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.

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 EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

2018 ◽  
Vol 8 (11) ◽  
pp. 199 ◽  
Author(s):  
Rodrigo Ramele ◽  
Ana Villar ◽  
Juan Santos
Keyword(s):  
Brain Computer Interface ◽  
Waveform Analysis ◽  
Computer Interface ◽  
Clinical Tool ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Public Dataset ◽  
Imaging Sensor ◽  
Clinical Eeg

The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

scholarly journals A Survey of Interactive Systems based on Brain-Computer Interfaces

2013 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Alessandro Luiz Stamatto Ferreira ◽  
Leonardo Cunha de Miranda ◽  
Erica Esteves Cunha de Miranda ◽  
Sarah Gomes Sakamoto
Keyword(s):  
Virtual Reality ◽  
Brain Computer Interface ◽  
Research Area ◽  
Interactive Systems ◽  
Computer Interface ◽  
Immersive Environments ◽  
User Interactions ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Near Future

Brain-Computer Interface (BCI) enables users to interact with a computer only through their brain biological signals, without the need to use muscles. BCI is an emerging research area but it is still relatively immature. However, it is important to reflect on the different aspects of the Human-Computer Interaction (HCI) area related to BCIs, considering that BCIs will be part of interactive systems in the near future. BCIs most attend not only to handicapped users, but also healthy ones, improving interaction for end-users. Virtual Reality (VR) is also an important part of interactive systems, and combined with BCI could greatly enhance user interactions, improving the user experience by using brain signals as input with immersive environments as output. This paper addresses only noninvasive BCIs, since this kind of capture is the only one to not present risk to human health. As contributions of this work we highlight the survey of interactive systems based on BCIs focusing on HCI and VR applications, and a discussion on challenges and future of this subject matter.

An Overview on the Use of Serious Games in Physical Therapy and Rehabilitation

2012 ◽  
pp. 1175-1187 ◽  
Author(s):  
Tiago Martins ◽  
Vítor Carvalho ◽  
Filomena Soares
Keyword(s):  
Physical Therapy ◽  
Serious Games ◽  
Rehabilitation Program ◽  
Motor Disabilities ◽  
Interactive Games ◽  
Healthcare Settings ◽  
Study Results ◽  
Severe Motor ◽  
Number Of Individuals

As a significant number of individuals have severe motor disabilities due to neurological and musculoskeletal conditions, it is important to provide them with an appropriate rehabilitation program in order to improve their quality of life. Several study results suggest that many elements of the interactive games have tremendous potential as rehabilitation tools. Serious games can entertain the players, while rewarding and reinforcing healthy movements. As these technologies create a pleasant environment, they motivate the patients to perform the necessary exercises with satisfaction and total relaxation, even forgetting that they are conducting therapy. In this sense, various serious games are being applied in healthcare settings, namely in many physical therapy and rehabilitation situations. This chapter discusses the different potentialities of several serious games when used in physical therapy and rehabilitation of patients with problems in motor skills.

scholarly journals Evolution of brain-computer interfaces: going beyond classic motor physiology

2009 ◽  
Vol 27 (1) ◽  
pp. E4 ◽  
Author(s):  
Eric C. Leuthardt ◽  
Gerwin Schalk ◽  
Jarod Roland ◽  
Adam Rouse ◽  
Daniel W. Moran
Keyword(s):  
Primary Motor Cortex ◽  
Current Status ◽  
Brain Computer Interfaces ◽  
Motor Disabilities ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Motor Physiology ◽  
Potential Efficacy ◽  
Technical Possibility ◽  
Cortical Physiology

The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future.

scholarly journals Behind the Scenes of Noninvasive Brain–Computer Interfaces: A Review of Electroencephalography Signals, How They Are Recorded, and Why They Matter

2019 ◽  
Vol 4 (6) ◽  
pp. 1622-1636
Author(s):  
Kevin M. Pitt ◽  
Jonathan S. Brumberg ◽  
Jeremy D. Burnison ◽  
Jyutika Mehta ◽  
Juhi Kidwai
Keyword(s):  
Augmentative And Alternative Communication ◽  
Computer Interface ◽  
Computer Access ◽  
Alternative Communication ◽  
Primary Interest ◽  
Physical Impairments ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Speech Science ◽  
Sensorimotor Rhythms

