scholarly journals Brain-Computer Interface-Based Humanoid Control: A Review

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3620 ◽  
Author(s):  
Vinay Chamola ◽  
Ankur Vineet ◽  
Anand Nayyar ◽  
Eklas Hossain
Keyword(s):  
Machine Learning ◽  
Humanoid Robot ◽  
Brain Computer Interface ◽  
Sensor Data ◽  
Computer Interface ◽  
Rule Based ◽  
Motor Disabilities ◽  
Brain Signals ◽  
Multi Sensor Data Fusion ◽  
Severe Motor

A Brain-Computer Interface (BCI) acts as a communication mechanism using brain signals to control external devices. The generation of such signals is sometimes independent of the nervous system, such as in Passive BCI. This is majorly beneficial for those who have severe motor disabilities. Traditional BCI systems have been dependent only on brain signals recorded using Electroencephalography (EEG) and have used a rule-based translation algorithm to generate control commands. However, the recent use of multi-sensor data fusion and machine learning-based translation algorithms has improved the accuracy of such systems. This paper discusses various BCI applications such as tele-presence, grasping of objects, navigation, etc. that use multi-sensor fusion and machine learning to control a humanoid robot to perform a desired task. The paper also includes a review of the methods and system design used in the discussed applications.

scholarly journals A Survey in Implementation and Applications of Electroencephalograph (EEG)-Based Brain-Computer Interface

2021 ◽  
Vol 39 (7) ◽  
pp. 1117-1132
Author(s):  
Samaa S. Abdulwahab ◽  
Hussain K. Khleaf ◽  
Manal H. Jassim
Keyword(s):  
Brain Computer Interface ◽  
Physical World ◽  
The Body ◽  
Computer Interface ◽  
Signal Acquisition ◽  
Rule Based ◽  
The Past ◽  
Brain Signals ◽  
Translation Systems ◽  
The Brain

A Brain-Computer Interface (BCI) is an external system that controls activities and processes in the physical world based on brain signals. In Passive BCI, artificial signals are automatically generated by a computer program without any input from nerves in the body. This is useful for individuals with mobility issues. Traditional BCI has been dependent only on recording brain signals with Electroencephalograph (EEG) and has used a rule-based translation algorithm to generate control commands. These systems have developed very accurate translation systems. This paper is about the different methods for adapting the signals from the brain. It has been mentioned that various kinds of surveys in the past to serve the purpose of the present research. This paper shows a simple and easy analysis of each technique and its respective benefits and drawbacks, including signal acquisition, signal pre-processing, feature classification and classification. Finally,  discussed is the application of EEG-based BCI.

scholarly journals Brain-Computer Interface for Persons with Motor Disabilities - A Review

2019 ◽  
Vol 13 (1) ◽  
pp. 127-133 ◽  
Author(s):  
T. Anitha ◽  
N. Shanthi ◽  
R. Sathiyasheelan ◽  
G. Emayavaramban ◽  
T. Rajendran
Keyword(s):  
Peripheral Nerves ◽  
Evoked Potential ◽  
High Efficiency ◽  
Signal To Noise Ratio ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Motor Disabilities ◽  
Self Organized ◽  
Brain Signals ◽  
Fuzzy Neural

Aim /Objective: A Brain-Computer Interface (BCI) is a communication medium, which restructures brain signals into respective commands for an external device. Methodology: A BCI allows its target users like persons with motor disabilities to act on their environment using brain signals without using peripheral nerves or muscles. In this review article, we have presented a view on different BCIs for humans with motor disabilities. Results & Conclusion: From the study, it is clear that the P300 based Electroencephalography (EEG)BCIs with Steady-State Visually Evoked Potential (SSVEP) non-parametric feature extraction techniques work with high efficiency in the major parameters like Information Bit Transfer Rate (ITR), Mutual Information (MI) rate and Low Signal to Noise Ratio (SNR) and achieve a maximum classification accuracy using Self Organized Fuzzy Neural Network (SOFNN).

Electrocorticographically controlled brain–computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants

2007 ◽  
Vol 106 (3) ◽  
pp. 495-500 ◽  
Author(s):  
Elizabeth A. Felton ◽  
J. Adam Wilson ◽  
Justin C. Williams ◽  
P. Charles Garell
Keyword(s):  
Computer Interface ◽  
Brain Areas ◽  
Motor Disabilities ◽  
Cursor Control ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Severe Motor ◽  
Subdural Electrode ◽  
Sensory Imagery

✓Brain–computer interface (BCI) technology can offer individuals with severe motor disabilities greater independence and a higher quality of life. The BCI systems take recorded brain signals and translate them into real-time actions, for improved communication, movement, or perception. Four patient participants with a clinical need for intracranial electrocorticography (ECoG) participated in this study. The participants were trained over multiple sessions to use motor and/or auditory imagery to modulate their brain signals in order to control the movement of a computer cursor. Participants with electrodes over motor and/or sensory areas were able to achieve cursor control over 2 to 7 days of training. These findings indicate that sensory and other brain areas not previously considered ideal for ECoG-based control can provide additional channels of control that may be useful for a motor BCI.

