scholarly journals Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks

2021 ◽  
Author(s):  
Dae-Hyeok Lee ◽  
Dong-Kyun Han ◽  
Sung-Jin Kim ◽  
Ji-Hoon Jeong ◽  
Seong-Whan Lee
Keyword(s):  
Visual Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
High Level

Development of a Deep Learning-Based Brain-Computer Interface for Visual Imagery Recognition

2020 ◽  
Author(s):  
Aldwin Jomar F. Castro ◽  
Justine Nicole P. Cruzit ◽  
Jerome Jeric C. De Guzman ◽  
John Jeru T. Pajarillo ◽  
Alyssa Margaux M. Rilloraza ◽  
...  
Keyword(s):  
Deep Learning ◽  
Visual Imagery ◽  
Brain Computer Interface ◽  
Computer Interface

scholarly journals Hjorth features and k-nearest neighbors algorithm for visual imagery classification

2021 ◽  
Author(s):  
Fabio Ricardo Llorella Costa ◽  
Gustavo Patow
Keyword(s):  
Time Domain ◽  
Visual Imagery ◽  
Brain Computer Interface ◽  
Nearest Neighbors ◽  
Computer Interface ◽  
Eeg Signals ◽  
K Nearest Neighbors ◽  
Kappa Value ◽  
The Time Domain

Abstract Visual imagery is an interesting paradigm for use in Brain-Computer Interface systems. Through visual imagery we can extend the potential of BCI systems beyond motor imagery or evoked potentials. In this work we have studied the possibility of classifying different visual imagery shapes in the time domain using EEG signals, with the Hjorth parameters and k-nearest neighbors classifier 69% accuracy has been obtained with a Cohen's kappa value of 0.64 in the classification of seven geometric shapes, obtaining results superior to other related works.

scholarly journals An Efficient and High Accuracy P300 Detection for Brain Computer Interface System Based on Kernel Principal Component Analysis

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
Keyword(s):  
Principal Component Analysis ◽  
Brain Computer Interface ◽  
Principal Component ◽  
Component Analysis ◽  
Computer Interface ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Single Trial ◽  
High Level

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.

A novel pattern with high-level commands for encoding motor imagery-based brain computer interface

2019 ◽  
Vol 125 ◽  
pp. 28-34 ◽  
Author(s):  
Shuailei Zhang ◽  
Shuai Wang ◽  
Dezhi Zheng ◽  
Kai Zhu ◽  
Mengxi Dai
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
High Level

scholarly journals What It Takes to Be a Pioneer: Ability Expectations From Brain-Computer Interface Users

NanoEthics ◽  
2020 ◽  
Vol 14 (3) ◽  
pp. 227-239
Author(s):  
Johannes Kögel ◽  
Gregor Wolbring
Keyword(s):  
Brain Computer Interface ◽  
Pain Tolerance ◽  
The Body ◽  
Computer Interface ◽  
Physical Conditions ◽  
Computer Interfaces ◽  
Emotion Control ◽  
Wide Range ◽  
The Subject ◽  
High Level

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.

A dual-arm mobile robot system performing assistive tasks operated via P300-based brain computer interface

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Giuseppe Gillini ◽  
Paolo Di Lillo ◽  
Filippo Arrichiello ◽  
Daniele Di Vito ◽  
Alessandro Marino ◽  
...  
Keyword(s):  
Daily Life ◽  
Brain Computer Interface ◽  
Life Quality ◽  
Inverse Kinematic ◽  
Robotic System ◽  
Assistive Robotics ◽  
Computer Interface ◽  
Hierarchical Architecture ◽  
Content Type ◽  
High Level

Purpose In the past decade, more than 700 million people are affected by some kind of disability or handicap. In this context, the research interest in assistive robotics is growing up. For people with mobility impairments, daily life operations, as dressing or feeding, require the assistance of dedicated people; thus, the use of devices providing independent mobility can have a large impact on improving their life quality. The purpose of this paper is to present the development of a robotic system aimed at assisting people with this kind of severe motion disabilities by providing a certain level of autonomy. Design/methodology/approach The system is based on a hierarchical architecture where, at the top level, the user generates simple and high-level commands by resorting to a graphical user interface operated via a P300-based brain computer interface. These commands are ultimately converted into joint and Cartesian space tasks for the robotic system that are then handled by the robot motion control algorithm resorting to a set-based task priority inverse kinematic strategy. The overall architecture is realized by integrating control and perception software modules developed in the robots and systems environment with the BCI2000 framework, used to operate the brain–computer interfaces (BCI) device. Findings The effectiveness of the proposed architecture is validated through experiments where a user generates commands, via an Emotiv Epoc+ BCI, to perform assistive tasks that are executed by a Kinova MOVO robot, i.e. an omnidirectional mobile robotic platform equipped with two lightweight seven degrees of freedoms manipulators. Originality/value The P300 paradigm has been successfully integrated with a control architecture that allows us to command a complex robotic system to perform daily life operations. The user defines high-level commands via the BCI, letting all the low-level tasks, for example, safety-related tasks, to be handled by the system in a completely autonomous manner.

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