Advances in Brain-Computer Interface

A brain–computer interface (BCI), sometimes called a brain–machine interface (BMI), is a direct communication link between the brain's electrical activity and an external device, most commonly a computer or robotic limb. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. They are often conceptualized as a human–machine interface that skips the intermediary of moving body parts (e.g. hands or feet). BCI implementations range from non-invasive (EEG, MEG, MRI) and partially invasive (ECoG and endovascular) to invasive (microelectrode array), based on how physically close electrodes are to brain tissue.

In the present book, eleven typical literatures about brain–computer interface published on international authoritative journals were selected to introduce the worldwide newest progress, which contains reviews or original researches on brain–computer interface. We hope this book can demonstrate advances in brain–computer interface as well as give references to the researchers, students and other related people.

Sample Chapter(s)
Preface (287 KB)
Components of the Book:
  • Chapter 1
    Neuro Nonsense: Why Ulysses Contracts don’t Compute in Brain Computer Interface Research
  • Chapter 2
    Towards Noninvasive Hybrid Brain–Computer Interfaces: Framework, Practice, Clinical Application, and Beyond
  • Chapter 3
    Eliminating or Shortening the Calibration for a P300 Brain–Computer Interface Based on a Convolutional Neural Network and Big Electroencephalography Data: An Online Study
  • Chapter 4
    A continuous pursuit dataset for online deep learning-based EEG brain-computer interface
  • Chapter 5
    Recent applications of EEG based brain computer interface in the medical field
  • Chapter 6
    Automatic Feature Selection for Sensorimotor Rhythms Brain-Computer Interface Fusing Expert and Data-Driven Knowledge
  • Chapter 7
    A Hybrid Asynchronous Brain-Computer Interface Combining SSVEP and EOG Signals
  • Chapter 8
    A high-performance brain–computer interface for finger decoding and quadcopter game control in an individual with paralysis
  • Chapter 9
    A Multi-Level Integrated EEG-Channel Selection Method Based on the Lateralization Index
  • Chapter 10
    Improving Motor Imagery of Gait on a Brain–Computer Interface by Means of Virtual Reality: A Case of Study
  • Chapter 11
    Brain–computer interface: trend, challenges, and threats
Readership: Students, academics, teachers and other people attending or interested in brain–computer interface (BCI).
Daniel J. Hurst
Department of Medical Education and Scholarship, Rowan-Virtua School of Osteopathic Medicine, 40 E. Laurel Road, UEC 2135, Stratford, NJ, 08084, USA

Christopher A. Bobier
College of Medicine, Central Michigan University, Mount Pleasant, MI, 48859, USA

Mushfika Sultana
Brain-Computer Interface and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, U.K.

and more...
Copyright © 2006-2025 Scientific Research Publishing Inc. All Rights Reserved.
Top