An automated approach towards detecting complex behaviours in deep brain oscillations

Mace, Michael; Yousif, Nada; Naushahi, M.J.; Abdullah-Al-Mamun, Khondaker; Wang, Shouyan; Nandi, Dipankar; Vaidyanathan, Ravi

doi:10.1016/j.jneumeth.2013.11.019

Cited by 5 publications

(12 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To carry out the identification of finger movements from the LFP data, we used wavelet packet transform (WPT) and Hilbert Transform (HT) to extract the LFP signal features from different frequency bands in the frequency range from 0 to 90 Hz. For non-stationary biosignals such as LFPs, WPT is a better alternative as a data analysis tool than STFT or standard DWT in extracting relevant signal features for pattern recognition in the time-frequency domain [ 29 ].…”

Section: Methodology Of Feature Extraction Of Lfp Signals Using Wamentioning

confidence: 99%

Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

Islam

Mamun

Deng

2017

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs) for robust movement decoding of Parkinson's disease (PD) and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value) at about 0.729 ± 0.16 for decoding movement from the resting state and about 0.671 ± 0.14 for decoding left and right visually cued movements.

show abstract

Section: Methodology Of Feature Extraction Of Lfp Signals Using Wamentioning

confidence: 99%

Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

Islam

Mamun

Deng

2017

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Mace and others (2013) tailor ERP extraction to PD patients by adapting the threshold to detect the ERP resulting from movement without false signal detection due to noise from other brain signals occurring simultaneously. Their algorithm adapts the threshold according to a short-term energy-dependent detection contour along which the LFP value is determined.…”

Section: Brain–computer Interface For Neurological Rehabilitationmentioning

confidence: 99%

“…Their algorithm adapts the threshold according to a short-term energy-dependent detection contour along which the LFP value is determined. The threshold adaptation and signal detection are then encoded into a binary output that can be used for movement detection in neurosurgery as well as a BCI-driven diagnostic tool for PD (Mace and others 2013).…”

Section: Brain–computer Interface For Neurological Rehabilitationmentioning

confidence: 99%

Brain–Computer Interface after Nervous System Injury

2014

View full text Add to dashboard Cite

Brain-computer interface (BCI) has proven to be a useful tool for providing alternative communication and mobility to patients suffering from nervous system injury. BCI has been and will continue to be implemented into rehabilitation practices for more interactive and speedy neurological recovery. The most exciting BCI technology is evolving to provide therapeutic benefits by inducing cortical reorganization via neuronal plasticity. This article presents a state-of-the-art review of BCI technology used after nervous system injuries, specifically: amyotrophic lateral sclerosis, Parkinson's disease, spinal cord injury, stroke, and disorders of consciousness. Also presented is transcending, innovative research involving new treatment of neurological disorders.

show abstract

“…LFPs are predominantly generated by synaptic potentials in the vicinity of the electrode contact and represent the sum of the post-synaptic activity of many (approximately 200) neurons [22,23]. The inherent deep brain LFPs in the STN or GPi may be broadly subdivided into several frequency bands, namely, delta (0-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12), low beta (12)(13)(14)(15)(16)(17)(18)(19)(20), high beta (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), low gamma and high gamma (60-90 Hz) bands [14].…”

Section: Introductionmentioning

confidence: 99%

“…Both contra-and ipsi-lateral gamma band synchronization have also been found in STN LFPs during wrist extensions [16]. However, during less well-defined real-world tasks involving complex behaviours such as turning or balance, these frequency specific synchronizations can be hidden beneath intrinsic or extrinsic neuronal noise, have high variability and/or encompass other brain areas [25]. Instead, Basal ganglia STN activity can also be modulated by imaginary movements.…”

Section: Introductionmentioning

confidence: 99%

Movement decoding using neural synchronization and inter-hemispheric connectivity from deep brain local field potentials

et al. 2015

View full text Add to dashboard Cite

These findings demonstrate optimally selected neural synchronization alongside causality measures related to inter-hemispheric connectivity can provide an effective control signal for augmenting adaptive BMIs. In the case of DBS patients, acquiring such signals requires no additional surgery whilst providing a relatively stable and computationally inexpensive control signal. This has the potential to extend invasive BMI, based on recordings within the motor cortex, by providing additional information from subcortical regions.

show abstract

An automated approach towards detecting complex behaviours in deep brain oscillations

Cited by 5 publications

References 38 publications

Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

Brain–Computer Interface after Nervous System Injury

Movement decoding using neural synchronization and inter-hemispheric connectivity from deep brain local field potentials

Contact Info

Product

Resources

About