Magnetic particle composites as a non-invasive high-resolution brain-machine interface (Conference Presentation)

2019 ◽  
Author(s):  
Irving N. Weinberg ◽  
Lamar O. Mair ◽  
Sahar Jafari ◽  
Chad Ropp ◽  
Olivia Hale ◽  
...  
Keyword(s):  
High Resolution ◽  
Magnetic Particle ◽  
Brain Machine Interface ◽  
Non Invasive ◽  
Machine Interface

scholarly journals Rate-Based Decoding of Images from Multi-Region, High Resolution, Electrode Recordings In the Mouse Visual System

2021 ◽  
Author(s):  
Christopher B Fritz
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Neural Activity ◽  
Deep Neural Network ◽  
Superior Performance ◽  
Brain Machine Interface ◽  
Feed Forward ◽  
Deep Networks ◽  
Feed Forward Neural Networks ◽  
Machine Interface

We hypothesize that deep networks are superior to linear decoders at recovering visual stimuli from neural activity. Using high-resolution, multielectrode Neuropixels recordings, we verify this is the case for a simple feed-forward deep neural network having just 7 layers. These results suggest that these feed-forward neural networks and perhaps more complex deep architectures will give superior performance in a visual brain-machine interface.

scholarly journals Perceptions of Brain-Machine Interface Technology among Mothers of Disabled Children

2015 ◽  
Vol 35 (4) ◽  
Author(s):  
Lucy Diep ◽  
Gregor Wolbring
Keyword(s):  
Potential Benefit ◽  
Communication Technologies ◽  
Brain Machine Interface ◽  
Disabled Children ◽  
Disabled People ◽  
Non Invasive ◽  
Machine Interface ◽  
The Impact ◽  
Near Future ◽  
The Way

<p>Communication technologies are constantly transforming the way we communicate and interact with each other, and with our environment, with its impact affecting everyone including disabled people and the groups linked to them. The brain-machine interface (BMI) is one example of an emerging communication technology envisioned to transform the way we communicate and interact with each other and our environment in the near future. One group targeted to use BMI technology and impacted by others using BMI are disabled people. For disabled people and their families, the impact and implications of adopting BMI technologies is important to understand so they can make informed decisions and advocate for policies governing the technology's application to decrease negative and increase positive outcomes. In this study, we interviewed nine mothers of disabled children, with no prior knowledge of BMI technology, to explore their perceptions and attitude toward the technology. Five main themes emerged from our findings: the potential benefit to aid mothers to interpret their children's needs; the potential benefit to expand a child's social network; the preference for non-invasive BMI approach; impact of BMI use by non-disabled people and cost and qualification barriers.</p>

scholarly journals Image-guided Placement of Magnetic Neuroparticles as a Potential High-Resolution Brain-Machine Interface

2018 ◽  
Author(s):  
Irving N. Weinberg ◽  
Lamar O. Mair ◽  
Sahar Jafari ◽  
Jose Algarin ◽  
Jose Maria Benlloch Baviera ◽  
...  
Keyword(s):  
High Resolution ◽  
Brain Machine Interface ◽  
Image Guided ◽  
Machine Interface

scholarly journals Non-Invasive Brain-Computer Interface Technology (BCI) Modalities and Implementation: A Review Article and Assimilation of BCI Models

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Dalia Mirghani Mahmoud Saadabi
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Brain Machine Interface ◽  
Output Stage ◽  
Important Place ◽  
Non Invasive ◽  
System Input ◽  
Pros And Cons ◽  
Machine Interface ◽  
Bci System

Brain-computer interface (BCI) technology or brain-machine interface (BMI) technology has become the most attractive field for researchers in various disciplines and has occupied an important place in many scientific and even recreational applications. This review first highlights the different and most frequently used methods for implementing brain-computer interface (BCI) systems with a focus on non-invasive BCI models. Secondly, it analyzes the different stages of building a BCI system (input stage, signal processing stage, and output stage). Then it compares the different methods in terms of the algorithms used and the pros and cons. The aim of the study is to find the most adequate and price method to record the EEG by means of electrodes placed on the scalp. Then some features will be extracted from the EEG and sent to a classifier, whose response is translated features into some action whose execution provides feedback to the user.

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