scholarly journals Use of Both Eyes-Open and Eyes-Closed Resting States May Yield a More Robust Predictor of Motor Imagery BCI Performance

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 690 ◽  
Author(s):  
Moonyoung Kwon ◽  
Hohyun Cho ◽  
Kyungho Won ◽  
Minkyu Ahn ◽  
Sung Chan Jun
Keyword(s):  
Motor Imagery ◽  
Spectral Entropy ◽  
Eyes Closed ◽  
Spectral Bands ◽  
Brain Signals ◽  
Subject Variability ◽  
Eyes Open ◽  
Brain States ◽  
Single State ◽  
Lateral Sclerosis

Motor-imagery brain-computer interface (MI-BCI) is a technique that manipulates external machines using brain activities, and is highly useful to amyotrophic lateral sclerosis patients who cannot move their limbs. However, it is reported that approximately 15–30% of users cannot modulate their brain signals, which results in the inability to operate motor imagery BCI systems. Thus, advance prediction of BCI performance has drawn researchers’ attention, and some predictors have been proposed using the alpha band’s power, as well as other spectral bands’ powers, or spectral entropy from resting state electroencephalography (EEG). However, these predictors rely on a single state alone, such as the eyes-closed or eyes-open state; thus, they may often be less stable or unable to explain inter-/intra-subject variability. In this work, a modified predictor of MI-BCI performance that considered both brain states (eyes-open and eyes-closed resting states) was investigated with 41 online MI-BCI session datasets acquired from 15 subjects. The results showed that our proposed predictor and online MI-BCI classification accuracy were positively and highly significantly correlated (r = 0.71, p < 0.1 × 10 − 7 ), which indicates that the use of multiple brain states may yield a more robust predictor than the use of a single state alone.

CAN WE MEASURE CONSCIOUSNESS WITH EEG COMPLEXITIES?

2003 ◽  
Vol 13 (03) ◽  
pp. 733-742 ◽  
Author(s):  
FANJI GU ◽  
XIN MENG ◽  
ENHUA SHEN ◽  
ZHIJIE CAI
Keyword(s):  
Mutual Information ◽  
Epileptic Seizures ◽  
Approximate Entropy ◽  
Deep Sleep ◽  
Complexity Measures ◽  
Eyes Closed ◽  
Significant Difference ◽  
Eyes Open ◽  
Brain States ◽  
Light Sleep

Several complexity measures, especially approximate entropy (ApEn) and a new defined complexity measure [Formula: see text], of EEG signals or the ones of the mutual information transmission between different channels of EEGs were calculated to distinguish different consciousness levels for different brain functional states. All of the measures decreased with the following order of brain states: rest with eyes open, eyes closed, light sleep and deep sleep. They decreased during epileptic seizures. On the contrary, the averaged mutual information between different channels increased significantly during the epileptic seizure; there is no significant difference among the averaged mutual information for the subject resting with eyes open, closed, being in light sleep and in deep sleep. Thus, the former indexes seem to be promising candidates to characterize different consciousness levels, while the latter seems not.

scholarly journals Full Spectrum EEG Response to Eyes Open Versus Eyes Closed in Children and The Development of The Alpha Rhythm

2021 ◽  
Author(s):  
Joseph R. Isler ◽  
Nicolo Pini ◽  
Maristella Lucchini ◽  
Lauren C. Shuffrey ◽  
Santiago Morales ◽  
...  
Keyword(s):  
Electrode System ◽  
Full Spectrum ◽  
Eyes Closed ◽  
Sign Change ◽  
Eyes Open ◽  
Brain States ◽  
Eeg Power ◽  
The Difference ◽  
Open Versus ◽  
Electroencephalogram Eeg

Abstract This report examines spectrum-wide (1 to 100 Hz) differences in electroencephalogram (EEG) power between eyes open (EO) and eyes closed (EC) conditions in children. A high density (60 electrode) system was used to measure EEG power at 4, 5, 7, 9, and 11 years of age. Results showed spatial and frequency band differences as a function of age. Specifically, 1) the alpha peak shifts from 8 Hz at 4 years to 9 Hz at 11 years, 2) EC results in increased power at lower frequencies but decreased power at higher frequencies for all ages, 3) the sign change for the difference between EO and EC occurs in a narrow band of frequencies which changes across childhood, 4) at 4 and 5 years, EC increases lower frequency power most prominently over posterior regions; 5) in contrast, at all ages, EC decreases power above 30 Hz most prominently over anterior regions. These results extend previous findings to show EO/EC differences in higher frequencies and to the presence of developmental changes across childhood. This report demonstrates that the simple EO/EC task can provide important information about maturation of brain states and can be done with a very brief, minimal protocol.

