scholarly journals Biomechanical Signals Human-Computer Interface for Severe Motor Disabilities

2013 ◽  
Vol 02 (04) ◽  
pp. 65-71 ◽  
Author(s):  
Albano Carrera ◽  
Alonso A. Alonso ◽  
Ramón de la Rosa ◽  
Javier M. Aguiar
Keyword(s):  
Computer Interface ◽  
Human Computer Interface ◽  
Motor Disabilities ◽  
Severe Motor

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.

Eyeing a real-time human-computer interface to assist those with motor disabilities

2008 ◽  
Vol 27 (3) ◽  
pp. 19-25 ◽  
Author(s):  
Anaelis Sesin ◽  
Malek Adjouadi ◽  
Melvin Ayala ◽  
Mercedes Cabrerizo ◽  
Armando Barreto
Keyword(s):  
Real Time ◽  
Computer Interface ◽  
Human Computer Interface ◽  
Motor Disabilities

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 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.

An Adaptive Hardware and Software Based Human Computer Interface for People with Motor Disabilities

2019 ◽  
Vol 17 (09) ◽  
pp. 1401-1409 ◽  
Author(s):  
Marcio Bender Machado ◽  
Andreia Sias Rodrigues ◽  
Marcelo Bender Machado ◽  
Vinicius Kruger da Costa ◽  
Rafael Cunha Cardoso ◽  
...  
Keyword(s):  
Computer Interface ◽  
Human Computer Interface ◽  
Motor Disabilities
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