scholarly journals A telepresence robotic system operated with a P300-based brain-computer interface: Initial tests with ALS patients

Author(s):  
C Escolano ◽  
Ander Ramos Murguialday ◽  
T Matuz ◽  
N Birbaumer ◽  
J Minguez
2011 ◽  
Vol 71 ◽  
pp. e98
Author(s):  
Koichi Mori ◽  
Toshinori Maruoka ◽  
Minae Okada ◽  
Kazuyuki Itoh ◽  
Takenobu Inoue

Author(s):  
Mridu Sahu ◽  
Saumya Vishwal ◽  
Srungaram Usha Srivalli ◽  
Naresh Kumar Nagwani ◽  
Shrish Verma ◽  
...  

Background: The purpose of this study is to identifying time series analysis and mathematical model fitting on electroencephalography channels that are placed on Amyotrophic Lateral Sclerosis (ALS) patients with P300 based brain-computer interface (BCI). Methods: Amyotrophic Lateral Sclerosis (ALS) or motor neuron diseases are a rapidly progressing neurological disease that attacks and kills neurons responsible for controlling voluntary muscles. There is no cure and treatment effective to reverse, to halt the disease progression. A Brain- Computer Interface enables disable person to communicate & interact with each other and with the environment. To bypass the important motor difficulties present in ALS patient, BCI is useful. An input for BCI system is patient's brain signals and these signals are converted into external operations, for brain signals detection, Electroencephalography (EEG) is normally used. P300 based BCI is used to record the reading of EEG brain signals with the help of non-invasive placement of channels. In EEG, channel analysis Autoregressive (AR) model is a widely used. In the present study, Yule-Walker approach of AR model has been used for channel data fitting. Model fitting as a form of digitization is majorly required for good understanding of the dataset, and also for data prediction. Results: Fourth order of the mathematical curve for this dataset is selected. The reason is the high accuracy obtained in the 4th order of Autoregressive model (97.51±0.64). Conclusion: In proposed Auto Regressive (AR) model has been used for Amyotrophic Lateral Sclerosis (ALS) patient data analysis. The 4th order of Yule Walker auto-regressive model is giving best fitting on this problem.


2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Giuseppe Gillini ◽  
Paolo Di Lillo ◽  
Filippo Arrichiello ◽  
Daniele Di Vito ◽  
Alessandro Marino ◽  
...  

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.


2010 ◽  
Vol 4 ◽  
Author(s):  
Jana I. Münßinger ◽  
Sebastian Halder ◽  
Sonja C. Kleih ◽  
Adrian Furdea ◽  
Valerio Raco ◽  
...  

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