Greater Movement-Related Cortical Potential During Human Eccentric Versus Concentric Muscle Contractions

2001 ◽  
Vol 86 (4) ◽  
pp. 1764-1772 ◽  
Author(s):  
Yin Fang ◽  
Vlodek Siemionow ◽  
Vinod Sahgal ◽  
Fuqin Xiong ◽  
Guang H. Yue
Keyword(s):  
Muscle Activation ◽  
Onset Time ◽  
Muscle Contractions ◽  
Elbow Flexor ◽  
Movement Planning ◽  
Cortical Activation ◽  
Cortical Potential ◽  
Eeg Signals ◽  
Muscle Actions ◽  
The Brain

Despite abundant evidence that different nervous system control strategies may exist for human concentric and eccentric muscle contractions, no data are available to indicate that the brain signal differs for eccentric versus concentric muscle actions. The purpose of this study was to evaluate electroencephalography (EEG)-derived movement-related cortical potential (MRCP) and to determine whether the level of MRCP-measured cortical activation differs between the two types of muscle activities. Eight healthy subjects performed 50 voluntary eccentric and 50 voluntary concentric elbow flexor contractions against a load equal to 10% body weight. Surface EEG signals from four scalp locations overlying sensorimotor-related cortical areas in the frontal and parietal lobes were measured along with kinetic and kinematic information from the muscle and joint. MRCP was derived from the EEG signals of the eccentric and concentric muscle contractions. Although the elbow flexor muscle activation (EMG) was lower during eccentric than concentric actions, the amplitude of two major MRCP components—one related to movement planning and execution and the other associated with feedback signals from the peripheral systems—was significantly greater for eccentric than for concentric actions. The MRCP onset time for the eccentric task occurred earlier than that for the concentric task. The greater cortical signal for eccentric muscle actions suggests that the brain probably plans and programs eccentric movements differently from concentric muscle tasks.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

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 Identification of Brain Electrical Activity Related to Head Yaw Rotations

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3345
Author(s):  
Enrico Zero ◽  
Chiara Bersani ◽  
Roberto Sacile
Keyword(s):  
Electrical Activity ◽  
Electrical Stimulus ◽  
Head Movements ◽  
Brain Electrical Activity ◽  
Output Function ◽  
Input Output ◽  
Eeg Signals ◽  
Head Turning ◽  
Identification Technique ◽  
The Brain

Automatizing the identification of human brain stimuli during head movements could lead towards a significant step forward for human computer interaction (HCI), with important applications for severely impaired people and for robotics. In this paper, a neural network-based identification technique is presented to recognize, by EEG signals, the participant’s head yaw rotations when they are subjected to visual stimulus. The goal is to identify an input-output function between the brain electrical activity and the head movement triggered by switching on/off a light on the participant’s left/right hand side. This identification process is based on “Levenberg–Marquardt” backpropagation algorithm. The results obtained on ten participants, spanning more than two hours of experiments, show the ability of the proposed approach in identifying the brain electrical stimulus associate with head turning. A first analysis is computed to the EEG signals associated to each experiment for each participant. The accuracy of prediction is demonstrated by a significant correlation between training and test trials of the same file, which, in the best case, reaches value r = 0.98 with MSE = 0.02. In a second analysis, the input output function trained on the EEG signals of one participant is tested on the EEG signals by other participants. In this case, the low correlation coefficient values demonstrated that the classifier performances decreases when it is trained and tested on different subjects.

Information-based decoding of the coupling among human brain activity and movement paths

2021 ◽  
pp. 1-10
Author(s):  
Shahul Mujib Kamal ◽  
Norazryana Mat Dawi ◽  
Hamidreza Namazi
Keyword(s):  
Brain Activity ◽  
Point Of View ◽  
Physiological Signals ◽  
Human Walking ◽  
Eeg Signals ◽  
Rehabilitation Science ◽  
Information Contents ◽  
First Time ◽  
The Brain ◽  
Brain Reaction

BACKGROUND: Walking like many other actions of a human is controlled by the brain through the nervous system. In fact, if a problem occurs in our brain, we cannot walk correctly. Therefore, the analysis of the coupling of brain activity and walking is very important especially in rehabilitation science. The complexity of movement paths is one of the factors that affect human walking. For instance, if we walk on a path that is more complex, our brain activity increases to adjust our movements. OBJECTIVE: This study for the first time analyzed the coupling of walking paths and brain reaction from the information point of view. METHODS: We analyzed the Shannon entropy for electroencephalography (EEG) signals versus the walking paths in order to relate their information contents. RESULTS: According to the results, walking on a path that contains more information causes more information in EEG signals. A strong correlation (p= 0.9999) was observed between the information contents of EEG signals and walking paths. Our method of analysis can also be used to investigate the relation among other physiological signals of a human and walking paths, which has great benefits in rehabilitation science.

