Faculty Opinions recommendation of A Link between the Increase in Electroencephalographic Coherence and Performance Improvement in Operating a Brain-Computer Interface.

2015 ◽  
Author(s):  
Mario Manto ◽  
Florian Bodranghien
Keyword(s):  
Performance Improvement ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
And Performance

scholarly journals A Link between the Increase in Electroencephalographic Coherence and Performance Improvement in Operating a Brain-Computer Interface

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Irma Nayeli Angulo-Sherman ◽  
David Gutiérrez
Keyword(s):  
Performance Improvement ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Training Session ◽  
Brain Regions ◽  
Computer Interface ◽  
Imagery Task ◽  
And Performance ◽  
The Relationship ◽  
Event Related Coherence

We study the relationship between electroencephalographic (EEG) coherence and accuracy in operating a brain-computer interface (BCI). In our case, the BCI is controlled through motor imagery. Hence, a number of volunteers were trained using different training paradigms: classical visual feedback, auditory stimulation, and functional electrical stimulation (FES). After each training session, the volunteers’ accuracy in operating the BCI was assessed, and the event-related coherence (ErCoh) was calculated for all possible combinations of pairs of EEG sensors. After at least four training sessions, we searched for significant differences in accuracy and ErCoh using one-way analysis of variance (ANOVA) and multiple comparison tests. Our results show that there exists a high correlation between an increase in ErCoh and performance improvement, and this effect is mainly localized in the centrofrontal and centroparietal brain regions for the case of our motor imagery task. This result has a direct implication with the development of new techniques to evaluate BCI performance and the process of selecting a feedback modality that better enhances the volunteer’s capacity to operate a BCI system.

scholarly journals Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis

Neurology ◽  
2018 ◽  
Vol 91 (3) ◽  
pp. e258-e267 ◽  
Author(s):  
Jonathan R. Wolpaw ◽  
Richard S. Bedlack ◽  
Domenic J. Reda ◽  
Robert J. Ringer ◽  
Patricia G. Banks ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Amyotrophic Lateral Sclerosis ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
It Use ◽  
Technical Problems ◽  
Rapid Disease Progression ◽  
And Performance ◽  
Lateral Sclerosis

ObjectiveTo assess the reliability and usefulness of an EEG-based brain-computer interface (BCI) for patients with advanced amyotrophic lateral sclerosis (ALS) who used it independently at home for up to 18 months.MethodsOf 42 patients consented, 39 (93%) met the study criteria, and 37 (88%) were assessed for use of the Wadsworth BCI. Nine (21%) could not use the BCI. Of the other 28, 27 (men, age 28–79 years) (64%) had the BCI placed in their homes, and they and their caregivers were trained to use it. Use data were collected by Internet. Periodic visits evaluated BCI benefit and burden and quality of life.ResultsOver subsequent months, 12 (29% of the original 42) left the study because of death or rapid disease progression and 6 (14%) left because of decreased interest. Fourteen (33%) completed training and used the BCI independently, mainly for communication. Technical problems were rare. Patient and caregiver ratings indicated that BCI benefit exceeded burden. Quality of life remained stable. Of those not lost to the disease, half completed the study; all but 1 patient kept the BCI for further use.ConclusionThe Wadsworth BCI home system can function reliably and usefully when operated by patients in their homes. BCIs that support communication are at present most suitable for people who are severely disabled but are otherwise in stable health. Improvements in BCI convenience and performance, including some now underway, should increase the number of people who find them useful and the extent to which they are used.

Competing at the Cybathlon Championship for Athletes With Disabilities: Long-Term Motor Imagery Brain-Computer Interface Training of a Tetraplegic Cybathlete

2021 ◽  
Author(s):  
Attila Korik ◽  
Karl McCreadie ◽  
Niall McShane ◽  
Naomi Du Bois ◽  
Massoud Khodadadzadeh ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Cognitive State ◽  
Contributing Factors ◽  
Performance Pressure ◽  
Cord Injury ◽  
Athletes With Disabilities ◽  
Run Up ◽  
And Performance

Abstract Background: The brain-computer interface (BCI) race at the Cybathlon championship for athletes with disabilities challenges teams (BCI researchers, developers and pilots with spinal cord injury) to control an avatar on a virtual racetrack without movement. Here we describe the training regime and results of the Ulster University BCI Team pilot who is tetraplegic and has trained to use an electroencephalography (EEG)-based BCI intermittently over 10 years, to compete in three Cybathlon events. Methods: A multi-class, multiple binary classifier framework was used to decode three kinesthetically imagined movements (motor imagery) (left (L) and right (R) arm and feet (F)) as well as relax state (X). Three games paradigms were used for training i.e., NeuroSensi, Triad, and Cybathlon: BrainDriver. An evaluation of the pilot’s performance is presented for two Cybathlon competition training periods – spanning 20 sessions over 5 weeks prior to the 2019 competition, and 25 sessions over 5 weeks in the run up to the 2020 competition.Results: Having participated in BCI training in 2009 and competed in Cybathlon 2016, the experienced pilot achieved high two-class accuracy on all class pairs when training began in 2019 (decoding accuracy >90%, resulting in efficient NeuroSensi and Triad game control). The BrainDriver performance (i.e., Cybathlon race completion time) improved significantly during the training period, leading up to the competition day, ranging from 274s - 156s (255±24s to 191±14s mean±std), over 17 days (10 sessions) in 2019, and from 230s - 168s (214±14s to 181±4s), over 18 days (13 sessions) in 2020. However, on both competition occasions, towards the race date, the performance deteriorated significantly.Conclusions: The training regime and framework applied were highly effective in achieving competitive race completion times. The BCI framework did not cope with significant deviation in electroencephalography (EEG) observed in the sessions occurring shortly before and during the race day. Stress, arousal level and fatigue, associated with the competition challenge and performance pressure resulting in cognitive state changes, were likely contributing factors to the nonstationary effects that resulted in the BCI and pilot achieving suboptimal performance on race day. Trial registration: not registered

