scholarly journals Applying Brain Computer Interface Technology for Playing Games

2021 ◽  
Vol 11 (3) ◽  
Author(s):  
Ankit Mehta ◽  
Adarsh Singh ◽  
Anshu Kumar ◽  
Ankit Katewa ◽  
N K Bansode
Keyword(s):  
Computer Games ◽  
Brain Computer Interface ◽  
Computer Applications ◽  
Computer Interface ◽  
Medical Field ◽  
Brain Wave ◽  
Starting Position ◽  
Computer Interfaces ◽  
Control Computer ◽  
User Friendly

Brain Computer Interfaces are specialized systems that allows users to control computer applications using their brain waves. Initially, BCI were mostly used in medical field. But after some research and thanks to consumer-grade electroencephalography (EEG) devices, many applications and research opportunities were opened outside of the medical field. One particular area that is gaining more evidence due to the arrival consumer-grade devices is that of computer games, as it allows more user-friendly applications of BCI technology for the general public. In this report, we are going to talk about one of those games, Maze game. It will be a 2D maze, path known to the user. Using the EEG device named Neurosky Brain Wave Kit user will be able to move the avatar in order to reach the goal from the starting position.

Feedback Modalities in Brain–Computer Interfaces: A Systematic Review

2020 ◽  
Vol 64 (1) ◽  
pp. 1186-1190
Author(s):  
Wakana Ishihara ◽  
Karen Moxon ◽  
Sheryl Ehrman ◽  
Mark Yarborough ◽  
Tina L. Panontin ◽  
...  
Keyword(s):  
Systematic Review ◽  
Visual Feedback ◽  
Auditory Feedback ◽  
Brain Computer Interface ◽  
Tactile Feedback ◽  
Proprioceptive Feedback ◽  
Computer Interface ◽  
Computer Interfaces ◽  
Feedback Modalities ◽  
Improving Accuracy

This systematic review addresses the plausibility of using novel feedback modalities for brain–computer interface (BCI) and attempts to identify the best feedback modality on the basis of the effectiveness or learning rate. Out of the chosen studies, it was found that 100% of studies tested visual feedback, 31.6% tested auditory feedback, 57.9% tested tactile feedback, and 21.1% tested proprioceptive feedback. Visual feedback was included in every study design because it was intrinsic to the response of the task (e.g. seeing a cursor move). However, when used alone, it was not very effective at improving accuracy or learning. Proprioceptive feedback was most successful at increasing the effectiveness of motor imagery BCI tasks involving neuroprosthetics. The use of auditory and tactile feedback resulted in mixed results. The limitations of this current study and further study recommendations are discussed.

scholarly journals A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Dheeraj Rathee ◽  
Haider Raza ◽  
Sujit Roy ◽  
Girijesh Prasad
Keyword(s):  
Scientific Community ◽  
Brain Computer Interface ◽  
Sampling Frequency ◽  
Computer Interface ◽  
Word Generation ◽  
Machine Learning Methods ◽  
Computer Interfaces ◽  
Signal Processing Algorithms ◽  
Processing Algorithms ◽  
Healthy Participants

AbstractRecent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential. However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons for this is the unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily available for researchers to develop effective and efficient BCI-related signal processing algorithms. In this work, we release a 306-channel MEG-BCI data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet imagery, subtraction imagery, and word generation imagery). The dataset contains two sessions of MEG recordings performed on separate days from 17 healthy participants using a typical BCI imagery paradigm. The current dataset will be the only publicly available MEG imagery BCI dataset as per our knowledge. The dataset can be used by the scientific community towards the development of novel pattern recognition machine learning methods to detect brain activities related to motor imagery and cognitive imagery tasks using MEG signals.

Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm

2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Stanisław Karkosz ◽  
Marcin Jukiewicz
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Steady State ◽  
Brain Computer Interface ◽  
Morphological Features ◽  
Computer Interface ◽  
Visually Evoked Potentials ◽  
Computer Interfaces ◽  
Subject Variability ◽  
Selection For

AbstractObjectivesOptimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy.MethodsSystem of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI).ResultsThe designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects.ConclusionsIt is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.