Whole-body adaptation to novel dynamics does not transfer between effectors

2021 ◽  
Author(s):  
Alison Pienciak-Siewert ◽  
Alaa A Ahmed
Keyword(s):  
Postural Control ◽  
Arm Movement ◽  
Movement Planning ◽  
Reaching Movements ◽  
Whole Body ◽  
Postural Control System ◽  
Movement Dynamics ◽  
Gradual Introduction ◽  
Postural Movement ◽  
The Brain

How does the brain coordinate concurrent adaptation of arm movements and standing posture? From previous studies, the postural control system can use information about previously adapted arm movement dynamics to plan appropriate postural control; however, it is unclear whether postural control can be adapted and controlled independently of arm control. The present study addresses that question. Subjects practiced planar reaching movements while standing and grasping the handle of a robotic arm, which generated a force field to create novel perturbations. Subjects were divided into two groups, for which perturbations were introduced in either an abrupt or gradual manner. All subjects adapted to the perturbations while reaching with their dominant (right) arm, then switched to reaching with their non-dominant (left) arm. Previous studies of seated reaching movements showed that abrupt perturbation introduction led to transfer of learning between arms, but gradual introduction did not. Interestingly, in this study neither group showed evidence of transferring adapted control of arm or posture between arms. These results suggest primarily that adapted postural control cannot be transferred independently of arm control in this task paradigm. In other words, whole-body postural movement planning related to a concurrent arm task is dependent on information about arm dynamics. Finally, we found that subjects were able to adapt to the gradual perturbation while experiencing very small errors, suggesting that both error size and consistency play a role in driving motor adaptation.

Information-based analysis of the coupling between brain and heart reactions to olfactory stimulation

2021 ◽  
pp. 1-11
Author(s):  
Najmeh Pakniyat ◽  
Mohammad Hossein Babini ◽  
Vladimir V. Kulish ◽  
Hamidreza Namazi
Keyword(s):  
Time Series ◽  
Biomedical Science ◽  
Strongly Correlated ◽  
Eeg Signals ◽  
Ecg Signals ◽  
Olfactory Stimuli ◽  
Electrocardiogram Ecg ◽  
First Time ◽  
Electroencephalogram Eeg ◽  
The Brain

BACKGROUND: Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart’s activity, a relationship should exist among their activities. OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis. METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18–22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked. RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566). CONCLUSION: We conclude that heart and brain activities are related.

Older Adults Use a Unique Strategy to Lift Inertial Loads With the Elbow Flexor Muscles

2000 ◽  
Vol 83 (4) ◽  
pp. 2030-2039 ◽  
Author(s):  
Andrew E. Graves ◽  
Kurt W. Kornatz ◽  
Roger M. Enoka
Keyword(s):  
Young Adults ◽  
Elbow Joint ◽  
Muscle Activation ◽  
Angular Displacement ◽  
Elbow Flexor ◽  
Lengthening Contractions ◽  
Old Adults ◽  
Flexor Muscles ◽  
Elbow Flexor Muscles ◽  
Target Force

The purpose of this study was to determine the effect of age on the ability to exert steady forces and to perform steady flexion movements with the muscles that cross the elbow joint. An isometric task required subjects to exert a steady force to match a target force that was displayed on a monitor. An anisometric task required subjects to raise and lower inertial loads so that the angular displacement around the elbow joint matched a template displayed on a monitor. Steadiness was measured as the coefficient of variation of force and as the normalized standard deviation of wrist acceleration. For the isometric task, steadiness as a function of target force decreased similarly for old adults and young adults. For the anisometric task, steadiness increased as a function of the inertial load and there were significant differences caused by age. Old adults were less steady than young adults during both shortening and lengthening contractions with the lightest loads. Furthermore, old adults were least steady when performing lengthening contractions. These behaviors appear to be associated with the patterns of muscle activation. These results suggest that different neural strategies are used to control isometric and anisometric contractions performed with the elbow flexor muscles and that these strategies do not change in parallel with advancing age.