scholarly journals Brain Computer Interface Controlled Voice Assistant

2020 ◽  
Vol 8 (6) ◽  
pp. 2370-2377
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Project Work ◽  
Daily Lives ◽  
Computer Interfaces ◽  
The Disabled ◽  
And Performance ◽  
The Brain ◽  
Bci System

A brain-controlled robot using brain computer interfaces (BCIs) was explored in this project. BCIs are systems that are able to circumvent traditional communication channels (i.e. muscles and thoughts), to ensure the human brain and physical devices communicate directly and are in charge by converting various patterns of brain activity to instructions in real time. An automation can be managed with these commands. The project work seeks to build and monitor a program that can help the disabled people accomplish certain activities independently of others in their daily lives. Develop open-source EEG and brain-computer interface analysis software. The quality and performance of BCI of different EEG signals are compared. Variable signals obtained through MATLAB Processing from the Brainwave sensor. Automation modules operate by means of the BCI system. The Brain Computer Interface aims to build a fast and reliable link between a person's brain and a personal computer. The controls also use the Brain-Computer Interface for home appliances. The system will integrate with any smartphones voice assistant.

scholarly journals The Impact of Distractions on Intracortical Brain-Computer Interface Control of a Robotic Arm

2021 ◽  
Author(s):  
Michael D. Guthrie ◽  
Angelica J. Herrera ◽  
John E. Downey ◽  
Lucas J. Brane ◽  
Michael L. Boninger ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Brain Computer Interface ◽  
Microelectrode Arrays ◽  
Computer Interface ◽  
Robotic Arm ◽  
Observational Trial ◽  
Interface Control ◽  
Path Efficiency ◽  
And Performance ◽  
The Impact

AbstractThis was an investigational device observational trial with the objective to evaluate the impact of distractions on intracortical brain-computer interface (BCI) performance. Two individuals with tetraplegia had microelectrode arrays implanted into their motor cortex for trials of intracortical BCI safety and performance. The primary task was moving a robotic arm between two targets as quickly as possible, performed alone and with various secondary distraction conditions. Primary outcomes included targets acquired, path efficiency, and subjective difficulty. There was no difference in the number of targets acquired for either subject with or without distractions. Median path efficiency was similar across all conditions (range: 0.766-0.846) except the motor distraction for Subject P2, where the median path efficiency dropped to 0.675 (p = 0.033, Mann-Whitney U test). Both subjects rated the overall difficulty of the task with and without distractions as low. Overall, intracortical BCI performance was robust to various distractions.

scholarly journals Fully Open-Access Passive Dry Electrodes BIOADC: Open-Electroencephalography (EEG) Re-Invented

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 772 ◽  
Author(s):  
Gaetano Gargiulo ◽  
Paolo Bifulco ◽  
Mario Cesarelli ◽  
Alistair McEwan ◽  
Armin Nikpour ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Alpha Wave ◽  
Bill Of Materials ◽  
Dry Electrodes ◽  
Dry Electrode ◽  
And Performance ◽  
Volunteer Participant ◽  
Analogue To Digital ◽  
The Brain

The Open-electroencephalography (EEG) framework is a popular platform to enable EEG measurements and general purposes Brain Computer Interface experimentations. However, the current platform is limited by the number of available channels and electrode compatibility. In this paper we present a fully configurable platform with up to 32 EEG channels and compatibility with virtually any kind of passive electrodes including textile, rubber and contactless electrodes. Together with the full hardware details, results and performance on a single volunteer participant (limited to alpha wave elicitation), we present the brain computer interface (BCI)2000 EEG source driver together with source code as well as the compiled (.exe). In addition, all the necessary device firmware, gerbers and bill of materials for the full reproducibility of the presented hardware is included. Furthermore, the end user can vary the dry-electrode reference circuitry, circuit bandwidth as well as sample rate to adapt the device to other generalized biopotential measurements. Although, not implemented in the tested prototype, the Biomedical Analogue to Digital Converter BIOADC naturally supports SPI communication for an additional 32 channels including the gain controller. In the appendix we describe the necessary modification to the presented hardware to enable this function.

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