Brain-Computer Interface for high-level control of rehabilitation robotic systems

Author(s):  
Diana Valbuena ◽  
Marco Cyriacks ◽  
Ola Friman ◽  
Ivan Volosyak ◽  
Axel Graser
2021 ◽  
Author(s):  
Dae-Hyeok Lee ◽  
Dong-Kyun Han ◽  
Sung-Jin Kim ◽  
Ji-Hoon Jeong ◽  
Seong-Whan Lee

2021 ◽  
Author(s):  
Mohammad Farukh Hashmi Mohammad Farukh Hashmi ◽  
Jagdish D.Kene Jagdish D.Kene ◽  
Deepali M.Kotambkar Deepali M.Kotambkar ◽  
Praveen Matte Praveen Matte ◽  
Avinash G.Keskar Avinash G.Keskar

Abstract Human machine interaction with the use of brain signals has been made possible by the advent of the technology popularly known as brain computer interface (BCI). P300 is one such brain signal which is used in many BCI systems. The problems associated with most of the existing P300 detection methods are that they are time consuming and computationally complex as they follow the procedure of averaging the values obtained from multiple trials. Also the existing single trial methods have been able to obtain only moderate accuracy levels. In this paper, a novel approach which for achieving a high level of accuracy has been proposed for single trial P300 signal detection amidst noise and artifacts. In this method features were obtained by applying Discrete Wavelet Transform followed by a technique making use of the obtained wavelet coefficients. Kernel Principal Component Analysis (KPCA) was used for reducing the feature dimension. Classification of the P300 signal using the reduced features was done using Support Vector Machine (SVM). The Dataset used was the Dataset II of the third BCI Competition. An accuracy of 98.53% was achieved for Subject S1 (signal obtained from the first person) and 99.25% for Subject S2 (signal obtained from the second person) by using the proposed method. A high level of accuracy was obtained, as compared to many existing techniques. Also the speed of classification was improved with the use of reduced feature dimensions.


2019 ◽  
Vol 125 ◽  
pp. 28-34 ◽  
Author(s):  
Shuailei Zhang ◽  
Shuai Wang ◽  
Dezhi Zheng ◽  
Kai Zhu ◽  
Mengxi Dai

NanoEthics ◽  
2020 ◽  
Vol 14 (3) ◽  
pp. 227-239
Author(s):  
Johannes Kögel ◽  
Gregor Wolbring

AbstractBrain-computer interfaces (BCIs) are envisioned to enable new abilities of action. This potential can be fruitful in particular when it comes to restoring lost motion or communication abilities or to implementing new possibilities of action. However, BCIs do not come without presuppositions. Applying the concept of ability expectations to BCIs, a wide range of requirements on the side of the users becomes apparent. We examined these ability expectations by taking the example of therapeutic BCI users who got enrolled into BCI research studies due to particular physical conditions. Some of the expectations identified are quite explicit, like particular physical conditions and BCI “literacy”. Other expectations are more implicit, such as motivation, a high level of concentration, pain tolerance, emotion control and resources. These expectations may produce a conception of the human and a self-understanding among BCI users that objectify the body in favour of a brain-centred, cerebral notion of the subject which also plays its part in upholding a normality regime.


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