Brain-computer Interfaces and Art: Toward a Theoretical Framework

2019 ◽  
Vol 13 (1-2) ◽  
pp. 182-195
Author(s):  
Veronica Alfano
Keyword(s):  
Brain Computer Interface ◽  
Theoretical Framework ◽  
Computer Interface ◽  
Direct Access ◽  
Aesthetic Theory ◽  
Reader Response Theory ◽  
The Public ◽  
Computer Interfaces ◽  
New Perspective

Art that incorporates brain-computer interface (or BCI) technology sheds fresh light on several aspects of aesthetic theory. Because it is radically interactive and can permit viewers or listeners to modify a work directly by means of their cerebral activity, such art illuminates the role of audience members in shaping that work's meaning; in this way, it literalizes reader-response theory and allows the public to engage even with opaque or alienating pieces. BCI-based art also reframes the significance of the artist's intentions, prompting a reconsideration of the truism that ‘the author is dead’, both by positing a collective form of authorship and by granting a creator access to her own unconscious impulses. Finally, via the notion that it may be possible to transfer unfiltered ideas between brains, BCI-inspired artworks provide a new perspective on art as mediation. Although artists have traditionally been praised for seeming to grant direct access to their emotions, one could argue that artistry happens in the act of concretizing and externalizing one's ideas – that is, in the mediated translation of thought rather than in thought itself. The essay concludes by discussing the implications of this theoretical framework for (among other fields) the digital humanities.

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 A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation

2020 ◽  
Vol 10 (10) ◽  
pp. 734
Author(s):  
Md Rakibul Mowla ◽  
Jesus D. Gonzalez-Morales ◽  
Jacob Rico-Martinez ◽  
Daniel A. Ulichnie ◽  
David E. Thompson
Keyword(s):  
Discriminant Analysis ◽  
System Performance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Linear Discriminant ◽  
Computer Interfaces ◽  
Classification Technique ◽  
Latency Jitter ◽  
Bci System

P300-based Brain-Computer Interface (BCI) performance is vulnerable to latency jitter. To investigate the role of latency jitter on BCI system performance, we proposed the classifier-based latency estimation (CBLE) method. In our previous study, CBLE was based on least-squares (LS) and stepwise linear discriminant analysis (SWLDA) classifiers. Here, we aim to extend the CBLE method using sparse autoencoders (SAE) to compare the SAE-based CBLE method with LS- and SWLDA-based CBLE. The newly-developed SAE-based CBLE and previously used methods are also applied to a newly-collected dataset to reduce the possibility of spurious correlations. Our results showed a significant (p<0.001) negative correlation between BCI accuracy and estimated latency jitter. Furthermore, we also examined the effect of the number of electrodes on each classification technique. Our results showed that on the whole, CBLE worked regardless of the classification method and electrode count; by contrast the effect of the number of electrodes on BCI performance was classifier dependent.

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 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 Restorative Design for Heritage Requalification: Selected Roman Works

2021 ◽  
pp. 227-244
Author(s):  
Luciano Cupelloni
Keyword(s):  
Human Resources ◽  
Public Space ◽  
Experimental Research ◽  
Common Good ◽  
Urban Systems ◽  
Technological Design ◽  
Transformative Power ◽  
The Public ◽  
New Perspective

AbstractThe theme is the urban re-qualification, applied in particular to the architectural heritage and the public space. The goal is the ongoing challenge of outlining a new perspective aimed at “common good” and sustainability. The instrument chosen is the “environmental technological design,” understood as a cultural, scientific, and social position, that is, as a position on the role of architecture. The contribution reiterates the urgency of restoring the transformative power of the design mission to the project, too often reduced to a set of technical compilation procedures. In the best cases, a position that is lost in the complication of procedures, in the extension of time, in the waste of economic and human resources. A crisis of the project as “anticipation” of progressive scenarios, precisely in the most acute, ever more serious phase, of the urgency of the reorganization of urban systems, with a view to environmental, social and economic sustainability. Not a recent urgency, today only brought to light, dramatically, by the reality of the SARS-CoV-2 pandemic. Among the solutions, the design experimental research, well beyond the objective of flexibility, up to the notion of “functional indifference,” understood not as shapeless neutrality, but as the maximum functionality of spatial, architectural and urban quality.

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