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Journal of Neural Engineering

Ramin Bighamian, Yan Tat Wong, Bijan Pesaran, Maryam M Shanechi
OBJECTIVE: Behavior is encoded across multiple scales of brain activity, from binary neuronal spikes to continuous fields including local field potentials (LFP). Multiscale models need to describe both the encoding of behavior and the conditional dependencies in simultaneously recorded spike and field signals, which form a high-dimensional multiscale network. However, learning spike-field dependencies in high-dimensional recordings is challenging due to the prohibitively large number of spike-field signal pairs, which makes standard learning techniques subject to overfitting...
May 17, 2019: Journal of Neural Engineering
Caroline Witton, Sergey Sergeyev, Elena Turitsyna, Paul L Furlong, Stefano Seri, Matthew J Brookes, Sergei K Turitsyn
Brain electromagnetic activity in patients with epilepsy is characterized by abnormal high-amplitude transient events (spikes) and abnormal patterns of synchronization of brain rhythms that accompany epileptic seizures. With the aim of improving methods for identifying epileptogenic sources in magnetoencephalographic (MEG) recordings of brain data, we applied methods previously used in the study of oceanic 'rogue waves' and other freak events in complex systems 
 Approach. For data from 3 patients who were awaiting surgical treatment for epilepsy, we used a beamformer source-model to produce volumetric maps showing areas with a high proportion of spikes that could be classified as 'rogue waves', and areas with high Hurst Exponent (HE)...
May 17, 2019: Journal of Neural Engineering
Yuxiao Yang, Omid Sani, Edward F Chang, Maryam M Shanechi
OBJECTIVE: Developing dynamic network models for multisite electrocorticogram (ECoG) activity can help study neural representations and design neurotechnologies in humans given the clinical promise of ECoG. However, dynamic network models have so far largely focused on spike recordings rather than ECoG. A dynamic network model for ECoG recordings, which constitute a network, should describe their temporal dynamics while also achieving dimensionality reduction given the inherent spatial and temporal correlations...
May 16, 2019: Journal of Neural Engineering
Konstantinos Ilias Georgiadis, Nikolaos Laskaris, Spiros Nikolopoulos, Yiannis Kompatsiaris
Graph signal processing concepts are exploited for brain activity decoding and particularly the detection and recognition of a Motor Imagery (MI) movement. A novel signal analytic technique that combines Graph Fourier Transform (GFT) with estimates of cross-frequency coupling and discriminative learning is introduced as a means to recover the subject's intention from the multichannel signal.
 Approach. Adopting a multi-view perspective, based on the popular concept of co-existing and interacting brain rhythms, a multilayer network model is first built from empirical data and its connectivity graph is used to derive the GFT-basis...
May 16, 2019: Journal of Neural Engineering
Francesco Marini, Clement Lee, Johanna Wagner, Scott Makeig, Mateusz Gola
Electroencephalography (EEG) is widely used by clinicians, scientists, engineers and other professionals worldwide, with an increasing number of low-cost, commercially-oriented EEG systems that have become available in recent years. One such system is the Cognionics Quick-20 (Cognionics Inc., San Diego, USA), which uses dry electrodes and offers the convenience of portability thanks to its built-in amplifier and wireless connection. Because of such characteristics, this system has been used in several applications for both clinical and basic research studies...
May 16, 2019: Journal of Neural Engineering
Masaaki Hayashi, Shohei Tsuchimoto, Nobuaki Mizuguchi, Mizuki Miyatake, Shoko Kasuga, Junichi Ushiba
OBJECTIVE: A critical feature for the maintenance of precise skeletal muscle force production by the human brain is its ability to configure motor function activity dynamically and adaptively in response to visual and somatosensory information. Existing studies have concluded that not only the sensorimotor area but also distributed cortical areas act cooperatively in the generation of motor commands for voluntary force production to the desired level. However, less attention has been paid to such physiological mechanisms in conventional brain-computer interface (BCI) design and implementation...