scholarly journals Convolution Neural Network Architectures for Motor Imagery EEG Signal Classification

2021 ◽  
Vol 11 (1) ◽  
pp. 15-22
Author(s):  
Nagabushanam Perattur ◽  
S. Thomas George ◽  
D. Raveena Judie Dolly ◽  
Radha Subramanyam
Keyword(s):  
Motor Imagery ◽  
Network Architectures ◽  
Eeg Signal ◽  
Performance Simulation ◽  
Eyes Closed ◽  
Teaching Assessment ◽  
Number Of Layers ◽  
Coefficient Increase ◽  
Eyes Open ◽  
Deep Cnn

This paper has made a survey on motor imagery EEG signals and different classifiers to analyze them. Resolution for medical images like CT, MRI can be improved using deep sense CNN and improved resolution technology. Drowsiness of a student can be analyzed using deep CNN and it helps in teaching, assessment of the student. The authors have proposed 1D-CNN with 2 layers and 3 layers architecture to classify EEG signal for eyes open and eyes closed conditions. Various activation functions and combinations are tried for 2-layer 1D-CNN. Similarly, various loss models are applied in compile model to check the CNN performance. Simulation is carried out using Python 2.7 and 1D-CNN with 3 layers show better performance as it increases number of training parameters by increasing number of layers in the architecture. Accuracy and kappa coefficient increase whereas hamming loss and logloss decreases by increasing number of layers in CNN architecture.

1D-CNN architectures for EEG classification with motor imagery input of eyes open and eyes closed conditions

2021 ◽  
Vol 1 (3) ◽  
pp. 280
Author(s):  
P. Nagabushanam ◽  
S. Thomas George ◽  
M.S.P. Subathra ◽  
S. Radha
Keyword(s):  
Motor Imagery ◽  
Eeg Classification ◽  
Eyes Closed ◽  
Eyes Open

1D CNN Architectures for EEG Classification with Motor Imagery input of eyes open and eyes closed conditions

2020 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
SUBATHRA M. S. P ◽  
S. THOMAS GEORGE ◽  
NAGABUSHANAM PERATTUR ◽  
RADHA SUBRAMANYAM
Keyword(s):  
Motor Imagery ◽  
Eeg Classification ◽  
Eyes Closed ◽  
Eyes Open

scholarly journals Entropy as Measure of Brain Networks’ Complexity in Eyes Open and Closed Conditions

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2178
Author(s):  
Fabrizio Vecchio ◽  
Francesca Miraglia ◽  
Chiara Pappalettera ◽  
Alessandro Orticoni ◽  
Francesca Alù ◽  
...  
Keyword(s):  
Chaotic Behavior ◽  
Brain Networks ◽  
Approximate Entropy ◽  
Brain State ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open ◽  
Brain States ◽  
System Disorder ◽  
Electroencephalogram Eeg

Brain complexity can be revealed even through a comparison between two trivial conditions, such as eyes open and eyes closed (EO and EC respectively) during resting. Electroencephalogram (EEG) has been widely used to investigate brain networks, and several non-linear approaches have been applied to investigate EO and EC signals modulation, both symmetric and not. Entropy is one of the approaches used to evaluate the system disorder. This study explores the differences in the EO and EC awake brain dynamics by measuring entropy. In particular, an approximate entropy (ApEn) was measured, focusing on the specific cerebral areas (frontal, central, parietal, occipital, temporal) on EEG data of 37 adult healthy subjects while resting. Each participant was submitted to an EO and an EC resting EEG recording in two separate sessions. The results showed that in the EO condition the cerebral networks of the subjects are characterized by higher values of entropy than in the EC condition. All the cerebral regions are subjected to this chaotic behavior, symmetrically in both hemispheres, proving the complexity of networks dynamics dependence from the subject brain state. Remarkable dynamics regarding cerebral networks during simple resting and awake brain states are shown by entropy. The application of this parameter can be also extended to neurological conditions, to establish and monitor personalized rehabilitation treatments.