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 An Open Source-Based BCI Application for Virtual World Tour and Its Usability Evaluation

2021 ◽  
Vol 15 ◽  
Author(s):  
Sanghum Woo ◽  
Jongmin Lee ◽  
Hyunji Kim ◽  
Sungwoo Chun ◽  
Daehyung Lee ◽  
...  
Keyword(s):  
Open Source ◽  
Virtual World ◽  
Communication Channel ◽  
Brain Computer Interface ◽  
Open Science ◽  
Computer Interface ◽  
The Public ◽  
Control Functions ◽  
Computer Interfaces ◽  
And Control

Brain–computer interfaces can provide a new communication channel and control functions to people with restricted movements. Recent studies have indicated the effectiveness of brain–computer interface (BCI) applications. Various types of applications have been introduced so far in this field, but the number of those available to the public is still insufficient. Thus, there is a need to expand the usability and accessibility of BCI applications. In this study, we introduce a BCI application for users to experience a virtual world tour. This software was built on three open-source environments and is publicly available through the GitHub repository. For a usability test, 10 healthy subjects participated in an electroencephalography (EEG) experiment and evaluated the system through a questionnaire. As a result, all the participants successfully played the BCI application with 96.6% accuracy with 20 blinks from two sessions and gave opinions on its usability (e.g., controllability, completeness, comfort, and enjoyment) through the questionnaire. We believe that this open-source BCI world tour system can be used in both research and entertainment settings and hopefully contribute to open science in the BCI field.

scholarly journals Demonstration of a portable intracortical brain-computer interface

2019 ◽  
Author(s):  
Jeffrey M. Weiss ◽  
Robert A. Gaunt ◽  
Robert Franklin ◽  
Michael Boninger ◽  
Jennifer L. Collinger
Keyword(s):  
Signal Processing ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Free Form ◽  
Small Signal ◽  
Standard Laboratory ◽  
Tablet Pc ◽  
Computer Interfaces ◽  
Investigational Device Exemption ◽  
Human Participant

AbstractWhile recent advances in intracortical brain-computer interfaces (iBCI) have demonstrated the ability to restore motor and communication functions, such demonstrations have generally been confined to controlled experimental settings and have required bulky laboratory hardware. Here, we developed and evaluated a self-contained portable iBCI that enabled the user to interact with various computer programs. The iBCI, which weighs 1.5 kg, consists of digital headstages, a small signal processing hub, and a tablet PC. A human participant tested the portable iBCI in laboratory and home settings under an FDA Investigational Device Exemption (NCT01894802). The participant successfully completed 96% of trials in a 2D cursor center-out task with the portable iBCI, a rate indistinguishable from that achieved with the standard laboratory iBCI. The participant also completed a variety of free-form tasks, including drawing, gaming, and typing.

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.

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 Brain–Computer Interface Spellers for Communication: Why We Need to Address Their Security and Authenticity

2020 ◽  
Vol 10 (3) ◽  
pp. 139
Author(s):  
Anirban Dutta
Keyword(s):  
Research And Development ◽  
Information Transfer ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Brain Computer Interfaces ◽  
Transfer Rates ◽  
Computer Interfaces ◽  
Significant Research

Brain–Computer Interfaces (BCI) have witnessed significant research and development in the last 20 years where the main aim was to improve their accuracy and increase their information transfer rates (ITRs), while still making them portable and easy to use by a broad range of users [...]

scholarly journals Brain-Computer Interfaces for Internet of Things

Proceedings ◽  
2018 ◽  
Vol 2 (18) ◽  
pp. 1179 ◽  
Author(s):  
Francisco Laport ◽  
Francisco J. Vazquez-Araujo ◽  
Paula M. Castro ◽  
Adriana Dapena
Keyword(s):  
Internet Of Things ◽  
Communication Protocol ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Event Classification ◽  
Computer Interfaces ◽  
Controlling Elements ◽  
Artefact Reduction ◽  
The Brain

A brain-computer interface for controlling elements commonly used at home is presented in this paper. It includes the electroencephalography device needed to acquire signals associated to the brain activity, the algorithms for artefact reduction and event classification, and the communication protocol.

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