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Special Issue "Mind-Controlled Robotics"

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A special issue of Micromachines (ISSN 2072-666X).

Deadline for manuscript submissions: closed (30 June 2015)

Special Issue Editor

Guest Editor
Prof. Dr. Dean M. Aslam

Electrical and Computer Engineering Department, 2120 EB, Michigan State University, E. Lansing, MI 48824, USA
Website | E-Mail
Phone: 517-353-6329
Fax: +1 517 353 1980
Interests: neural studies using microprobes; biochemical nanosensors; polycrystalline diamond (poly-C) sensors and MEMS (Bio; RF; Packaging); hands-on Lego-robotics modules for K-12; UG and graduate teaching

Special Issue Information

Dear Colleagues,

Inexpensive, non-invasive, and single-electrode EEG (electroencephalogram) technologies will play a key role in the following application areas: mind-controlled robots, drones, prosthetics, personal healthcare systems, smart homes, and smart hospitals/nursing-homes. Therefore, developing non-invasive and inexpensive EEGs and EMGs (electromyogram), based on wearable systems, is very important. Such technologies should benefit from the latest micro- and nanotechnologies. The Special Issue solicits original papers related to the title below.

Title: Non-invasive Mind-control of Robots and Other Systems Using Inexpensive EEG/EMG Electrodes

Prof. Dr. Dean M. Aslam
Guest Editor

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines 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 1000 CHF (Swiss Francs).


Keywords

  • brainwaves, their generation and uses
  • new electrode technologies for EEG/EMG
  • non-invasive and inexpensive brain computer interfaces
  • miniaturization of mind-controlled and wireless systems applications
  • mind-controlled robots, drones, prosthetics, personal healthcare systems, smart homes

Related Special Issue

Published Papers (2 papers)

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Research

Open AccessArticle sBCI-Headset—Wearable and Modular Device for Hybrid Brain-Computer Interface
Micromachines 2015, 6(3), 291-311; doi:10.3390/mi6030291
Received: 25 August 2014 / Revised: 24 December 2014 / Accepted: 2 February 2015 / Published: 27 February 2015
PDF Full-text (2073 KB) | HTML Full-text | XML Full-text
Abstract
Severely disabled people, like completely paralyzed persons either with tetraplegia or similar disabilities who cannot use their arms and hands, are often considered as a user group of Brain Computer Interfaces (BCI). In order to achieve high acceptance of the BCI by this
[...] Read more.
Severely disabled people, like completely paralyzed persons either with tetraplegia or similar disabilities who cannot use their arms and hands, are often considered as a user group of Brain Computer Interfaces (BCI). In order to achieve high acceptance of the BCI by this user group and their supporters, the BCI system has to be integrated into their support infrastructure. Critical disadvantages of a BCI are the time consuming preparation of the user for the electroencephalography (EEG) measurements and the low information transfer rate of EEG based BCI. These disadvantages become apparent if a BCI is used to control complex devices. In this paper, a hybrid BCI is described that enables research for a Human Machine Interface (HMI) that is optimally adapted to requirements of the user and the tasks to be carried out. The solution is based on the integration of a Steady-state visual evoked potential (SSVEP)-BCI, an Event-related (de)-synchronization (ERD/ERS)-BCI, an eye tracker, an environmental observation camera, and a new EEG head cap for wearing comfort and easy preparation. The design of the new fast multimodal BCI (called sBCI) system is described and first test results, obtained in experiments with six healthy subjects, are presented. The sBCI concept may also become useful for healthy people in cases where a “hands-free” handling of devices is necessary. Full article
(This article belongs to the Special Issue Mind-Controlled Robotics)
Figures

Open AccessArticle Executed Movement Using EEG Signals through a Naive Bayes Classifier
Micromachines 2014, 5(4), 1082-1105; doi:10.3390/mi5041082
Received: 15 May 2014 / Revised: 15 October 2014 / Accepted: 30 October 2014 / Published: 13 November 2014
Cited by 3 | PDF Full-text (1802 KB) | HTML Full-text | XML Full-text
Abstract
Recent years have witnessed a rapid development of brain-computer interface (BCI) technology. An independent BCI is a communication system for controlling a device by human intension, e.g., a computer, a wheelchair or a neuroprosthes is, not depending on the brain’s normal output pathways
[...] Read more.
Recent years have witnessed a rapid development of brain-computer interface (BCI) technology. An independent BCI is a communication system for controlling a device by human intension, e.g., a computer, a wheelchair or a neuroprosthes is, not depending on the brain’s normal output pathways of peripheral nerves and muscles, but on detectable signals that represent responsive or intentional brain activities. This paper presents a comparative study of the usage of the linear discriminant analysis (LDA) and the naive Bayes (NB) classifiers on describing both right- and left-hand movement through electroencephalographic signal (EEG) acquisition. For the analysis, we considered the following input features: the energy of the segments of a band pass-filtered signal with the frequency band in sensorimotor rhythms and the components of the spectral energy obtained through the Welch method. We also used the common spatial pattern (CSP) filter, so as to increase the discriminatory activity among movement classes. By using the database generated by this experiment, we obtained hit rates up to 70%. The results are compatible with previous studies. Full article
(This article belongs to the Special Issue Mind-Controlled Robotics)

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