scholarly journals Metabolic connectivity mapping reveals effective connectivity in the resting human brain

2015 ◽  
Vol 113 (2) ◽  
pp. 428-433 ◽  
Author(s):  
Valentin Riedl ◽  
Lukas Utz ◽  
Gabriel Castrillón ◽  
Timo Grimmer ◽  
Josef P. Rauschecker ◽  
...  
Keyword(s):  
Energy Metabolism ◽  
Effective Connectivity ◽  
Human Cognition ◽  
Top Down ◽  
Essential Information ◽  
Connectivity Mapping ◽  
Eyes Closed ◽  
Eyes Open ◽  
Metabolic Connectivity ◽  
Brain States

Directionality of signaling among brain regions provides essential information about human cognition and disease states. Assessing such effective connectivity (EC) across brain states using functional magnetic resonance imaging (fMRI) alone has proven difficult, however. We propose a novel measure of EC, termed metabolic connectivity mapping (MCM), that integrates undirected functional connectivity (FC) with local energy metabolism from fMRI and positron emission tomography (PET) data acquired simultaneously. This method is based on the concept that most energy required for neuronal communication is consumed postsynaptically, i.e., at the target neurons. We investigated MCM and possible changes in EC within the physiological range using “eyes open” versus “eyes closed” conditions in healthy subjects. Independent of condition, MCM reliably detected stable and bidirectional communication between early and higher visual regions. Moreover, we found stable top-down signaling from a frontoparietal network including frontal eye fields. In contrast, we found additional top-down signaling from all major clusters of the salience network to early visual cortex only in the eyes open condition. MCM revealed consistent bidirectional and unidirectional signaling across the entire cortex, along with prominent changes in network interactions across two simple brain states. We propose MCM as a novel approach for inferring EC from neuronal energy metabolism that is ideally suited to study signaling hierarchies in the brain and their defects in brain disorders.

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.

A systematic review and meta-analysis on efficacy of exercise on posture and balance in patients suffering from Diabetic Neuropathy

2020 ◽  
Vol 16 ◽  
Author(s):  
Neerja Thukral ◽  
Jaspreet Kaur ◽  
Manoj Malik
Keyword(s):  
Diabetes Mellitus ◽  
Diabetic Neuropathy ◽  
Meta Analysis ◽  
Berg Balance Scale ◽  
Risk Of Bias ◽  
Core Stability ◽  
Balance Scale ◽  
Eyes Closed ◽  
Significant Difference ◽  
Eyes Open

Background: Peripheral neuropathy is a major and chronic complication of diabetes mellitus affecting more than 50% of patients suffering from diabetes. There is involvement of both large and small diameter nerve fibres leading to altered somatosensory and motor sensations, thereby causing impaired balance and postural instability. Objective: To assess the effects of exercises on posture and balance in patients suffering from diabetes mellitus. Method: Mean changes in Timed Up and Go test(TUGT), Berg Balance Scale and Postural Sway with eyes open and eyes closed on Balance System were primary outcome measures. RevMan 5.3 software was used for the meta-analyses. Eighteen randomized controlled trials met the selection criteria and were included in the study. All the studies ranked high on PEDro Rating scale. Risk of bias was assessed by Cochrane collaboration tool of risk of bias. Included studies had low risk of bias. Sixteen RCT’s were included for the meta-analysis. Result: Results of meta-analysis showed that there was statistically significant improvement in TUGT with p≤ 0.05 and substantial heterogeneity (I 2 = 84%, p < 0.00001) in experimental group as compared to control group. There was statistically significant difference in Berg Balance Scale scores and heterogeneity of I 2 = 62%, p < 0.00001 and significant changes in postural stability (eyes open heterogeneity of I 2 = 100%, p =0.01 and eyes closed, heteogeneity I 2 = 0%, p =0.01). Sensitivity analysis causes change in heterogeneity. Conclusion: It can be concluded that various exercises like balance training, core stability, Tai-Chi, proprioceptive training etc. have a significant effect in improving balance and posture in diabetic neuropathy.

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