scholarly journals Hybrid EEG-EMG system to detect steering actions in car driving settings

2021 ◽  
Author(s):  
Giovanni Vecchiato ◽  
Maria Del Vecchio ◽  
Jonas Ambeck-Madsen ◽  
Luca Ascari ◽  
Pietro Avanzini
Keyword(s):  
Cross Correlation ◽  
Predictive Power ◽  
Muscular Activity ◽  
Motor Preparation ◽  
Emg Activity ◽  
Mu Rhythm ◽  
Eeg Signals ◽  
Emg Signal ◽  
Cross Correlation Analysis ◽  
Car Driving

Understanding mental processes in complex human behaviour is a key issue in the context of driving, representing a milestone for developing user-centred assistive driving devices. Here we propose a hybrid method based on electroencephalographic (EEG) and electromyographic (EMG) signatures to distinguish left from right steering in driving scenarios. Twenty-four participants took part in the experiment consisting of recordings 128-channel EEG as well as EMG activity from deltoids and forearm extensors in non-ecological and ecological steering tasks. Specifically, we identified the EEG mu rhythm modulation correlates with motor preparation of self-paced steering actions in the non-ecological task, while the concurrent EMG activity of the left (right) deltoids correlates with right (left) steering. Consequently, we exploited the mu rhythm de-synchronization resulting from the non-ecological task to detect the steering side by means of a cross-correlation analysis with the ecological EMG signals. Results returned significant cross-correlation values showing the coupling between the non-ecological EEG feature and the muscular activity collected in ecological driving conditions. Moreover, such cross-correlation patterns discriminate left from right steering with an earlier dynamic with respect to the single EMG signal. This hybrid system overcomes the limitation of the EEG signals collected in ecological settings such as low reliability, accuracy and adaptability, thus adding to the EMG the characteristic predictive power of the cerebral data. These results are a proof of concept of how it is possible to complement different physiological signals to control the level of assistance needed by the driver.

scholarly journals EEG–EMG coupling as a hybrid method for steering detection in car driving settings

2022 ◽  
Author(s):  
Giovanni Vecchiato ◽  
Maria Del Vecchio ◽  
Jonas Ambeck-Madsen ◽  
Luca Ascari ◽  
Pietro Avanzini
Keyword(s):  
Hybrid Method ◽  
Cross Correlation ◽  
Muscular Activity ◽  
Motor Preparation ◽  
Emg Activity ◽  
Mu Rhythm ◽  
Emg Signal ◽  
Cross Correlation Analysis ◽  
Left And Right ◽  
Car Driving

AbstractUnderstanding mental processes in complex human behavior is a key issue in driving, representing a milestone for developing user-centered assistive driving devices. Here, we propose a hybrid method based on electroencephalographic (EEG) and electromyographic (EMG) signatures to distinguish left and right steering in driving scenarios. Twenty-four participants took part in the experiment consisting of recordings of 128-channel EEG and EMG activity from deltoids and forearm extensors in non-ecological and ecological steering tasks. Specifically, we identified the EEG mu rhythm modulation correlates with motor preparation of self-paced steering actions in the non-ecological task, while the concurrent EMG activity of the left (right) deltoids correlates with right (left) steering. Consequently, we exploited the mu rhythm de-synchronization resulting from the non-ecological task to detect the steering side using cross-correlation analysis with the ecological EMG signals. Results returned significant cross-correlation values showing the coupling between the non-ecological EEG feature and the muscular activity collected in ecological driving conditions. Moreover, such cross-correlation patterns discriminate the steering side earlier relative to the single EMG signal. This hybrid system overcomes the limitation of the EEG signals collected in ecological settings such as low reliability, accuracy, and adaptability, thus adding to the EMG the characteristic predictive power of the cerebral data. These results prove how it is possible to complement different physiological signals to control the level of assistance needed by the driver.