Purpose Brain–computer interface (BCI) techniques may provide computer access for individuals with severe physical impairments. However, the relatively hidden nature of BCI control obscures how BCI systems work behind the scenes, making it difficult to understand “how” electroencephalography (EEG) records the BCI-related brain signals, “what” brain signals are recorded by EEG, and “why” these signals are targeted for BCI control. Furthermore, in the field of speech-language-hearing, signals targeted for BCI application have been of primary interest to clinicians and researchers in the area of augmentative and alternative communication (AAC). However, signals utilized for BCI control reflect sensory, cognitive, and motor processes, which are of interest to a range of related disciplines, including speech science. Method This tutorial was developed by a multidisciplinary team emphasizing primary and secondary BCI-AAC–related signals of interest to speech-language-hearing. Results An overview of BCI-AAC–related signals are provided discussing (a) “how” BCI signals are recorded via EEG; (b) “what” signals are targeted for noninvasive BCI control, including the P300, sensorimotor rhythms, steady-state evoked potentials, contingent negative variation, and the N400; and (c) “why” these signals are targeted. During tutorial creation, attention was given to help support EEG and BCI understanding for those without an engineering background. Conclusion Tutorials highlighting how BCI-AAC signals are elicited and recorded can help increase interest and familiarity with EEG and BCI techniques and provide a framework for understanding key principles behind BCI-AAC design and implementation.

scholarly journals Brain-Computer Interfaces and Creative Expression: Interface Considerations for Rehabilitative and Therapeutic Interactions

2021 ◽  
Vol 3 ◽  
Author(s):  
Stephanie M. Scott ◽  
Chris Raftery
Keyword(s):  
Personal Experience ◽  
Meaning Making ◽  
Digital Health ◽  
Computer Interface ◽  
Creative Arts ◽  
Embodied Interaction ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Digital Environments ◽  
The Brain

By translating brain signals into new kinds of outputs, Brain-Computer Interface (BCI) systems hold tremendous potential as both transformative rehabilitation and communication tools. BCIs can be considered a unique technology, in that they are able to provide a direct link between the brain and the external environment. By affording users with opportunities for communication and self-expression, BCI systems serve as a bridge between abled-bodied and disabled users, in turn reducing existing barriers between these groups. This perspective piece explores the complex shifting relationship between neuroadaptive systems and humans by foregrounding personal experience and embodied interaction as concepts through which to evaluate digital environments cultivated through the design of BCI interfaces. To underscore the importance of fostering human-centered experiences through technologically mediated interactions, this work offers a conceptual framework through which the rehabilitative and therapeutic possibilities of BCI user-system engagement could be furthered. By inviting somatic analysis towards the design of BCI interfaces and incorporating tenets of creative arts therapies practices into hybrid navigation paradigms for self-expressive applications, this work highlights the need for examining individual technological interactions as sites with meaning-making potentiality, as well as those conceived through unique exchanges based on user-specific needs for communication. Designing BCI interfaces in ways that afford users with increased options for navigation, as well as with the ability to share subjective and collective experiences, helps to redefine existing boundaries of digital and physical user-system interactions and encourages the reimagining of these systems as novel digital health tools for recovery.

scholarly journals Brain-Computer Interface for Persons with Motor Disabilities - A Review

2019 ◽  
Vol 13 (1) ◽  
pp. 127-133 ◽  
Author(s):  
T. Anitha ◽  
N. Shanthi ◽  
R. Sathiyasheelan ◽  
G. Emayavaramban ◽  
T. Rajendran
Keyword(s):  
Peripheral Nerves ◽  
Evoked Potential ◽  
High Efficiency ◽  
Signal To Noise Ratio ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Motor Disabilities ◽  
Self Organized ◽  
Brain Signals ◽  
Fuzzy Neural

Aim /Objective: A Brain-Computer Interface (BCI) is a communication medium, which restructures brain signals into respective commands for an external device. Methodology: A BCI allows its target users like persons with motor disabilities to act on their environment using brain signals without using peripheral nerves or muscles. In this review article, we have presented a view on different BCIs for humans with motor disabilities. Results & Conclusion: From the study, it is clear that the P300 based Electroencephalography (EEG)BCIs with Steady-State Visually Evoked Potential (SSVEP) non-parametric feature extraction techniques work with high efficiency in the major parameters like Information Bit Transfer Rate (ITR), Mutual Information (MI) rate and Low Signal to Noise Ratio (SNR) and achieve a maximum classification accuracy using Self Organized Fuzzy Neural Network (SOFNN).

The Application of BCI Technology in Android RPG Game

2014 ◽  
Vol 496-500 ◽  
pp. 2015-2018
Author(s):  
Jing Hai Yin ◽  
Zheng Dong Mu ◽  
Jian Feng Hu
Keyword(s):  
Brain Computer Interface ◽  
Human Interaction ◽  
Research Interest ◽  
Computer Interface ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Brain Signals

To enhance human interaction with machines, research interest is growing to develop a Brain-Computer Interface (BCI), which allows communication of a human with a machine only by use of brain signals. In this paper, one type of android RPG game was designed for application of brain computer interfaces.

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