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Technologies
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Brain-Machine Interface Technology
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Brain-Machine Interface Technology

A special issue of Technologies (ISSN 2227-7080).

Deadline for manuscript submissions: closed (30 June 2016) | Viewed by 21550

Special Issue Editor

Dr. Mikhail A. Lebedev

E-Mail Website
Guest Editor
Faculty of Mechanics and Mathematics, Moscow State University, 119991 Moscow, Russia
Interests: neuroscience; neurobiology; neurophysiology; brain-machine interfaces; motor control; multielectrode recordings; microstimulation

Special Issue Information

Dear Colleagues,

Brain-machine interface (BMI) is a technology for decoding brain signals and using them for direct communication between the brain and external devices. BMIs are expected to revolutionize medical treatment of neurological disabilities caused by trauma and disease. Patients may benefit from BMIs connecting intact brain areas to assistive devices, such as neuroprosthetic limbs, wheelchairs that respond to brain commands, and artificial sensors that assist people with sensory disabilities. BMIs have experienced an explosive development during the last decade. This development was facilitated by advances in neural recording methods, computational approaches to decoding neural signals, computer technologies and robotic engineering.

Modern BMIs utilize a variety of noninvasive and intracranial recording methods. They can be classified into three main types: sensory, motor and bidirectional, for enabling sensory, motor and sensorimotor functions, respectively. More recently, cognitive BMIs have emerged that decode neural signals involved in higher brain functions, such as decision making, memory and attention. Moreover, brain-to-brain interfaces have been recently introduced for direct information exchange between individual brains.

In this special issue we invite articles on all aspects of BMI technologies: from recording methods to decoding algorithms and clinical applications. We intend to cover BMIs for motor function, senses (e.g., vision, hearing, proprioception, and smell), cognition, and social interaction. Both animal and human studies are invited.

Dr. Mikhail A. Lebedev
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • brain machine interface
  • neuroprosthetics
  • neural prosthesis
  • neurological disability
  • neurological trauma
  • sensory disability
  • social interaction
  • neural computations

Published Papers (3 papers)

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Research

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3855 KiB  
Article
Handcrafted Electrocorticography Electrodes for a Rodent Behavioral Model
by Nishat Tasnim, Ali Ajam, Raul Ramos, Mukhesh K. Koripalli, Manisankar Chennamsetti and Yoonsu Choi
Technologies 2016, 4(3), 23; https://doi.org/10.3390/technologies4030023 - 16 Aug 2016
Cited by 1 | Viewed by 7163
Abstract
Electrocorticography (ECoG) is a minimally invasive neural recording method that has been extensively used for neuroscience applications. It has proven to have the potential to ease the establishment of proper links for neural interfaces that can offer disabled patients an alternative solution for [...] Read more.
Electrocorticography (ECoG) is a minimally invasive neural recording method that has been extensively used for neuroscience applications. It has proven to have the potential to ease the establishment of proper links for neural interfaces that can offer disabled patients an alternative solution for their lost sensory and motor functions through the use of brain-computer interface (BCI) technology. Although many neural recording methods exist, ECoG provides a combination of stability, high spatial and temporal resolution with chronic and mobile capabilities that could make BCI systems accessible for daily applications. However, many ECoG electrodes require MEMS fabricating techniques which are accompanied by various expenses that are obstacles for research projects. For this reason, this paper presents an animal study using a low cost and simple handcrafted ECoG electrode that is made of commercially accessible materials. The study is performed on a Lewis rat implanted with a handcrafted 32-channel non-penetrative ECoG electrode covering an area of 3 × 3 mm2 on the cortical surface. The ECoG electrodes were placed on the motor and somatosensory cortex to record the signal patterns while the animal was active on a treadmill. Using a Tucker-Davis Technologies acquisition system and the software Synapse to monitor and analyze the electrophysiological signals, the electrodes obtained signals within the amplitude range of 200 µV for local field potentials with reliable spatiotemporal profiles. It was also confirmed that the handcrafted ECoG electrode has the stability and chronic features found in other commercial electrodes. Full article
(This article belongs to the Special Issue Brain-Machine Interface Technology)
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2532 KiB  
Article
Designing Closed-Loop Brain-Machine Interfaces Using Model Predictive Control
by Gautam Kumar, Mayuresh V. Kothare, Nitish V. Thakor, Marc H. Schieber, Hongguang Pan, Baocang Ding and Weimin Zhong
Technologies 2016, 4(2), 18; https://doi.org/10.3390/technologies4020018 - 22 Jun 2016
Cited by 1 | Viewed by 5572
Abstract
Brain-machine interfaces (BMIs) are broadly defined as systems that establish direct communications between living brain tissue and external devices, such as artificial arms. By sensing and interpreting neuronal activities to actuate an external device, BMI-based neuroprostheses hold great promise in rehabilitating motor disabled [...] Read more.
Brain-machine interfaces (BMIs) are broadly defined as systems that establish direct communications between living brain tissue and external devices, such as artificial arms. By sensing and interpreting neuronal activities to actuate an external device, BMI-based neuroprostheses hold great promise in rehabilitating motor disabled subjects, such as amputees. In this paper, we develop a control-theoretic analysis of a BMI-based neuroprosthetic system for voluntary single joint reaching task in the absence of visual feedback. Using synthetic data obtained through the simulation of an experimentally validated psycho-physiological cortical circuit model, both the Wiener filter and the Kalman filter based linear decoders are developed. We analyze the performance of both decoders in the presence and in the absence of natural proprioceptive feedback information. By performing simulations, we show that the performance of both decoders degrades significantly in the absence of the natural proprioception. To recover the performance of these decoders, we propose two problems, namely tracking the desired position trajectory and tracking the firing rate trajectory of neurons which encode the proprioception, in the model predictive control framework to design optimal artificial sensory feedback. Our results indicate that while the position trajectory based design can only recover the position and velocity trajectories, the firing rate trajectory based design can recover the performance of the motor task along with the recovery of firing rates in other cortical regions. Finally, we extend our design by incorporating a network of spiking neurons and designing artificial sensory feedback in the form of a charged balanced biphasic stimulating current. Full article
(This article belongs to the Special Issue Brain-Machine Interface Technology)
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Review

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Review
Neural Operant Conditioning as a Core Mechanism of Brain-Machine Interface Control
by Yoshio Sakurai and Kichan Song
Technologies 2016, 4(3), 26; https://doi.org/10.3390/technologies4030026 - 26 Aug 2016
Cited by 5 | Viewed by 8382
Abstract
The process of changing the neuronal activity of the brain to acquire rewards in a broad sense is essential for utilizing brain-machine interfaces (BMIs), which is essentially operant conditioning of neuronal activity. Currently, this is also known as neural biofeedback, and it is [...] Read more.
The process of changing the neuronal activity of the brain to acquire rewards in a broad sense is essential for utilizing brain-machine interfaces (BMIs), which is essentially operant conditioning of neuronal activity. Currently, this is also known as neural biofeedback, and it is often referred to as neurofeedback when human brain activity is targeted. In this review, we first illustrate biofeedback and operant conditioning, which are methodological background elements in neural operant conditioning. Then, we introduce research models of neural operant conditioning in animal experiments and demonstrate that it is possible to change the firing frequency and synchronous firing of local neuronal populations in a short time period. We also debate the possibility of the application of neural operant conditioning and its contribution to BMIs. Full article
(This article belongs to the Special Issue Brain-Machine Interface Technology)
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