Using a Common Average Reference to Improve Cortical Neuron Recordings From Microelectrode Arrays

Ludwig, Kip A.; Miriani, Rachel M.; Langhals, Nicholas B.; Joseph, Michael D; Anderson, David J.; Kipke, Daryl R.

doi:10.1152/jn.90989.2008

Cited by 375 publications

(305 citation statements)

References 45 publications

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“…Data were detrended to remove DC bias and common averaged reference to reduce noise and artifacts across channels [22]. A finite impulse response (FIR) notch filter at 60Hz was used to remove electrical noise, followed by a FIR bandpass filter from 1 to 150Hz.…”

Section: Data Processing and Statistical Analysismentioning

confidence: 99%

Cerebral blood flow is decoupled from blood pressure and linked to EEG bursting after resuscitation from cardiac arrest

Crouzet¹,

Wilson²,

Bazrafkan³

et al. 2016

Biomed. Opt. Express

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Abstract:In the present study, we have developed a multi-modal instrument that combines laser speckle imaging, arterial blood pressure, and electroencephalography (EEG) to quantitatively assess cerebral blood flow (CBF), mean arterial pressure (MAP), and brain electrophysiology before, during, and after asphyxial cardiac arrest (CA) and resuscitation. Using the acquired data, we quantified the time and magnitude of the CBF hyperemic peak and stabilized hypoperfusion after resuscitation. Furthermore, we assessed the correlation between CBF and MAP before and after stabilized hypoperfusion. Finally, we examined when brain electrical activity resumes after resuscitation from CA with relation to CBF and MAP, and developed an empirical predictive model to predict when brain electrical activity resumes after resuscitation from CA. Our results show that: 1) more severe CA results in longer time to stabilized cerebral hypoperfusion; 2) CBF and MAP are coupled before stabilized hypoperfusion and uncoupled after stabilized hypoperfusion; 3) EEG activity (bursting) resumes after the CBF hyperemic phase and before stabilized hypoperfusion; 4) CBF predicts when EEG activity resumes for 5-min asphyxial CA, but is a poor predictor for 7-min asphyxial CA. Together, these novel findings highlight the importance of using multimodal approaches to investigate CA recovery to better understand physiological processes and ultimately improve neurological outcome.

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Section: Data Processing and Statistical Analysismentioning

confidence: 99%

Cerebral blood flow is decoupled from blood pressure and linked to EEG bursting after resuscitation from cardiac arrest

Crouzet¹,

Wilson²,

Bazrafkan³

et al. 2016

Biomed. Opt. Express

View full text Add to dashboard Cite

show abstract

“…As noted in other studies, recorded unit activity for animals in this study typically diminished substantially over the three weeks following surgery [2,[38][39]42]. When fewer than two well-isolated neuronal units were evident, animals were transitioned to a singlestate classification task using their recorded local field potentials (LFPs) as the input to the classifier.…”

Section: Performance Using Local Field Potentialsmentioning

confidence: 58%

“…Surgical procedures were identical to those outlined previously [38][39][40]. While five animals were implanted in motor cortex, one animal was implanted in cingulate cortex using coordinates 1.5-2.5 mm anterior to bregma, 0.3-0.7 mm lateral from bregma, and 1.6-2.5 mm deep from the surface of the brain [41].…”

Section: Behavioral Testingmentioning

confidence: 99%

Use of a Bayesian maximum-likelihood classifier to generate training data for brain–machine interfaces

Ludwig¹,

Miriani²,

Langhals³

et al. 2011

J. Neural Eng.

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Brain-machine interface decoding algorithms need to be predicated on assumptions that are easily met outside of an experimental setting to enable a practical clinical device. Given present technology limitations, there is a need for decoding algorithms which a) are not dependent upon a large number of neurons for control, b) are adaptable to alternative sources of neuronal input such as local field potentials, and c) require only marginal training data for daily calibrations. Moreover, practical algorithms must recognize when the user is not intending to generate a control output and eliminate poor training data.In this study, we introduce and evaluate a Bayesian Maximum-Likelihood Estimation (bMLE) strategy to address the issues of isolating quality training data and self-paced control. Six animal subjects demonstrate that a multiple state classification task, loosely based on the standard centerout task, can be accomplished with fewer than five engaged neurons while requiring less than ten trials for algorithm training. In addition, untrained animals quickly obtained accurate device control utilizing local field potentials as well as neurons in cingulate cortex, two non-traditional neural inputs.

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“…The multi-domain preprocessing module performed common average reference (CAR) [29] as spatial filter, wavelet packet decomposition (WPD) [27] as temporal-spectral filter, winsorisation [30], normalization and segment as temporal filter. In the feature extraction module, multi-domain features were being extracted from temporal, temporal-spectral & spatial domains using temporal energy entropy (TEE), wavelet transform (WT) [28] and independent component analysis (ICA), respectively [28].…”

Section: 3multi-domain Eeg Signal Processing Methodsmentioning

confidence: 99%

Study on Detection of P300 ERP Component in EEG Signals and Algorithms

Haider¹,

Jiang²,

Ashraf³

et al. 2017

Proceedings of the 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 20

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Abstract.P300 Event Related Potential (ERP) has drawn much interest in different application of psychological background. An endogenous ERP component, P300, has been widely inspected in detecting deception. Detecting P300 from EEG signals is hard to find because P300 is overloaded with noise and other EEG signals. We typically use the features of the amplitude and latency of P300 to recognize P300.In this paper we are presenting several methods for the efficient detection of P300 detection algorithms such as; reconstruction algorithm for small P300 wave using Independent Component Analysis (ICA), Extreme Learning Machine (ELM) method using BackPropagation network, Multi-domain EEG signal processing. Each detection method has good effectiveness but the complexation level is dissimilar for each algorithm based on the constraints and optimal features used at the classification stage. This survey will brings you algorithms for the P300 detection and give you detail idea about the use of them.

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Using a Common Average Reference to Improve Cortical Neuron Recordings From Microelectrode Arrays

Abstract: Ludwig KA, Miriani RM, Langhals NB, Joseph MD, Anderson DJ, Kipke DR. Using a common average reference to improve cortical neuron recordings from microelectrode arrays.

Cited by 375 publications

References 45 publications

Cerebral blood flow is decoupled from blood pressure and linked to EEG bursting after resuscitation from cardiac arrest

Cerebral blood flow is decoupled from blood pressure and linked to EEG bursting after resuscitation from cardiac arrest

Use of a Bayesian maximum-likelihood classifier to generate training data for brain–machine interfaces

Study on Detection of P300 ERP Component in EEG Signals and Algorithms

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