A hierarchical structure for human behavior classification using STN local field potentials

Golshan, Hosein M.; Hebb, Adam O.; Hanrahan, Sara J.; Nedrud, Joshua; Mahoor, Mohammad H.

doi:10.1016/j.jneumeth.2017.10.001

Cited by 21 publications

(25 citation statements)

References 40 publications

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“…Classifying human behavior from brain signals has also been explored in developing closed loop aDBS [75][76][77][78]. The real-time classification of human behaviors would allow for adaptively optimizing stimulation parameters per task.…”

Section: Adaptive Dbsmentioning

confidence: 99%

Machine Learning’s Application in Deep Brain Stimulation for Parkinson’s Disease: A Review

et al. 2020

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Deep brain stimulation (DBS) is a surgical treatment for advanced Parkinson’s disease (PD) that has undergone technological evolution that parallels an expansion in clinical phenotyping, neurophysiology, and neuroimaging of the disease state. Machine learning (ML) has been successfully used in a wide range of healthcare problems, including DBS. As computational power increases and more data become available, the application of ML in DBS is expected to grow. We review the literature of ML in DBS and discuss future opportunities for such applications. Specifically, we perform a comprehensive review of the literature from PubMed, the Institute for Scientific Information’s Web of Science, Cochrane Database of Systematic Reviews, and Institute of Electrical and Electronics Engineers’ (IEEE) Xplore Digital Library for ML applications in DBS. These studies are broadly placed in the following categories: (1) DBS candidate selection; (2) programming optimization; (3) surgical targeting; and (4) insights into DBS mechanisms. For each category, we provide and contextualize the current body of research and discuss potential future directions for the application of ML in DBS.

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Section: Adaptive Dbsmentioning

confidence: 99%

Machine Learning’s Application in Deep Brain Stimulation for Parkinson’s Disease: A Review

et al. 2020

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“…There are several options to minimize the additional power needed by the pulse generator and to eventually make battery replacement less frequent. First, comparing the computational demands of various signal processing and machine‐learning approaches should minimize the required power . Second, the possibility to perform analyses on external devices or cloud‐platform solutions should be evaluated.…”

Section: Future Considerationsmentioning

confidence: 99%

“…[10][11][12] However, others found a correlation between STN-LFP recordings and tremor. 13,14 Furthermore, freezing-of-gait periods 15 and differentiation between speech and movement activities 16 can be detected using STN-LFPs. Such differentiation of clinical indications underlines the potential of STN-LFP recordings as promising input signals, with the added benefit of not requiring additional implants or equipment compared to cDBS.…”

Section: Adbs Based On Electrophysiological Recordings Basal Ganglia mentioning

confidence: 99%

“…First, comparing the computational demands of various signal processing and machine-learning approaches should minimize the required power. 16 Second, the possibility to perform analyses on external devices or cloud-platform solutions should be evaluated. Energy saved by outsourcing these computations should be compared with the energy required of wireless data transfer.…”

Section: Adbs Modulation Other Than Amplitude Modulationmentioning

confidence: 99%

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An update on adaptive deep brain stimulation in Parkinson's disease

et al. 2018

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Advancing conventional open‐loop DBS as a therapy for PD is crucial for overcoming important issues such as the delicate balance between beneficial and adverse effects and limited battery longevity that are currently associated with treatment. Closed‐loop or adaptive DBS aims to overcome these limitations by real‐time adjustment of stimulation parameters based on continuous feedback input signals that are representative of the patient's clinical state. The focus of this update is to discuss the most recent developments regarding potential input signals and possible stimulation parameter modulation for adaptive DBS in PD. Potential input signals for adaptive DBS include basal ganglia local field potentials, cortical recordings (electrocorticography), wearable sensors, and eHealth and mHealth devices. Furthermore, adaptive DBS can be applied with different approaches of stimulation parameter modulation, the feasibility of which can be adapted depending on specific PD phenotypes. Implementation of technological developments like machine learning show potential in the design of such approaches; however, energy consumption deserves further attention. Furthermore, we discuss future considerations regarding the clinical implementation of adaptive DBS in PD. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

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“…Brain activities can be measured through invasive and noninvasive devices. So far, a wide research has been carried out on human behaviour and task classification using invasive techniques, such as electrocorticography (ECoG) [3] and Local Field Potentials (LFPs) [4]- [5]. In ECoG, the electrodes are placed directly on the exposed surface of the brain to record electrical activity and LFP refers to the electrical field recorded using a small-sized electrode in the extracellular space of brain tissue.…”

Section: Introductionmentioning

confidence: 99%

Motor Imagery EEG Signal Processing and Classification using Machine Learning Approach

Sreeja¹,

Samanta²,

Mitra³

et al. 2018

JJCIT

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A hierarchical structure for human behavior classification using STN local field potentials

Cited by 21 publications

References 40 publications

Machine Learning’s Application in Deep Brain Stimulation for Parkinson’s Disease: A Review

Machine Learning’s Application in Deep Brain Stimulation for Parkinson’s Disease: A Review

An update on adaptive deep brain stimulation in Parkinson's disease

Motor Imagery EEG Signal Processing and Classification using Machine Learning Approach

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