Single-Neuron Analysis of Human Thalamus in Patients With Intention Tremor and Other Clinical Signs of Cerebellar Disease

2002 ◽  
Vol 87 (4) ◽  
pp. 2084-2094 ◽  
Author(s):  
F. A. Lenz ◽  
C. J. Jaeger ◽  
M. S. Seike ◽  
Y. C. Lin ◽  
S. G. Reich
Keyword(s):  
Neuronal Activity ◽  
Cross Correlation ◽  
Clinical Signs ◽  
Emg Activity ◽  
Phase Lag ◽  
Cerebellar Disease ◽  
Intention Tremor ◽  
Cross Correlation Analysis ◽  
Relay Nucleus ◽  
Cerebellar Tremor

Tremor that occurs as a result of a cerebellar lesion, cerebellar tremor, is characteristically an intention tremor. Thalamic activity may be related to cerebellar tremor because transmission of some cerebellar efferent signals occurs via the thalamus and cortex to the periphery. We have now studied thalamic neuronal activity in a cerebellar relay nucleus (ventral intermediate—Vim) and a pallidal relay nucleus (ventralis oral posterior—Vop) during thalamotomy in patients with intention tremor and other clinical signs of cerebellar disease (tremor patients). The activity of single neurons and the simultaneous electromyographic (EMG) activity of the contralateral upper extremity in tremor patients performing a pointing task were analyzed by spectral cross-correlation analysis. EMG spectra during intention tremor often showed peaks of activity in the tremor-frequency range (1.9–5.8 Hz). There were significant differences in thalamic neuronal activity between tremor patients and controls. Neurons in Vim and Vop had significantly lower firing rates in tremor patients than in patients undergoing thalamic surgery for pain (pain controls). Other studies have shown that inputs to Vim from the cerebellum are transmitted through excitatory connections. Therefore the present results suggest that tremor in these tremor patients is associated with deafferentation of the thalamus from cerebellar efferent pathways. The thalamic X EMG cross-correlation functions were studied for cells located in Vim and Vop. Neuronal and EMG activity were as likely to be significantly correlated for cells in Vim as for those in Vop. Cells in Vim were more likely to have a phase lag relative to EMG than were cells in Vop. In monkeys, cells in the cerebellar relay nucleus of the thalamus, corresponding to Vim, are reported to lead movement during active oscillations at the wrist. In view of these monkey studies, the present results suggest that cells in Vim are deafferented and have a phase lag relative to tremor that is not found in normal active oscillations. The difference in phase of thalamic spike X EMG activity between Vim and Vop may contribute to tremor because lesions of pallidum or Vop are reported to relieve cerebellar tremor.

MARGIN-TRADING ACTIVITIES AND FUTURE STOCK RETURNS: NEW EVIDENCE FROM NONLINEAR ANALYSIS

Fractals ◽  
2020 ◽  
Vol 28 (06) ◽  
pp. 2050126
Author(s):  
QINGSONG RUAN ◽  
JIARUI ZHANG ◽  
YAPING ZHOU ◽  
DAYONG LV
Keyword(s):  
Stock Returns ◽  
Cross Correlation ◽  
Predictive Power ◽  
Granger Causality Test ◽  
Return Predictability ◽  
Positive Information ◽  
Causality Test ◽  
Cross Correlation Analysis ◽  
Trading Activities ◽  
New Evidence

Using multifractal detrended cross-correlation analysis (MF-DCCA) and nonlinear Granger causality test, this paper examines the return predictability of margin-trading activities. Results show that the predictive power of margin-trading activities on subsequent stock returns varies with respect to the different aspects of margin trading. In line with previous studies, we find no significant correlation between margin-buying amount and subsequent stock returns. However, the margin-covering amount is negatively associated with subsequent stock returns; and margin debt is positively associated with the future stock returns. In general, our findings suggest that margin traders may have no positive information when they conduct a margin-buying position, but may possess negative information when covering their positions.

Use of Cross-Correlation Analysis of EEG Signals for Detecting Risk Level for Development of Schizophrenia

2013 ◽  
Vol 47 (3) ◽  
pp. 153-156 ◽  
Author(s):  
O. Yu. Panischev ◽  
S. A. Demin ◽  
A. Ya. Kaplan ◽  
N. Yu. Varaksina
Keyword(s):  
Correlation Analysis ◽  
Cross Correlation ◽  
Risk Level ◽  
Eeg Signals ◽  
Cross Correlation Analysis

