@article {4436,
	title = {The Evoked Potential Operant Conditioning System (EPOCS): A Research Tool and an Emerging Therapy for Chronic Neuromuscular Disorders.},
	journal = {J Vis Exp},
	year = {2022},
	month = {2022 08 25},
	abstract = {<p>The Evoked Potential Operant Conditioning System (EPOCS) is a software tool that implements protocols for operantly conditioning stimulus-triggered muscle responses in people with neuromuscular disorders, which in turn can improve sensorimotor function when applied appropriately. EPOCS monitors the state of specific target muscles-e.g., from surface electromyography (EMG) while standing, or from gait cycle measurements while walking on a treadmill-and automatically triggers calibrated stimulation when pre-defined conditions are met. It provides two forms of feedback that enable a person to learn to modulate the targeted pathway{\textquoteright}s excitability. First, it continuously monitors ongoing EMG activity in the target muscle, guiding the person to produce a consistent level of activity suitable for conditioning. Second, it provides immediate feedback of the response size following each stimulation and indicates whether it has reached the target value. To illustrate its use, this article describes a protocol through which a person can learn to decrease the size of the Hoffmann reflex-the electrically-elicited analog of the spinal stretch reflex-in the soleus muscle. Down-conditioning this pathway{\textquoteright}s excitability can improve walking in people with spastic gait due to incomplete spinal cord injury. The article demonstrates how to set up the equipment; how to place stimulating and recording electrodes; and how to use the free software to optimize electrode placement, measure the recruitment curve of direct motor and reflex responses, measure the response without operant conditioning, condition the reflex, and analyze the resulting data. It illustrates how the reflex changes over multiple sessions and how walking improves. It also discusses how the system can be applied to other kinds of evoked responses and to other kinds of stimulation, e.g., motor evoked potentials to transcranial magnetic stimulation; how it can address various clinical problems; and how it can support research studies of sensorimotor function in health and disease.</p>},
	keywords = {Chronic Disease, Conditioning, Operant, Electromyography, Evoked Potentials, H-Reflex, Humans, Neuromuscular Diseases, Spinal Cord Injuries},
	issn = {1940-087X},
	doi = {10.3791/63736},
	author = {Hill, N Jeremy and Gupta, Disha and Eftekhar, Amir and Brangaccio, Jodi A and Norton, James J S and McLeod, Michelle and Fake, Tim and Wolpaw, Jonathan R and Thompson, Aiko K}
}
@article {2845,
	title = {Seizure prediction for epilepsy using a multi-stage phase synchrony based system.},
	journal = {Conf Proc IEEE Eng Med Biol Soc},
	volume = {2009},
	year = {2009},
	month = {09/2009},
	pages = {25-8},
	abstract = {Seizure onset prediction in epilepsy is a challenge which is under investigation using many and varied signal processing techniques. Here we present a multi-stage phase synchrony based system that brings to bear the advantages of many techniques in each substage. The 1(st) stage of the system unmixes continuous long-term (2-4 days) multichannel scalp EEG using spatially constrained Independent Component Analysis and estimates the long term significant phase synchrony dynamics of narrowband (2-8 Hz and 8-14 Hz) seizure components. It then projects multidimensional features onto a 2-D map using Neuroscale and evaluates the probability of predictive events using Gaussian Mixture Models. We show the possibility of seizure onset prediction within a prediction window of 35-65 minutes with a sensitivity of 65-100\% and specificity of 65-80\% across epileptic patients.},
	keywords = {Algorithms, Artificial Intelligence, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Humans, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity},
	issn = {1557-170X},
	doi = {10.1109/IEMBS.2009.5334898},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/19965104},
	author = {Christopher J James and Disha Gupta}
}
@article {2844,
	title = {Narrowband vs. broadband phase synchronization analysis applied to independent components of ictal and interictal EEG.},
	journal = {Conf Proc IEEE Eng Med Biol Soc},
	volume = {2007},
	year = {2007},
	month = {08/2007},
	pages = {3864-7},
	abstract = {This paper presents a comparison of the use of broadband and narrow band signals for phase synchronization analysis as applied to Independent Components of ictal and interictal scalp EEG in the context of seizure onset detection and prediction. Narrow band analysis for phase synchronization is found to be better performed in the present context than the broad band signal analysis. It has been observed that the phase synchronization of Independent Components in a narrow band (particularly the Gamma band) shows a prominent trend of increasing and decreasing synchronization at seizure onset near the epileptogenic area (spatially). This information is not always found to be consistent in analysis with the raw EEG signals, which may show spurious synchronization happening due to volume conduction effects. These observations lead us to believe that tracking changes in phase synchronization of narrow band activity, on continuous data records will be of great value in the context of seizure prediction.},
	keywords = {Algorithms, Electroencephalography, Humans, Predictive Value of Tests, Seizures, Signal Processing, Computer-Assisted},
	issn = {1557-170X},
	doi = {10.1109/IEMBS.2007.4353176},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/18002842},
	author = {Disha Gupta and Christopher J James}
}
@article {2846,
	title = {Space-time ICA versus Ensemble ICA for ictal EEG analysis with component differentiation via Lempel-Ziv complexity.},
	journal = {Conf Proc IEEE Eng Med Biol Soc},
	volume = {08/2007},
	year = {2007},
	month = {2007},
	pages = {5473-6},
	abstract = {In this proof-of-principle study we analyzed intracranial electroencephalogram recordings in patients with intractable focal epilepsy. We contrast two implementations of Independent Component Analysis (ICA) - Ensemble (or spatial) ICA (E-ICA) and Space-Time ICA (ST-ICA) in separating out the ictal components underlying the measurements. In each case we assess the outputs of the ICA algorithms by means of a non-linear method known as the Lempel-Ziv (LZ) complexity. LZ complexity quantifies the complexity of a time series and is well suited to the analysis of non-stationary biomedical signals of short length. Our results show that for small numbers of intracranial recordings, standard E-ICA results in marginal improvements in the separation as measured by the LZ complexity changes. ST-ICA using just 2 recording channels both near and far from the epileptic focus result in more distinct ictal components--although at this stage there is a subjective element to the separation process for ST-ICA. Our results are promising showing that it is possible to extract meaningful information from just 2 recording electrodes through ST-ICA, even if they are not directly over the seizure focus. This work is being further expanded for seizure onset analysis.},
	keywords = {Algorithms, Artificial Intelligence, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Humans, Pattern Recognition, Automated, Principal Component Analysis, Reproducibility of Results, Sensitivity and Specificity},
	issn = {1557-170X},
	doi = {10.1109/IEMBS.2007.4353584},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/18003250},
	author = {Christopher J James and Ab{\'a}solo, Daniel and Disha Gupta}
}