May 16, 2019: Journal of Neural Engineering
Ren Xu, Strahinja Dosen, Ning Jiang, Lin Yao, Asma Farooq, Mads Jochumsen, Natalie Mrachacz-Kersting, Kim Dremstrup, Dario Farina
OBJECTIVE: Brain computer interfacing (BCI) is a promising method to control assistive systems for patients with severe disabilities. However, only a small number of commands (2 to 3) can be discriminated from EEG signals. Recently, we have presented a novel BCI approach that combines an electrotactile menu and a brain switch, which allows the user to trigger many commands robustly and efficiently. However, the commands are timed to periodic tactile cues and this may challenge online control...
May 10, 2019: Journal of Neural Engineering
Zeinab Mohammadi, John Michael Kincaid, Sio Hang Pun, Achim Klug, Chao Liu, Tim Chifong Lei
OBJECTIVE: Real-time closed-loop neural feedback control requires the analysis of action potential traces within several milliseconds after they have been recorded from the brain. The current generation of spike clustering algorithms were mostly designed for off-line use and also require a significant amount of computational resources. A new spike clustering algorithm, termed "Enhanced Growing Neural Gas (EGNG)", was therefore developed that is computationally lightweight and memory conserved...
May 9, 2019: Journal of Neural Engineering
Benshen Song, Ningning Ma, Guangyao Liu, Haochuan Zhang, Lianchun Yu, LiWei Liu, Jing Zhang
OBJECTIVE: The exploration of time-varying functional connectivity (FC) through human neuroimaging techniques provides important new insights on the spatio-temporal organization of functional communication in the brain's networks and its alterations in diseased brains. However, little is known about the underlying dynamic mechanism with which such a dynamic FC is flexibly organized under the constraint of structural connections. In this work, we explore the relationship between critical dynamics and FC flexibility based on both functional magnetic resonance imaging data and computer models...
May 9, 2019: Journal of Neural Engineering
Yu Huang, Abhishek Datta, Marom Bikson, Lucas C Parra
Research in the area of transcranial electrical stimulation (TES) often relies on computational models of current flow in the brain. Models are built based on magnetic resonance images (MRI) of the human head to capture detailed individual anatomy. To simulate current flow on an individual, the subject's MRI is segmented, virtual electrodes are placed on this anatomical model, the volume is tessellated into a mesh, and a finite element model (FEM) is solved numerically to estimate the current flow. Various software tools are available for each of these steps, as well as processing pipelines that connect these tools for automated or semi-automated processing...
May 9, 2019: Journal of Neural Engineering
Shangen Zhang, Xiaorong Gao
OBJECTIVE: In many cases, noise in visual stimuli plays an active role in brain information processing. Electroencephalogram (EEG) provides an objective mean to measure brain cognition and information processing, and studies on the effect of noise on EEG can help us better understand the mechanisms involved in information processing. APPROACH: In this study, visual stimuli, consisting of images with different noise levels, were created using the phase-scrambled method...
May 3, 2019: Journal of Neural Engineering
Jessica D Falcone, Harbaljit Sohal, Themis Kyriakides, Ravi V Bellamkonda
OBJECTIVE: Successful application of chronic intracortical electrodes remains highly variable. The biological mechanisms leading to electrode failure are still being explored. Recent work has shown a correlation between blood-brain barrier (BBB) integrity and long-term recordings. Here we proposed to modulate the BBB healing after intracortical electrode implantation, while evaluating the functional electrophysiology. The CCL2/CCR2 pathway was chosen based on previous work demonstrating the positive histological effects in an intracortical electrode model, as well as in other neurodegenerative models...
May 2, 2019: Journal of Neural Engineering
Sabine Haumann, Günther Bauernfeind, Magnus Johannes Teschner, Irina Schierholz, Martin G Bleichner, Andreas Büchner, Thomas Lenarz

 In the long term it is desirable for CI users to control their device via brain signals. A possible strategy is the use of auditory evoked potentials (AEPs). Several studies have shown the suitability of auditory paradigms for such an approach. However, these
 investigations are based on non-invasive recordings. When thinking about everyday life applications, it would be more convenient to use implanted electrodes for signal acquisition. Ideally, the electrodes would be directly integrated into the CI...
May 1, 2019: Journal of Neural Engineering
Muhammad Saif-Ur-Rehman, Robin Lienkämper, Yaroslav Parpaley, Jörg Wellmer, Charles Liu, Brian Lee, Spencer Kellis, Richard A Andersen, Ioannis Iossifidis, Tobias Glasmachers, Christian Klaes
OBJECTIVE: In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed...
