Non-invasive Control of a Intelligent Room Using EEG Signals

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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Advanced Micro-Actuator/Robot Fabrication Using Ultrafast Laser Direct Writing and Its Remote Control

Applied Sciences ◽

10.3390/app10238563 ◽

2020 ◽

Vol 10 (23) ◽

pp. 8563

Author(s):

Sangmo Koo

Keyword(s):

Three Dimensional ◽

Processing Technique ◽

Direct Writing ◽

Laser Direct Writing ◽

Precise Control ◽

Two Photon ◽

Non Invasive ◽

Acoustic Control ◽

Fs Laser ◽

Invasive Control

Two-photon polymerization (TPP) based on the femtosecond laser (fs laser) direct writing technique in the realization of high-resolution three-dimensional (3D) shapes is spotlighted as a unique and promising processing technique. It is also interesting that TPP can be applied to various applications in not only optics, chemistry, physics, biomedical engineering, and microfluidics but also micro-robotics systems. Effort has been made to design innovative microscale actuators, and research on how to remotely manipulate actuators is also constantly being conducted. Various manipulation methods have been devised including the magnetic, optical, and acoustic control of microscale actuators, demonstrating the great potential for non-contact and non-invasive control. However, research related to the precise control of microscale actuators is still in the early stages, and in-depth research is needed for the efficient control and diversification of a range of applications. In the future, the combination of the fs laser-based fabrication technique for the precise fabrication of microscale actuators/robots and their manipulation can be established as a next-generation processing method by presenting the possibility of applications to various areas.

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Comparison of haemodynamic parameters derived from invasive and non-invasive control methods

Anaesthesia, Pain, Intensive Care and Emergency Medicine — A.P.I.C.E. ◽

10.1007/978-88-470-2215-7_11 ◽

2003 ◽

pp. 771-790

Author(s):

B. Allaria ◽

M. Favaro ◽

M. Resta

Keyword(s):

Control Methods ◽

Non Invasive ◽

Haemodynamic Parameters ◽

Invasive Control

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Non-invasive control of the Hegselmann-Krause type model

2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR) ◽

10.1109/mmar.2017.8046953 ◽

2017 ◽

Author(s):

Ewa Girejko ◽

Agnieszka B. Malinowska

Keyword(s):

Type Model ◽

Non Invasive ◽

Invasive Control

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Channel Selection for Optimal EEG Measurement in Motor Imagery-Based Brain-Computer Interfaces

International Journal of Neural Systems ◽

10.1142/s0129065721500039 ◽

2020 ◽

pp. 2150003

Author(s):

Pasquale Arpaia ◽

Francesco Donnarumma ◽

Antonio Esposito ◽

Marco Parvis

Keyword(s):

Motor Imagery ◽

Classification Accuracy ◽

State Of The Art ◽

Binary Classification ◽

Brain Computer Interfaces ◽

Eeg Signals ◽

Non Invasive ◽

Computer Interfaces ◽

Selection For ◽

A Performance

A method for selecting electroencephalographic (EEG) signals in motor imagery-based brain-computer interfaces (MI-BCI) is proposed for enhancing the online interoperability and portability of BCI systems, as well as user comfort. The attempt is also to reduce variability and noise of MI-BCI, which could be affected by a large number of EEG channels. The relation between selected channels and MI-BCI performance is therefore analyzed. The proposed method is able to select acquisition channels common to all subjects, while achieving a performance compatible with the use of all the channels. Results are reported with reference to a standard benchmark dataset, the BCI competition IV dataset 2a. They prove that a performance compatible with the best state-of-the-art approaches can be achieved, while adopting a significantly smaller number of channels, both in two and in four tasks classification. In particular, classification accuracy is about 77–83% in binary classification with down to 6 EEG channels, and above 60% for the four-classes case when 10 channels are employed. This gives a contribution in optimizing the EEG measurement while developing non-invasive and wearable MI-based brain-computer interfaces.

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Early-Stage Pilot Study on Using Fractional-Order Calculus-Based Filtering for the Purpose of Analysis of Electroencephalography Signals

Studies in Logic, Grammar and Rhetoric ◽

10.1515/slgr-2016-0049 ◽

2016 ◽

Vol 47 (1) ◽

pp. 103-111 ◽

Cited By ~ 8

Author(s):

Aleksandra Kawala-Janik ◽

Waldemar Bauer ◽

Magda Żołubak ◽

Jerzy Baranowski

Keyword(s):

Pilot Study ◽

Fractional Order ◽

Early Stage ◽

Rapid Development ◽

Eeg Signals ◽

Fractional Order Calculus ◽

Non Invasive ◽

Research Areas ◽

Fractional Filters ◽

The Brain

Abstract Analysis of Electroencephalography (EEG) signals has recently awoken the increased interest of numerous researchers all around the world with regard to rapid development of Brain-Computer Interaction-related research areas and because EEG signals are implemented in most of the non-invasive BCI systems, as they provide necessary information regarding activity of the brain. In this paper, a very early stage pilot study on implementation of filtering based on fractional-order calculus (Bi-Fractional Filters – BFF) for the purpose of EEG signal classification is presented in brief.

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