From Brain Signals to Adaptive Interfaces: Using fNIRS in HCI

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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73-OR: Gut–Brain Signals That Regulate Hunger

Diabetes ◽

10.2337/db20-73-or ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 73-OR

Author(s):

ZACHARY KNIGHT

Keyword(s):

Brain Signals

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Adaptive interfaces for application defragmentation in diverse operating contexts

10.31274/etd-180810-1746 ◽

2012 ◽

Author(s):

Arun Kalyanasamy

Keyword(s):

Adaptive Interfaces

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Phase/Amplitude Synchronization of Brain Signals During Motor Imagery BCI Tasks

IEEE Transactions on Neural Systems and Rehabilitation Engineering ◽

10.1109/tnsre.2021.3088637 ◽

2021 ◽

pp. 1-1

Author(s):

Tiziana Cattai ◽

Stefania Colonnese ◽

Marie-Constance Corsi ◽

Danielle S. Bassett ◽

Gaetano Scarano ◽

...

Keyword(s):

Motor Imagery ◽

Brain Signals ◽

Phase Amplitude

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Switching it Up: Designing Adaptive Interfaces for Virtual Reality Exergames

Proceedings of the 31st European Conference on Cognitive Ergonomics - ECCE 2019 ◽

10.1145/3335082.3335087 ◽

2019 ◽

Cited By ~ 2

Author(s):

Joey Campbell ◽

Mike Fraser

Keyword(s):

Virtual Reality ◽

Adaptive Interfaces

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An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

Electronics ◽

10.3390/electronics10050560 ◽

2021 ◽

Vol 10 (5) ◽

pp. 560

Author(s):

Andrea Bonci ◽

Simone Fiori ◽

Hiroshi Higashi ◽

Toshihisa Tanaka ◽

Federica Verdini

Keyword(s):

Biomedical Engineering ◽

Data Privacy ◽

Lie Detection ◽

Human Cognition ◽

Brain Computer Interfaces ◽

Computer Interfaces ◽

Brain Signals ◽

Ethical And Legal Issues ◽

And Performance ◽

Brain Computer Interfacing

The prospect and potentiality of interfacing minds with machines has long captured human imagination. Recent advances in biomedical engineering, computer science, and neuroscience are making brain–computer interfaces a reality, paving the way to restoring and potentially augmenting human physical and mental capabilities. Applications of brain–computer interfaces are being explored in applications as diverse as security, lie detection, alertness monitoring, gaming, education, art, and human cognition augmentation. The present tutorial aims to survey the principal features and challenges of brain–computer interfaces (such as reliable acquisition of brain signals, filtering and processing of the acquired brainwaves, ethical and legal issues related to brain–computer interface (BCI), data privacy, and performance assessment) with special emphasis to biomedical engineering and automation engineering applications. The content of this paper is aimed at students, researchers, and practitioners to glimpse the multifaceted world of brain–computer interfacing.

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Brain–Computer Interface (BCI) Control of a Virtual Assistant in a Smartphone to Manage Messaging Applications

Sensors ◽

10.3390/s21113716 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3716

Author(s):

Francisco Velasco-Álvarez ◽

Álvaro Fernández-Rodríguez ◽

Francisco-Javier Vizcaíno-Martín ◽

Antonio Díaz-Estrella ◽

Ricardo Ron-Angevin

Keyword(s):

Free Text ◽

Short Message ◽

Negative Consequences ◽

Computer Interfaces ◽

Brain Signals ◽

Message Service ◽

Performance Results ◽

And Control ◽

E Mail ◽

Visual P300

Brain–computer interfaces (BCI) are a type of assistive technology that uses the brain signals of users to establish a communication and control channel between them and an external device. BCI systems may be a suitable tool to restore communication skills in severely motor-disabled patients, as BCI do not rely on muscular control. The loss of communication is one of the most negative consequences reported by such patients. This paper presents a BCI system focused on the control of four mainstream messaging applications running in a smartphone: WhatsApp, Telegram, e-mail and short message service (SMS). The control of the BCI is achieved through the well-known visual P300 row-column paradigm (RCP), allowing the user to select control commands as well as spelling characters. For the control of the smartphone, the system sends synthesized voice commands that are interpreted by a virtual assistant running in the smartphone. Four tasks related to the four mentioned messaging services were tested with 15 healthy volunteers, most of whom were able to accomplish the tasks, which included sending free text e-mails to an address proposed by the subjects themselves. The online performance results obtained, as well as the results of subjective questionnaires, support the viability of the proposed system.

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