Wavelet methodology to improve single unit isolation in primary motor cortex cells

Ortiz-Rosario, Alexis; Adeli, Hojjat; Buford, John A.

doi:10.1016/j.jneumeth.2015.03.014

Cited by 49 publications

(10 citation statements)

References 60 publications

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“…Our classification accuracy was a bit (<2%) lower than some of the previous similar studies, 35 large because we tested two different MVCs, which could have induced most of the errors. Brain-computer interface [36][37][38][39][40] has been an active research area for the control of robotic devices, through the decoding of neural control signals directly from electroencephalogram (EEG) or individual cortical neuron discharge activities. Our myoelectric-based control approach can be complementary to these approaches for neural-machine interface.…”

Section: Pattern Recognitionmentioning

confidence: 99%

Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings

Dai

2019

Int. J. Neur. Syst.

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The human hand is capable of producing versatile yet precise movements largely owing to the complex neuromuscular systems that control our finger movement. This study seeks to quantify the spatial activation patterns of the forearm flexor muscles during individualized finger flexions. High-density (HD) surface electromyogram (sEMG) signals of forearm flexor muscles were obtained, and individual motor units were decomposed from the sEMG. Both macro-level spatial patterns of EMG activity and micro-level motor unit distributions were used to systematically characterize the forearm flexor activation patterns. Different features capturing the spatial patterns were extracted, and the unique patterns of forearm flexor activation were then quantified using pattern recognition approaches. We found that the forearm flexor spatial activation during the ring finger flexion was mostly distinct from other fingers, whereas the activation patterns of the middle finger were least distinguishable. However, all the different activation patterns can still be classified in high accuracy (94-100%) using pattern recognition. Our findings indicate that the partial overlapping of neural activation can limit accurate identification of specific finger movement based on limited recordings and sEMG features, and that HD sEMG recordings capturing detailed spatial activation patterns at both macro- and micro-levels are needed.

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Section: Pattern Recognitionmentioning

confidence: 99%

Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings

Dai

2019

Int. J. Neur. Syst.

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“…This trigger ensured that only action potentials that were below −0.75 mV were accepted for isolation. The individual action potentials were then analyzed using a principal component analysis (PCA) algorithm (Ghosh-Dastidar et al, 2008;Ortiz-Rosario et al, 2015). The first two components of PCA are plotted in Figure 2A.…”

Section: Methodsmentioning

confidence: 99%

“…The first two components of PCA are plotted in Figure 2A. Each action potential in the 2D feature space was clustered using the Gaussian mixture model (GMM) (Ortiz-Rosario et al, 2015). The resulting clusters, in this example 3, were identified as individual neurons.…”

Section: Methodsmentioning

confidence: 99%

MUSIC-Expected maximization gaussian mixture methodology for clustering and detection of task-related neuronal firing rates

Ortiz-Rosario

Adeli

Buford

2017

Behavioural Brain Research

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Researchers often rely on simple methods to identify involvement of neurons in a particular motor task. The historical approach has been to inspect large groups of neurons and subjectively separate neurons into groups based on the expertise of the investigator. In cases where neuron populations are small it is reasonable to inspect these neuronal recordings and their firing rates carefully to avoid data omissions. In this paper, a new methodology is presented for automatic objective classification of neurons recorded in association with behavioral tasks into groups. By identifying characteristics of neurons in a particular group, the investigator can then identify functional classes of neurons based on their relationship to the task. The methodology is based on integration of a multiple signal classification (MUSIC) algorithm to extract relevant features from the firing rate and an expectation-maximization Gaussian mixture algorithm (EM-GMM) to cluster the extracted features. The methodology is capable of identifying and clustering similar firing rate profiles automatically based on specific signal features. An empirical wavelet transform (EWT) was used to validate the features found in the MUSIC pseudospectrum and the resulting signal features captured by the methodology. Additionally, this methodology was used to inspect behavioral elements of neurons to physiologically validate the model. This methodology was tested using a set of data collected from awake behaving non-human primates.

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“…This result opens new perspectives for Brain Computer Interface (BCI). The key point of BCI, indeed, is the capability of the decoder to discriminate meaningful signal features at single-trial level and many studies have been focused precisely on improving the features extraction [52], [53], [54], [55], [56], [57], [58], [59]. He Bin and collaborators have clearly shown the benefits from adopting a source analysis approach to classify motor imagery tasks in humans [15], [60].…”

Section: Micro-sources and Brain Computer Interfacementioning

confidence: 99%

Independent Component Decomposition of Human Somatosensory Evoked Potentials Recorded by Micro-Electrocorticography

Rembado

Castagnola

Turella

et al. 2017

Int. J. Neur. Syst.

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High-density surface microelectrodes for electrocorticography (ECoG) have become more common in recent years for recording electrical signals from the cortex. With an acceptable invasiveness/signal fidelity trade-off and high spatial resolution, micro-ECoG is a promising tool to resolve fine task-related spatial-temporal dynamics. However, volume conduction - not a negligible phenomenon - is likely to frustrate efforts to obtain reliable and resolved signals from a sub-millimeter electrode array. To address this issue, we performed an independent component analysis (ICA) on micro-ECoG recordings of somatosensory-evoked potentials (SEPs) elicited by median nerve stimulation in three human patients undergoing brain surgery for tumor resection. Using well-described cortical responses in SEPs, we were able to validate our results showing that the array could segregate different functional units possessing unique, highly localized spatial distributions. The representation of signals through the root-mean-square (rms) maps and the signal-to-noise ratio (SNR) analysis emphasizes the advantages of adopting a source analysis approach on micro-ECoG recordings in order to obtain a clear picture of cortical activity. The implications are twofold: while on one side ICA may be used as a spatial-temporal filter extracting micro-signal components relevant to tasks for brain-computer interface (BCI) applications, it could also be adopted to accurately identify the sites of nonfunctional regions for clinical purposes.

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Wavelet methodology to improve single unit isolation in primary motor cortex cells

Cited by 49 publications

References 60 publications

Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings

Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings

MUSIC-Expected maximization gaussian mixture methodology for clustering and detection of task-related neuronal firing rates

Independent Component Decomposition of Human Somatosensory Evoked Potentials Recorded by Micro-Electrocorticography

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