Shared control of a robotic arm using non-invasive brain–computer interface and computer vision guidance

2019 ◽  
Vol 115 ◽  
pp. 121-129 ◽  
Author(s):  
Yang Xu ◽  
Cheng Ding ◽  
Xiaokang Shu ◽  
Kai Gui ◽  
Yulia Bezsudnova ◽  
...  
Keyword(s):  
Computer Vision ◽  
Brain Computer Interface ◽  
Shared Control ◽  
Computer Interface ◽  
Robotic Arm ◽  
Non Invasive ◽  
Vision Guidance

scholarly journals Progress in the brain–computer interface: an interview with Bin He

2019 ◽  
Vol 7 (2) ◽  
pp. 480-483
Author(s):  
Chengyu Li ◽  
Weijie Zhao
Keyword(s):  
Science Fiction ◽  
Brain Computer Interface ◽  
Department Head ◽  
Computer Interface ◽  
Robotic Arm ◽  
The Public ◽  
Non Invasive ◽  
History Of ◽  
Human Brains ◽  
The Brain

Abstract What can the brain–computer interface (BCI) do? Wearing an electroencephalogram (EEG) headcap, you can control the flight of a drone in the laboratory by your thought; with electrodes inserted inside the brain, paralytic patients can drink by controlling a robotic arm with thinking. Both invasive and non-invasive BCI try to connect human brains to machines. In the past several decades, BCI technology has continued to develop, making science fiction into reality and laboratory inventions into indispensable gadgets. In July 2019, Neuralink, a company founded by Elon Musk, proposed a sewing machine-like device that can dig holes in the skull and implant 3072 electrodes onto the cortex, promising more accurate reading of what you are thinking, although many serious scientists consider the claim misleading to the public. Recently, National Science Review (NSR) interviewed Professor Bin He, the department head of Biomedical Engineering at Carnegie Mellon University, and a leading scientist in the non-invasive-BCI field. His team developed new methods for non-invasive BCI to control drones by thoughts. In 2019, Bin’s team demonstrated the control of a robotic arm to follow a continuously randomly moving target on the screen. In this interview, Bin He recounted the history of BCI, as well as the opportunities and challenges of non-invasive BCI.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

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.

Pilot Study on Using Innovative Counting Peaks Method for Assessment Purposes of the EEG Data Recorded from a Single-Channel Non-Invasive Brain-Computer Interface

2021 ◽  
Author(s):  
Natalia Browarska ◽  
Jaroslaw Zygarlicki ◽  
Mariusz Pelc ◽  
Michal Niemczynowicz ◽  
Malgorzata Zygarlicka ◽  
...  
Keyword(s):  
Pilot Study ◽  
Single Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Non Invasive ◽  
Eeg Data

Cerebral Pulse Controlled Robotic Hand for Effective Paralytic Movements

2021 ◽  
Vol 9 (8) ◽  
pp. 2201-2204
Author(s):  
Abhay Patil
Keyword(s):  
Basic Assumption ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Robotic Arm ◽  
Human Hand ◽  
Robotic Hand ◽  
Handicapped People ◽  
Alpha Beta

Abstract: There are roughly 21 million handicapped people in India, which is comparable to 2.2% of the complete populace. These people are affected by various neuromuscular problems. To empower them to articulate their thoughts, one can supply them with elective and augmentative correspondence. For this, a Brain-Computer Interface framework (BCI) has been assembled to manage this specific need. The basic assumption of the venture reports the plan, working just as a testing impersonation of a man's arm which is intended to be powerfully just as kinematically exact. The conveyed gadget attempts to take after the movement of the human hand by investigating the signs delivered by cerebrum waves. The cerebrum waves are really detected by sensors in the Neurosky headset and produce alpha, beta, and gamma signals. Then, at that point, this sign is examined by the microcontroller and is then acquired onto the engineered hand by means of servo engines. A patient that experiences an amputee underneath the elbow can acquire from this specific biomechanical arm. Keywords: Brainwaves, Brain Computer Interface, Arduino, EEG sensor, Neurosky Mindwave Headset, Robotic arm

scholarly journals EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

2018 ◽  
Vol 8 (11) ◽  
pp. 199 ◽  
Author(s):  
Rodrigo Ramele ◽  
Ana Villar ◽  
Juan Santos
Keyword(s):  
Brain Computer Interface ◽  
Waveform Analysis ◽  
Computer Interface ◽  
Clinical Tool ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Public Dataset ◽  
Imaging Sensor ◽  
Clinical Eeg

The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

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