May 1, 2019: Journal of Neural Engineering
Shriya Srinivasan, Booker Schelhaas, Benjamin E Maimon, Hyungeun Song, Hugh M Herr
OBJECTIVE: Over the last two decades, optical control of neuronal activity in the central nervous system has seen rapid growth, demonstrating the utility of optogenetics as both an experimental and therapeutic tool. Conversely, applications of optogenetics in the peripheral nervous system have been relatively constrained by the challenges of temporally variable opsin expression, light penetration and immune attack of non-native opsins. Whilst opsin expression can be increased significantly through high concentration viral induction, subsequent attack by the immune system causes temporal decay and high variability in electrophysiological response...
April 30, 2019: Journal of Neural Engineering
Fernando Llanos, Zilong Xie, Bharath Chandrasekaran
We investigated the biometric specificity of the frequency following response (FFR), an EEG marker of early auditory processing that reflects phase-locked activity from neural ensembles in the auditory cortex and subcortex (Bidelman, 2015a, 2018; Chandrasekaran & Kraus, 2010; Coffey et al., 2017). Our objective is two-fold: demonstrate that the FFR contains information beyond stimulus properties and broad group-level markers, and to assess the practical viability of the FFR as a biometric across different sounds, auditory experiences, and recording days...
April 30, 2019: Journal of Neural Engineering
Ana Guadalupe Hernandez-Reynoso, Shrenevas Nandam, Jonathan M O'Brien, Aswini Kanneganti, Stuart Cogan, Daniel Freeman, Mario Romero-Ortega
Recent developments in peripheral nerve electrodes allow the efficient and selective neuromodulation of somatic and autonomic nerves, which has proven beneficial in specific bioelectronic medical applications. However, current most clinical devices are wired and powered by implantable batteries which suffer from several limitations. We recently developed a sub-millimeter inductively powered neural stimulator (electroparticle; EP), and in this study, we report the integration of the EP onto commercial cuff electrodes (EP-C) allowing the wireless activation of peripheral nerves...
April 24, 2019: Journal of Neural Engineering
Alexandra Joshi-Imre, Bryan James Black, Justin Abbott, Aswini Kanneganti, Rashed Rihani, Bitan Chakraborty, Vindhya Reddy Danda, Jimin Maeng, Rohit Sharma, Loren Rieth, Sandeep Negi, Joseph J Pancrazio, Stuart Cogan
Clinical applications of implantable microelectrode arrays are currently limited by device failure due to, in part, mechanical and electrochemical failure modes. To overcome this challenge, there is significant research interest in the exploration of novel array architectures and encapsulation materials. Amorphous silicon carbide (a-SiC) is biocompatible and corrosion resistant, and has recently been employed as a coating on biomedical devices including planar microelectrode arrays. However, to date, the three-dimensional Utah electrode array (UEA) is the only array architecture which has been approved by the FDA for long-term human trials...
April 23, 2019: Journal of Neural Engineering
Bret Robert Kenny, Brian Veitch, Sarah D Power
This study explored the classification of electroencephalography (EEG) signals to assess changes in neural activity as individuals performed a training task in a virtual environment simulator. Commonly, task behavior and perception are used to assess a trainee's ability to perform a task, however, changes in cognition are not usually measured and could be important to provide a true indication of an individual's level of knowledge or skill. In this study, 15 participants acquired spatial knowledge via 60 navigation trials (divided into 10 blocks) in a novel virtual environment...
April 17, 2019: Journal of Neural Engineering
Marko Angjelichinoski, Taposh Banerjee, John Choi, Bijan Pesaran, Vahid Tarokh
OBJECTIVE: We consider the problem of predicting eye movement goals from local field potentials(LFP) recorded through a multielectrode array in the macaque prefrontal cortex. The monkey is tasked with performing memory-guided saccades to one of eight targets during which LFP activity is recorded and used to train a decoder. APPROACH: Previous reports have mainly relied on the spectral amplitude of the LFPs as decoding feature, while neglecting the phase without proper theoretical justification...
April 16, 2019: Journal of Neural Engineering
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