Brain-Computer Interface Research (eBook, PDF)

A State-of-the-Art Summary 10

• Format: PDF

Produktbeschreibung

The Annual BCI Research Awards are international prizes that recognize the top new projects in brain–computer interface (BCI) research. This book contains concise descriptions of projects nominated for the 2020 BCI Research Award and interviews with nominees. Each article is authored by the researchers who developed the project, and articles have been updated with new progress achieved since their nomination. These chapters are complemented by an introduction by the editors together with a concluding chapter that reviews the annual Awards Ceremony, announces the winners, and ends with a brief discussion.
One of the prominent trends in recent years has been the development of BCIs for restoring limb use and for aiding optical and auditory sensory perception. Many chapters in this book present emerging and novel research directions likely to become more prevalent in the near future. This year's book includes chapters based on interviews with BCI experts who were nominated for an award, including this year's first, second, and third place winners. These interview chapters are generally less technical than project descriptions, and provide individual perspectives from people actively working on new methods and systems.

Produktdetails

• Verlag: Springer International Publishing
• Erscheinungstermin: 28. August 2021
• Englisch
• ISBN-13: 9783030792879
• Artikelnr.: 62510559

Autorenporträt

Christoph Guger is running g.tec medical engineering in order to design brain-computer interfaces systems.

Brendan Allison is carrying out BCI research with P300 and motor imagery BCIs.

Ayse Gündüz is running a BCI lab at University of Florida and performs direct brain stimulation and ECoG BCI research.

Inhaltsangabe

1 Enhancing gesture decoding performance using signals from human posterior parietal cortex.- Machine translation of cortical activity to text.- 2 Towards practical MEG-BCI with optically pumped magnetometers.- 3 EEG decoding of pain perception for a real-time reflex system in prostheses.- 4 A computer-brain interface that restores lost extremities touch and movement sensations.- 5 Restoring the sense of touch using a sensorimotor demultiplexing neural interface.- 6 A brain–spine interface complements deep-brain stimulation to both alleviate gait and balance deficits and increase alertness in a primate model of Parkinson’s disease.- 7 Speaker-independent auditory attention decoding without access to clean speech sources.- 8 A high-performance handwriting BCI.- 9 A neuromorphic brain computer interface for real-time detection of a new biomarker for epilepsy surgery.- 10 “Sono-optogenetics”: An ultrasound-mediated non-invasive optogenetic brain-computer interface.- 11 High-dimensional (8D) control of complex effectors such as an exoskeleton by a tetraplegic subject using chronic ECoG recordings using stable and robust over time adaptive direct neural decoder.

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1 Enhancing gesture decoding performance using signals from human posterior parietal cortex.- Machine translation of cortical activity to text.- 2 Towards practical MEG-BCI with optically pumped magnetometers.- 3 EEG decoding of pain perception for a real-time reflex system in prostheses.- 4 A computer-brain interface that restores lost extremities touch and movement sensations.- 5 Restoring the sense of touch using a sensorimotor demultiplexing neural interface.- 6 A brain-spine interface complements deep-brain stimulation to both alleviate gait and balance deficits and increase alertness in a primate model of Parkinson's disease.- 7 Speaker-independent auditory attention decoding without access to clean speech sources.- 8 A high-performance handwriting BCI.- 9 A neuromorphic brain computer interface for real-time detection of a new biomarker for epilepsy surgery.- 10 "Sono-optogenetics": An ultrasound-mediated non-invasive optogenetic brain-computer interface.- 11 High-dimensional (8D) control of complex effectors such as an exoskeleton by a tetraplegic subject using chronic ECoG recordings using stable and robust over time adaptive direct neural decoder.