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.

Rehabilitation robots

2018 ◽  
Vol 45 (3) ◽  
pp. 301-306 ◽  
Author(s):  
Robert Bogue
Keyword(s):  
Computer Games ◽  
Brain Computer Interface ◽  
Hand Movement ◽  
Traumatic Injury ◽  
Lower Limbs ◽  
Computer Interface ◽  
Robotic Rehabilitation ◽  
Content Type ◽  
Rehabilitation Robots ◽  
Ambulatory Ability

Purpose This paper aims to provide an insight into the emerging use of robots in the rehabilitation of sufferers from strokes and other neurological impediments. Design/methodology/approach This considers research, clinical trials and commercial products. Following an introduction, it explains brain neuroplasticity and its role in rehabilitation and then discusses the use of robots in the restoration of upper limb and hand movement in stroke and traumatic injury patients. Robotic techniques aimed at restoring ambulatory ability are then discussed, followed by examples of the application of brain–computer interface technology to robotic rehabilitation. Finally, concluding comments are drawn. Findings Research has shown that robotic techniques can assist in the restoration of functionality to partially or fully paralysed upper and lower limbs. A growing number of commercial exoskeleton and end-effector robotic products have been launched which are augmenting conventional rehabilitation therapies. These systems frequently include interactive computer games and tasks which encourage repetitive use and allow patients to monitor their progress. Trials which combine robotics with brain–computer interface technology have yielded encouraging and unexpectedly positive results. Originality/value This provides details of the increasingly important role played by robots in the rehabilitation of patients suffering from strokes and other neurological disorders.

scholarly journals User centred design and validation during the development of domestic brain computer interface applications for people with acquired brain injury and therapists: a multi-stakeholder approach

2016 ◽  
Vol 10 (2) ◽  
pp. 67-78 ◽  
Author(s):  
Jean Mary Daly Lynn ◽  
Elaine Armstrong ◽  
Suzanne Martin
Keyword(s):  
Brain Injury ◽  
Brain Computer Interface ◽  
Acquired Brain Injury ◽  
Computer Interface ◽  
Content Type ◽  
Implementation Phase ◽  
User Centred Design ◽  
Home Based ◽  
Multi Stakeholder ◽  
Bci System

Purpose – The purpose of this paper is to outline the application of user centred design (UCD) within a research project to support the design, development and evaluation of a brain computer interface (BCI) with associated home-based services and remote therapy station for people with acquired brain injury (ABI). Design/methodology/approach – A multi- stakeholder UCD approach was adopted to include people living with ABI, their caregivers and therapists providing rehabilitation. A three-phased iterative approach was implemented: Phase 1 was to gather user requirements, Phase 2 an iterative design phase with end user (EU) groups and therapists and finally the verification and implementation phase. The final phase had two strands of a home-based BCI evaluation with target EUs and their caregivers, alongside this, therapists evaluated the final therapist station that supports the use of the BCI at home. Ethical governance, inline with Ulster University, was awarded. Findings – UCD enabled the co-creation and validation of a home-based BCI system for social inclusion and rehabilitation. Originality/value – This was the first BCI project to adopt UCD to design and validation a novel home-based BCI system and migrate this from the lab to home. It highlights the importance of UCD to bridge the gap between the technical developers and those whom the technology is aimed at. This complex design process is essential to increase usability and reduce device abandonment.

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

scholarly journals Single channel electroencephalogram (EEG) brain computer interface (BCI) feature extraction and quantization method for support vector machine classification

2018 ◽  
Vol 7 (4) ◽  
pp. 2095 ◽  
Author(s):  
Tarmizi Ahmad Izzuddin ◽  
Norlaili Mat Safri ◽  
Fauzal Naim Zohedi ◽  
Mohamad Afzan Othman ◽  
Muhammad Shaufil Adha Shawkany Hazim
Keyword(s):  
Support Vector Machine ◽  
Single Channel ◽  
Brain Computer Interface ◽  
Target Object ◽  
International Standards ◽  
Assistive Robotics ◽  
Computer Interface ◽  
Support Vector ◽  
Electroencephalogram Eeg ◽  
Bci System

Over the recent years, there has been a huge interest towards Electroencephalogram (EEG) based brain computer interface (BCI) system. BCI system enables the extraction of meaningful information directly from the human brain via suitable signal processing and machine learning method and thus, many researches have applied this technology towards rehabilitation and assistive robotics. Such application is important towards improving the lives of people with motor diseases such as Amytrophic Lateral Scelorosis (ALS) disease or people with quadriplegia/tetraplegia. This paper introduces features extraction method based on the Fast Fourier Transform (FFT) with logarithmic bin-ning for rapid classification using Support Vector Machine (SVM) algorithm, with an application towards a BCI system with a shared con-trol scheme. In general, subjects wearing a single channel EEG electrode located at F8 (10-20 international standards) were required to syn-chronously imagine a star rotating and mind relaxation at specific time and direction. The imagination of a star would trigger a mobile robot suggesting that there exists a target object at certain direction. Based on the proposed algorithm, we showed that our algorithm can distin-guish between mind relaxation and mental star rotation with up to 80% accuracy from the single channel EEG signals.  

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.

Sign in / Sign up

Export Citation Format

Share Document