A Robust P300-Speller Brain-Computer Interface Based on Kernel PCA

2013 ◽  
Vol 300-301 ◽  
pp. 721-724 ◽  
Author(s):  
Yi Hung Liu ◽  
Jui Tsung Weng ◽  
Han Pang Huang ◽  
Jyh Tong Teng
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Principal Component ◽  
Computer Interface ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Detection Accuracy ◽  
Motor Disabilities ◽  
P300 Speller ◽  
Severe Motor

P300 speller is a well-known brain-computer interface (BCI), which allows patients with severe motor disabilities to spell words through the recognition on patients’ brain activity measured by electroencephalography (EEG). The brain-activity recognition is essentially a task of detecting of P300 responses in EEG signals. Support vector machine (SVM) has been a widely-used P300 detector in existing works. However, SVM is computationally expensive, greatly reducing the usability of the speller BCI for practical use. To address this issue, we propose in this paper a novel P300 detector, which is based on the kernel principal component analysis (KPCA). The proposed detector has a lower computational complexity, and can measure the belongingness of an input EEG to P300 class by the construction of EEG in nonlinear eigenspaces. Results carried out on subjects show that the proposed method is able to significantly shorten offline training sessions of the speller BCI while achieving high online P300-detection accuracy.

scholarly journals Brain Computer Interface Drone

2021 ◽  
Author(s):  
Manupati Hari Hara Nithin Reddy
Keyword(s):  
Machine Learning ◽  
Time Domain ◽  
Brain Computer Interface ◽  
Mental States ◽  
Computer Interface ◽  
Physical Sciences ◽  
Power Graph ◽  
Brain Signals ◽  
The Disabled ◽  
The Brain

Brain-Computer Interface has emerged from dazzling experiments of cognitive scientists and researchers who dig deep into the conscious of the human brain where neuroscience, signal processing, machine learning, physical sciences are blended together and neuroprosthesis, neuro spellers, bionic eyes, prosthetic arms, prosthetic legs are created which made the disabled to walk, a mute to express and talk, a blind to see the beautiful world, a deaf to hear, etc. My main aim is to analyze the frequency domain signal of the brain signals of 5 subjects at their respective mental states using an EEG and show how to control a DJI Tello drone using Insight EEG then present the results and interpretation of band power graph, FFT graph and time-domain signals graph of mental commands during the live control of the drone.

scholarly journals Embedded Brain Computer Interface: State-of-the-Art in Research

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4293
Author(s):  
Kais Belwafi ◽  
Sofien Gannouni ◽  
Hatim Aboalsamh
Keyword(s):  
State Of The Art ◽  
Brain Computer Interface ◽  
Experimental Studies ◽  
Interface State ◽  
Computer Interface ◽  
Excess Capacity ◽  
Motor Disabilities ◽  
Severe Motor ◽  
Signal Processing Algorithms ◽  
Available Technology

There is a wide area of application that uses cerebral activity to restore capabilities for people with severe motor disabilities, and actually the number of such systems keeps growing. Most of the current BCI systems are based on a personal computer. However, there is a tremendous interest in the implementation of BCIs on a portable platform, which has a small size, faster to load, much lower price, lower resources, and lower power consumption than those for full PCs. Depending on the complexity of the signal processing algorithms, it may be more suitable to work with slow processors because there is no need to allow excess capacity of more demanding tasks. So, in this review, we provide an overview of the BCIs development and the current available technology before discussing experimental studies of BCIs.

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.

scholarly journals Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure

AI Magazine ◽  
2012 ◽  
Vol 33 (2) ◽  
pp. 55 ◽  
Author(s):  
Nisarg Vyas ◽  
Jonathan Farringdon ◽  
David Andre ◽  
John Ivo Stivoric
Keyword(s):  
Machine Learning ◽  
Energy Expenditure ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Sensor Technology ◽  
Health Goals ◽  
System A ◽  
Multi Sensor Data Fusion ◽  
Learning Techniques ◽  
Insight Into

In this article we provide insight into the BodyMedia FIT armband system — a wearable multi-sensor technology that continuously monitors physiological events related to energy expenditure for weight management using machine learning and data modeling methods. Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges that arise in applying machine learning techniques to the health care domain and present various solutions utilized in the armband system. We demonstrate how machine learning and multi-sensor data fusion techniques are critical to the system’s success.

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