Involvement of Subcortical Structures in the Preparation of Self-Paced Movement

2004 ◽  
Vol 18 (2/3) ◽  
pp. 130-139 ◽  
Author(s):  
Guillermo Paradiso ◽  
Danny Cunic ◽  
Robert Chen
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Basal Ganglia ◽  
Onset Time ◽  
Movement Planning ◽  
Movement Preparation ◽  
Subcortical Structures ◽  
Postoperative Mri ◽  
Deep Brain

Abstract Although it has long been suggested that the basal ganglia and thalamus are involved in movement planning and preparation, there was little direct evidence in humans to support this hypothesis. Deep brain stimulation (DBS) is a well-established treatment for movement disorders such as Parkinson's disease, tremor, and dystonia. In patients undergoing DBS surgery, we recorded simultaneously from scalp contacts and from electrodes surgically implanted in the subthalamic nucleus (STN) of 13 patients with Parkinson's disease and in the “cerebellar” thalamus of 5 patients with tremor. The aim of our studies was to assess the role of the cortico-basal ganglia-thalamocortical loop through the STN and the cerebello-thalamocortical circuit through the “cerebellar” thalamus in movement preparation. The patients were asked to perform self-paced wrist extension movements. All subjects showed a cortical readiness potential (RP) with onset ranging between 1.5 to 2s before the onset of movement. Subcortical RPs were recorded in 11 of 13 with electrodes in the STN and in 4 of 5 patients with electrodes in the thalamus. The onset time of the STN and thalamic RPs were not significantly different from the onset time of the scalp RP. The STN and thalamic RPs were present before both contralateral and ipsilateral hand movements. Postoperative MRI studies showed that contacts with maximum RP amplitude generally were inside the target nucleus. These findings indicate that both the basal ganglia and the cerebellar circuits participate in movement preparation in parallel with the cortex.

scholarly journals Backward walking highlights gait asymmetries in children with cerebral palsy

2018 ◽  
Vol 119 (3) ◽  
pp. 1153-1165 ◽  
Author(s):  
Germana Cappellini ◽  
Francesca Sylos-Labini ◽  
Michael J. MacLellan ◽  
Annalisa Sacco ◽  
Daniela Morelli ◽  
...  
Keyword(s):  
Cerebral Palsy ◽  
Muscle Activation ◽  
Pattern Generation ◽  
Comprehensive Assessment ◽  
Emg Activity ◽  
Backward Walking ◽  
Spinal Circuitry ◽  
Children With Cerebral Palsy ◽  
Control Circuits ◽  
The Brain

To investigate how early injuries to developing motor regions of the brain affect different forms of gait, we compared the spatiotemporal locomotor patterns during forward (FW) and backward (BW) walking in children with cerebral palsy (CP). Bilateral gait kinematics and EMG activity of 11 pairs of leg muscles were recorded in 14 children with CP (9 diplegic, 5 hemiplegic; 3.0–11.1 yr) and 14 typically developing (TD) children (3.3–11.8 yr). During BW, children with CP showed a significant increase of gait asymmetry in foot trajectory characteristics and limb intersegmental coordination. Furthermore, gait asymmetries, which were not evident during FW in diplegic children, became evident during BW. Factorization of the EMG signals revealed a comparable structure of the motor output during FW and BW in all groups of children, but we found differences in the basic temporal activation patterns. Overall, the results are consistent with the idea that both forms of gait share pattern generation control circuits providing similar (though reversed) kinematic patterns. However, BW requires different muscle activation timings associated with muscle modules, highlighting subtle gait asymmetries in diplegic children, and thus provides a more comprehensive assessment of gait pathology in children with CP. The findings suggest that spatiotemporal asymmetry assessments during BW might reflect an impaired state and/or descending control of the spinal locomotor circuitry and can be used for diagnostic purposes and as complementary markers of gait recovery.NEW & NOTEWORTHY Early injuries to developing motor regions of the brain affect both forward progression and other forms of gait. In particular, backward walking highlights prominent gait asymmetries in children with hemiplegia and diplegia from cerebral palsy and can give a more comprehensive assessment of gait pathology. The observed spatiotemporal asymmetry assessments may reflect both impaired supraspinal control and impaired state of the spinal circuitry.

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.

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