 21–29Hz and 30–50Hz) in the temporal cortex as an indication of motor imagery.

scholarly journals Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO BasedK-Means Clustering

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Suraj ◽  
Purnendu Tiwari ◽  
Subhojit Ghosh ◽  
Rakesh Kumar Sinha
Keyword(s):  
Motor Imagery ◽  
Execution Time ◽  
Feature Vector ◽  
Time Frequency ◽  
Real World Application ◽  
Frequency Representation ◽  
High Level ◽  
Electroencephalogram Eeg ◽  
The Brain

Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO basedK-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO basedK-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) basedK-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.

scholarly journals Electroencephalogram of Happy Emotional Cognition Based on Complex System of Music and Image Visual and Auditory

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Lin Gan ◽  
Mu Zhang ◽  
Jiajia Jiang ◽  
Fajie Duan
Keyword(s):  
Complex System ◽  
Cognitive Neuroscience ◽  
Auditory Stimulation ◽  
External Information ◽  
Eeg Signals ◽  
Baseline Drift ◽  
Average Accuracy ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
The Brain

People are ingesting various information from different sense organs all the time to complete different cognitive tasks. The brain integrates and regulates this information. The two significant sensory channels for receiving external information are sight and hearing that have received extensive attention. This paper mainly studies the effect of music and visual-auditory stimulation on electroencephalogram (EEG) of happy emotion recognition based on a complex system. In the experiment, the presentation was used to prepare the experimental stimulation program, and the cognitive neuroscience experimental paradigm of EEG evoked by happy emotion pictures was established. Using 93 videos as natural stimuli, fMRI data were collected. Finally, the collected EEG signals were removed with the eye artifact and baseline drift, and the t-test was used to analyze the significant differences of different lead EEG data. Experimental data shows that, by adjusting the parameters of the convolutional neural network, the highest accuracy of the two-classification algorithm can reach 98.8%, and the average accuracy can reach 83.45%. The results show that the brain source under the combined visual and auditory stimulus is not a simple superposition of the brain source of the single visual and auditory stimulation, but a new interactive source is generated.

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