Selection of proper electrodes and improving performance in brain computer interfaces

2015 ◽  
Author(s):  
Ebru Durmus ◽  
Nurhan Gursel Ozmen
Keyword(s):  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Selection Of

scholarly journals Automated selection of brain regions for real-time fMRI brain–computer interfaces

2016 ◽  
Vol 14 (1) ◽  
pp. 016004 ◽  
Author(s):  
Michael Lührs ◽  
Bettina Sorger ◽  
Rainer Goebel ◽  
Fabrizio Esposito
Keyword(s):  
Real Time ◽  
Brain Regions ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Selection Of

scholarly journals Team PhyPA: Brain-Computer Interfacing for Everyday Human-Computer Interaction

2017 ◽  
Vol 61 (2) ◽  
pp. 209 ◽  
Author(s):  
Thorsten O. Zander ◽  
Laurens R. Krol
Keyword(s):  
Human Computer Interaction ◽  
Brain Activity ◽  
Brain Computer Interfaces ◽  
Input Channel ◽  
Computer Interfaces ◽  
Brain Computer Interfacing ◽  
Work Done ◽  
Selection Of ◽  
Computer Interfacing ◽  
Computer Interaction

Brain-computer interfaces can provide an input channel from humans to computers that depends only on brain activity, bypassing traditional means of communication and interaction. This input channel can be used to send explicit commands, but also to provide implicit input to the computer. As such, the computer can obtain information about its user that not only bypasses, but also goes beyond what can be communicated using traditional means. In this form, implicit input can potentially provide significant improvements to human-computer interaction. This paper describes a selection of work done by Team PhyPA (Physiological Parameters for Adaptation) at the Technische Universität Berlin to use brain-computer interfacing to enrich human-computer interaction.

Selection of Mental Tasks for Brain-Computer Interfaces Using NASA-TLX Index

2020 ◽  
pp. 160-172
Author(s):  
Jhon Freddy Moofarry ◽  
Kevin Andrés Suaza Cano ◽  
Diego Fernando Saavedra Lozano ◽  
Javier Ferney Castillo García
Keyword(s):  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Nasa Tlx ◽  
Selection Of ◽  
Mental Tasks

scholarly journals Formulation of the Challenges in Brain-Computer Interfaces as Optimization Problems—A Review

2021 ◽  
Vol 14 ◽  
Author(s):  
Shireen Fathima ◽  
Sheela Kiran Kore
Keyword(s):  
Domain Knowledge ◽  
Optimization Problems ◽  
Optimal Number ◽  
Optimization Techniques ◽  
Brain Computer Interfaces ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Huge Data ◽  
Electroencephalogram Eeg ◽  
Selection Of

Electroencephalogram (EEG) is one of the common modalities of monitoring the mental activities. Owing to the non-invasive availability of this system, its applicability has seen remarkable developments beyond medical use-cases. One such use case is brain-computer interfaces (BCI). Such systems require the usage of high resolution-based multi-channel EEG devices so that the data collection spans multiple locations of the brain like the occipital, frontal, temporal, and so on. This results in huge data (with high sampling rates) and with multiple EEG channels with inherent artifacts. Several challenges exist in analyzing data of this nature, for instance, selecting the optimal number of EEG channels or deciding what best features to rely on for achieving better performance. The selection of these variables is complicated and requires a lot of domain knowledge and non-invasive EEG monitoring, which is not feasible always. Hence, optimization serves to be an easy to access tool in deriving such parameters. Considerable efforts in formulating these issues as an optimization problem have been laid. As a result, various multi-objective and constrained optimization functions have been developed in BCI that has achieved reliable outcomes in device control like neuro-prosthetic arms, application control, gaming, and so on. This paper makes an attempt to study the usage of optimization techniques in formulating the issues in BCI. The outcomes, challenges, and major observations of these approaches are discussed in detail.

Robust Averaging of Covariances for EEG Recordings Classification in Motor Imagery Brain-Computer Interfaces

2017 ◽  
Vol 29 (6) ◽  
pp. 1631-1666 ◽  
Author(s):  
Takashi Uehara ◽  
Matteo Sartori ◽  
Toshihisa Tanaka ◽  
Simone Fiori
Keyword(s):  
Motor Imagery ◽  
Tangent Space ◽  
Covariance Matrices ◽  
Space Mapping ◽  
Brain Computer Interfaces ◽  
Robust Estimators ◽  
Computer Interfaces ◽  
Sample Covariance ◽  
Reference Matrix ◽  
Selection Of

The estimation of covariance matrices is of prime importance to analyze the distribution of multivariate signals. In motor imagery–based brain-computer interfaces (MI-BCI), covariance matrices play a central role in the extraction of features from recorded electroencephalograms (EEGs); therefore, correctly estimating covariance is crucial for EEG classification. This letter discusses algorithms to average sample covariance matrices (SCMs) for the selection of the reference matrix in tangent space mapping (TSM)–based MI-BCI. Tangent space mapping is a powerful method of feature extraction and strongly depends on the selection of a reference covariance matrix. In general, the observed signals may include outliers; therefore, taking the geometric mean of SCMs as the reference matrix may not be the best choice. In order to deal with the effects of outliers, robust estimators have to be used. In particular, we discuss and test the use of geometric medians and trimmed averages (defined on the basis of several metrics) as robust estimators. The main idea behind trimmed averages is to eliminate data that exhibit the largest distance from the average covariance calculated on the basis of all available data. The results of the experiments show that while the geometric medians show little differences from conventional methods in terms of classification accuracy in the classification of electroencephalographic recordings, the trimmed averages show significant improvement for all subjects.

scholarly journals Guidelines for Feature Matching Assessment of Brain–Computer Interfaces for Augmentative and Alternative Communication

2018 ◽  
Vol 27 (3) ◽  
pp. 950-964 ◽  
Author(s):  
Kevin M. Pitt ◽  
Jonathan S. Brumberg
Keyword(s):  
Motor Imagery ◽  
Augmentative And Alternative Communication ◽  
Feature Matching ◽  
Full Range ◽  
Brain Computer Interfaces ◽  
Alternative Communication ◽  
Access Methods ◽  
Computer Interfaces ◽  
Sensory Motor ◽  
Selection Of

Purpose Brain–computer interfaces (BCIs) can provide access to augmentative and alternative communication (AAC) devices using neurological activity alone without voluntary movements. As with traditional AAC access methods, BCI performance may be influenced by the cognitive–sensory–motor and motor imagery profiles of those who use these devices. Therefore, we propose a person-centered, feature matching framework consistent with clinical AAC best practices to ensure selection of the most appropriate BCI technology to meet individuals' communication needs. Method The proposed feature matching procedure is based on the current state of the art in BCI technology and published reports on cognitive, sensory, motor, and motor imagery factors important for successful operation of BCI devices. Results Considerations for successful selection of BCI for accessing AAC are summarized based on interpretation from a multidisciplinary team with experience in AAC, BCI, neuromotor disorders, and cognitive assessment. The set of features that support each BCI option are discussed in a hypothetical case format to model possible transition of BCI research from the laboratory into clinical AAC applications. Conclusions This procedure is an initial step toward consideration of feature matching assessment for the full range of BCI devices. Future investigations are needed to fully examine how person-centered factors influence BCI performance across devices.

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