Electroencephalagram Analysis in the State of Attention

2013 ◽  
Vol 321-324 ◽  
pp. 716-719
Author(s):  
Jun Chang Zhao ◽  
Zheng Zhong Zheng ◽  
Xiao Lin Huang ◽  
Jun Wang
Keyword(s):  
Correlation Analysis ◽  
Working Conditions ◽  
Brain Function ◽  
Cross Correlation ◽  
Brain State ◽  
Eeg Signals ◽  
Number State ◽  
Cross Correlation Analysis ◽  
The Difference ◽  
First Time

Assessment the distinction of different brain working conditions is very important for brain function study. For the first time, detrended cross-correlation analysis (DCCA) was applied to analyze different brain working conditions. It were compared the difference of DCCA values for EEG signals under count number state and close eyes state. It was found that the DCCA values of count number state EEG signals decreased compared with close eyes state EEG signals which can be helpful for studying different brain state.

Cross-Correlation Analysis of Thalamic Neurons and EMG Activity in Parkinsonian Tremor

1985 ◽  
Vol 48 (1-6) ◽  
pp. 305-308 ◽  
Author(s):  
F.A. Lenz ◽  
R.R. Tasker ◽  
H.C. Kwan ◽  
S. Schnider ◽  
R. Kwong ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Cross Correlation ◽  
Emg Activity ◽  
Thalamic Neurons ◽  
Parkinsonian Tremor ◽  
Cross Correlation Analysis

scholarly journals Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms

2014 ◽  
Vol 112 (11) ◽  
pp. 2865-2887 ◽  
Author(s):  
Katie Z. Zhuang ◽  
Mikhail A. Lebedev ◽  
Miguel A. L. Nicolelis
Keyword(s):  
Cortical Neurons ◽  
Cross Correlation ◽  
Cortical Activity ◽  
Emg Activity ◽  
Brain Machine Interfaces ◽  
Dynamic Task ◽  
Cortical Areas ◽  
Arm Reaching ◽  
Cross Correlation Analysis ◽  
Neurophysiological Studies

Correlation between cortical activity and electromyographic (EMG) activity of limb muscles has long been a subject of neurophysiological studies, especially in terms of corticospinal connectivity. Interest in this issue has recently increased due to the development of brain-machine interfaces with output signals that mimic muscle force. For this study, three monkeys were implanted with multielectrode arrays in multiple cortical areas. One monkey performed self-timed touch pad presses, whereas the other two executed arm reaching movements. We analyzed the dynamic relationship between cortical neuronal activity and arm EMGs using a joint cross-correlation (JCC) analysis that evaluated trial-by-trial correlation as a function of time intervals within a trial. JCCs revealed transient correlations between the EMGs of multiple muscles and neural activity in motor, premotor and somatosensory cortical areas. Matching results were obtained using spike-triggered averages corrected by subtracting trial-shuffled data. Compared with spike-triggered averages, JCCs more readily revealed dynamic changes in cortico-EMG correlations. JCCs showed that correlation peaks often sharpened around movement times and broadened during delay intervals. Furthermore, JCC patterns were directionally selective for the arm-reaching task. We propose that such highly dynamic, task-dependent and distributed relationships between cortical activity and EMGs should be taken into consideration for future brain-machine interfaces that generate EMG-like signals.

Detrended Cross-Correlation Analysis of Epilepsy Electroencephalagram Signals

2013 ◽  
Vol 765-767 ◽  
pp. 2664-2667 ◽  
Author(s):  
Jun Chang Zhao ◽  
Wan Hu Dou ◽  
Hong Da Ji ◽  
Jun Wang
Keyword(s):  
Correlation Analysis ◽  
Cross Correlation ◽  
Normal Subjects ◽  
Eeg Signals ◽  
Cross Correlation Analysis ◽  
Stationary Signals ◽  
The Status ◽  
The Difference ◽  
First Time ◽  
Electroencephalogram Eeg

The cross-correlation performance between epilepsy electroencephalogram (EEG) signals reflects the status of epilepsy patients which has importance for analyzing long-range correlation of non-stationary signals. For the first time, detrended cross-correlation analysis (DCCA) was applied to analyze different physiological and pathological states of epilepsy EEG signals. It were compared the difference of DCCA values between epilepsy patients EEG signals and normal subjects EEG signals. It was found that the DCCA values of epilepsy patients EEG signals increased compared the normal subjects EEG signals which can be helpful for medical diagnosis and treatment.

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