The Potential of the Brain-Computer Interface for Learning: A Technology Review

Galway, Leo; McCullagh, Paul; Lightbody, Gaye; Brennan, Chris; Trainor, David

doi:10.1109/cit/iucc/dasc/picom.2015.234

Cited by 9 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The signal processing and classification is based on the canonical correlation analysis (CCA) [7], which calculates the canonical coefficients, i.e., the maximal correlation 1 Commonly used for EEG, EMG and ECG. 2 When streaming raw data. 3 Protocol not specified.…”

Section: Classification Testsmentioning

confidence: 99%

“…In the trend of extracting EEG and EMG biomarkers, several low-cost platforms (<2k EUR), such as Emotiv Insight and EPOC+, Neurosky MindWave, and OpenBCI, have recently emerged [2]. For instance, Emotive offers a range of headset devices from 2 to 32 channels to automatically detect some events such as facial expressions, emotional states, and mental commands.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Low-Power, Low-Cost IoT Processors in Clinical Biosignal Research: an In-depth Feasibility Check

Kartsch

Artoni

Benatti

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

View full text Add to dashboard Cite

Research on biosignal (ExG) analysis is usually performed with expensive systems requiring connection with external computers for data processing. Consumer-grade low-cost wearable systems for bio-potential monitoring and embedded processing have been presented recently, but are not considered suitable for medical-grade analyses. This work presents a detailed quantitative comparative analysis of a recently presented fully-wearable low-power and low-cost platform (BioWolf) for ExG acquisition and embedded processing with two researchgrade acquisition systems, namely, ANTNeuro (EEG) and the Noraxon DTS (EMG). Our preliminary results demonstrate that BioWolf offers competitive performance in terms of electrical properties and classification accuracy. This paper also highlights distinctive features of BioWolf, such as real-time embedded processing, improved wearability, and energy-efficiency, which allows devising new types of experiments and usage scenarios for medical-grade biosignal processing in research and future clinical studies.

show abstract

Section: Classification Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Low-Power, Low-Cost IoT Processors in Clinical Biosignal Research: an In-depth Feasibility Check

Kartsch

Artoni

Benatti

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

View full text Add to dashboard Cite

show abstract

“…The BCI has been used in a wide variety of applications, including rehabilitation, robotics, entertainment, and virtual reality [3][4][5][6]. No longer seen as a purely assistive technology, BCI has been gaining interest as a noninvasive physiological observation mechanism applicable to health care and education settings [7]. AR provides an opportunity to integrate feedback into a real-world environment and enhance a user experience by advancing human-computer interaction capabilities, while the BCI enables a new hands-free interaction modality and provides information about the user's mental state, which supports adaptive training and performance improvement [8,9].…”

Section: Introductionmentioning

confidence: 99%

Integration of Augmented Reality and Brain-Computer Interface Technologies for Health Care Applications: Exploratory and Prototyping Study

Andrews

2022

JMIR Form Res

View full text Add to dashboard Cite

Background Augmented reality (AR) and brain-computer interface (BCI) are promising technologies that have a tremendous potential to revolutionize health care. While there has been a growing interest in these technologies for medical applications in the recent years, the combined use of AR and BCI remains a fairly unexplored area that offers significant opportunities for improving health care professional education and clinical practice. This paper describes a recent study to explore the integration of AR and BCI technologies for health care applications. Objective The described effort aims to advance an understanding of how AR and BCI technologies can effectively work together to transform modern health care practice by providing new mechanisms to improve patient and provider learning, communication, and shared decision-making. Methods The study methods included an environmental scan of AR and BCI technologies currently used in health care, a use case analysis for a combined AR-BCI capability, and development of an integrated AR-BCI prototype solution for health care applications. Results The study resulted in a novel interface technology solution that enables interoperability between consumer-grade wearable AR and BCI devices and provides the users with an ability to control digital objects in augmented reality using neural commands. The article discusses this novel solution within the context of practical digital health use cases developed during the course of the study where the combined AR and BCI technologies are anticipated to produce the most impact. Conclusions As one of the pioneering efforts in the area of AR and BCI integration, the study presents a practical implementation pathway for AR-BCI integration and provides directions for future research and innovation in this area.

show abstract

“…BCI systems use different neuroimaging sensors to retrieve evoked brain signals through a specific thinking process that corresponds to a certain mental state of the user at a time that can be converted into a computer command. In order to provide a general overview of sensory, motor and cognitive processes, the BCI system uses the electroencephalogram (EEG) principle to measure electrical activity of the brain, which is diffusely distributed across the scalp and that corresponds to the brainwaves alpha, beta and theta [2].…”

Section: Introductionmentioning

confidence: 99%

Design of a Brain-Controlled Video Game based on a BCI System

Rosca

Leba

2019

MATEC Web Conf.

View full text Add to dashboard Cite

The Computer Brain Interface (BCIs) is a new type of user interface designed to recognize real-time user-specific intentions based on user brain mapping. Gradual advancing technology behind the BCI interface has made it possible to develop gaming applications that use directly brain input instead of well-known traditional control methods. This paper presents the Mental Pool Game, a brain-controlled computer game powered by Unity3D, which uses a commercial BCI device based on a network of electroencephalography (EEG) sensors placed on the scalp capable of creating brain patterns only by capturing conscious thoughts from a user that can be easily reproduced as mental actions to control strength and speed on which the user can project on the white ball to hit any of the eight balls on the pool table. Mental PoolGame, powered by Unity3D, is a platform that provides all tools necessary for a developer to develop three-dimensional games that has been designed so that the end user no longer uses classical commands that depend on the use of well-known peripheral input devices present on any PC, such as a keyboard, mouse and more recently a joystick to control the movement of space in an object in a virtual environment.

show abstract

The Potential of the Brain-Computer Interface for Learning: A Technology Review

Cited by 9 publications

References 11 publications

Using Low-Power, Low-Cost IoT Processors in Clinical Biosignal Research: an In-depth Feasibility Check

Using Low-Power, Low-Cost IoT Processors in Clinical Biosignal Research: an In-depth Feasibility Check

Integration of Augmented Reality and Brain-Computer Interface Technologies for Health Care Applications: Exploratory and Prototyping Study

Design of a Brain-Controlled Video Game based on a BCI System

Contact Info

Product

Resources

About